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    <Title>Digital Earth Africa - OGC Web Services</Title>
    
    <Abstract>
        Digital Earth Africa OGC Web Services
    </Abstract>
    
    
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        <Keyword>WOfS</Keyword>
        
        <Keyword>africa</Keyword>
        
        <Keyword>fractional-cover</Keyword>
        
        <Keyword>time-series</Keyword>
        
        <Keyword>landsat</Keyword>
        
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            <Address>GPO Box 378</Address>
            <City>Canberra</City>
            <StateOrProvince>ACT</StateOrProvince>
            <PostCode>2609</PostCode>
            <Country>Australia</Country>
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    <Layer>
        <Title>Digital Earth Africa - OGC Web Services</Title>

        <Abstract>
            Digital Earth Africa OGC Web Services
        </Abstract>

        
            
                <CRS>EPSG:3857</CRS>
            
                <CRS>EPSG:4326</CRS>
            
                <CRS>EPSG:6933</CRS>
            

            
        
        
            
        
    <Layer>
        <Title>DE Africa Continental Services</Title>

        <Abstract>
            DE Africa Continental Services
        </Abstract>

        
        
            
        
    <Layer>
        <Title>Agriculture</Title>

        <Abstract>
            Agriculture
        </Abstract>

        
        
            
        
    <Layer>
        <Title>Cropland extent map (provisional)</Title>

        <Abstract>
            The Digital Earth Africa cropland extent service identifies areas of cropping in Africa, using Copernicus Sentinel-2 satellite imagery and machine learning techniques. The map indicates either the presence or absence of crop. An accurate, high-resolution, and regularly-updated cropland area map for the African continent is recognised as a useful tool in crop monitoring services. A precise and accurate cropland extent map indicating where cropland occurs serves as a basis for higher-level products, such as crop type and watering intensity.


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    <Layer queryable="1">
        <Name>crop_mask</Name>
        <Title>Cropland extent 2019</Title>
        <Abstract>
Digital Earth Africa&#39;s cropland extent map (2019) shows the estimated location of croplands in Africa for the period January to December 2019. Cropland is defined as: &#34;a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvest-able at least once within the 12 months after the sowing/planting date.&#34; This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation.

This provisional cropland extent map has a resolution of 10m, and was built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent map was produced using extensive training data from regions across Africa, coupled with a Random Forest machine learning model. The continental service contains maps built seperately for eight Agro-Ecological Zones (AEZs). For a detailed exploration of the methods used to produce the cropland extent map, read the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository.

Independent validations suggest this service has an overall accuracy ranging from 84 to 94 % across the AEZs. The algorithm tends to report more omission errors (labelling actual crops as non-crops) than commission errors (labelling non-crops as crops). Where commission errors occur they tend to be focussed around wetlands and seasonal grasslands (e.g. in the Serengeti) which spectrally resemble some kinds of cropping.

The crop mask contains three measurements:
- mask (&#34;Cropped land&#34;): This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification.
- prob (&#34;Probability of cropping&#34;): This band displays the prediction probabilities for the &#39;crop&#39; class during 2019. As this service used a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted &#39;crop&#39;, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at 50 % will produce a map identical to mask.
- filtered (&#34;Cropped land (object-filtered)&#34;): This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has filtered using an image segmentation algorithm. During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as small commission errors are removed by this process, and the &#39;salt and pepper&#39; effect typical of classifying pixels is diminished.

More technical information about the cropland extent service can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.html)

Cropland extent maps are a foundational, baseline layer in high-order crop health and crop productivity products which necessarily rely on knowing where cropping occurs before further analysis can take place.

This service is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download click on a tile in the explorer page (https://explorer.digitalearth.africa/products/crop_mask_eastern/extents
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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                <Name>green</Name>
                <Title>Cropped land</Title>
                <Abstract>Classified as crop</Abstract>
                
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                <Title>Probability of cropping</Title>
                <Abstract>Probability of cropping</Abstract>
                
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        <Title>Geomedians</Title>

        <Abstract>
            Geomedians
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>gm_s2_annual</Name>
        <Title>Annual GeoMAD (Sentinel-2)</Title>
        <Abstract>
Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create &#39;summary&#39; images or &#39;composites&#39; that combine multiple images into one image to reveal the median or &#39;typical&#39; appearance of the landscape for a certain time period.

One approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the &#39;geometric median&#39; (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year.

In addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:
- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.
- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.
- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.

More techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)

This product has a spatial resolution of 10 m and is available annually for 2017 to 2021.

It is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data 2017-2021.

Annual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year.

For more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312

</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Geomedian - Red, Green, Blue</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>Geomedian - SWIR, NIR, Green</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_sens</Name>
                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
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            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
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                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
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            <Style>
                <Name>ndci</Name>
                <Title>NDCI - Red Edge, Red</Title>
                <Abstract>Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll</Abstract>
                
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                        <Format>image/png</Format>
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                <Name>blue</Name>
                <Title>Blue - 490</Title>
                <Abstract>Blue band, centered on 490nm</Abstract>
                
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                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
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                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
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                <Name>red_edge_1</Name>
                <Title>Vegetation Red Edge - 710</Title>
                <Abstract>Near infra-red band, centred on 710nm</Abstract>
                
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                <Name>red_edge_2</Name>
                <Title>Vegetation Red Edge - 740</Title>
                <Abstract>Near infra-red band, centred on 740nm</Abstract>
                
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                <Name>red_edge_3</Name>
                <Title>Vegetation Red Edge - 780</Title>
                <Abstract>Near infra-red band, centred on 780nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
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                <Name>narrow_nir</Name>
                <Title>Narrow Near Infrared - 870</Title>
                <Abstract>Near infra-red band, centred on 865nm</Abstract>
                
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            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, centered on 1610nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared (SWIR) - 2200</Title>
                <Abstract>Short wave infra-red band 2, centered on 2200nm</Abstract>
                
            </Style>
        
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                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                <Name>log_edev</Name>
                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in geomedian/MAD calculations</Abstract>
                
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    <Layer queryable="1">
        <Name>gm_ls8_annual</Name>
        <Title>Annual GeoMAD (Landsat 8)</Title>
        <Abstract>
Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create &#39;summary&#39; images or &#39;composites&#39; that combine multiple images into one image to reveal the median or &#39;typical&#39; appearance of the landscape for a certain time period.

One approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the &#39;geometric median&#39; (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year.

In addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:
- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.
- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.
- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.

More techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)

This product has a spatial resolution of 30 m and is available annually from 2013.

It is derived from USGS Collection 2, Level 2 Landsat 8 surface reflectance data.

Annual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year.

For more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312

</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <Style>
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                <Title>Geomedian - Red, Green, Blue</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
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                <Title>Geomedian - SWIR, NIR, Green</Title>
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            </Style>
        
            <Style>
                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
            </Style>
        
            <Style>
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                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
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                        <Format>image/png</Format>
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            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, centered on 1610nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared (SWIR) - 2200</Title>
                <Abstract>Short wave infra-red band 2, centered on 2200nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
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                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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            <Style>
                <Name>count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in Geomedian/MAD calculations</Abstract>
                
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        <Title>Annual GeoMAD (Landsat 8 &amp; Landsat 9)</Title>
        <Abstract>
Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create &#39;summary&#39; images or &#39;composites&#39; that combine multiple images into one image to reveal the median or &#39;typical&#39; appearance of the landscape for a certain time period.

One approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the &#39;geometric median&#39; (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year.

In addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:
- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.
- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.
- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.

More techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)

This product has a spatial resolution of 30 m and is available annually starting from 2021.

It is derived from USGS Collection 2, Level 2 Landsat 8 &amp; 9 surface reflectance data.

Annual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year.

For more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312

</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Geomedian - Red, Green, Blue</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>Geomedian - SWIR, NIR, Green</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
            </Style>
        
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                <Name>tmad_rgb_sens</Name>
                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
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                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, centered on 1610nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared (SWIR) - 2200</Title>
                <Abstract>Short wave infra-red band 2, centered on 2200nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/gm_ls8_ls9_annual/arcsec_sdev/legend.png"/>
                    </LegendURL>
                
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            <Style>
                <Name>log_edev</Name>
                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/gm_ls8_ls9_annual/log_edev/legend.png"/>
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            </Style>
        
            <Style>
                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in Geomedian/MAD calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/gm_ls8_ls9_annual/count/legend.png"/>
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    <Layer queryable="1">
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        <Title>Annual GeoMAD (Landsat 5 &amp; Landsat 7)</Title>
        <Abstract>
Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create &#39;summary&#39; images or &#39;composites&#39; that combine multiple images into one image to reveal the median or &#39;typical&#39; appearance of the landscape for a certain time period.

One approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the &#39;geometric median&#39; (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year.

In addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:
- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.
- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.
- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.

More techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)

This product has a spatial resolution of 30 m and is available annually from 1984 to 2012 in selected areas.

It is derived from USGS Collection 2, Level 2 Landsat 5 &amp; 7 surface reflectance data.

Annual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year.

For more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312

</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Geomedian - Red, Green, Blue</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>Geomedian - SWIR, NIR, Green</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_sens</Name>
                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                                        xlink:href="https://ows.digitalearth.africa/legend/gm_ls5_ls7_annual/ndwi/legend.png"/>
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            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/gm_ls5_ls7_annual/mndwi/legend.png"/>
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            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, centered on 1610nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared (SWIR) - 2200</Title>
                <Abstract>Short wave infra-red band 2, centered on 2200nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/gm_ls5_ls7_annual/arcsec_sdev/legend.png"/>
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            </Style>
        
            <Style>
                <Name>log_edev</Name>
                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
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            <Style>
                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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            </Style>
        
            <Style>
                <Name>count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in Geomedian/MAD calculations</Abstract>
                
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    <Layer queryable="1">
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        <Title>Semiannual GeoMAD (Sentinel-2)</Title>
        <Abstract>
Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create &#39;summary&#39; images or &#39;composites&#39; that combine multiple images into one image to reveal the median or &#39;typical&#39; appearance of the landscape for a certain time period.

One approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the &#39;geometric median&#39; (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An semiannual geomedian image is calculated from the surface reflectance values drawn from a 6 month period.

In addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:
- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.
- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.
- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.

More techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)

This product has a spatial resolution of 10 m and is available semiannually for 2017 to 2021 in 6 month intervals: January–June and July–December.
It is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data 2017-2021.

Semiannual geomedian images and the MADs are useful for characterising landscapes with seasonal changes.

For more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312

</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Geomedian - Red, Green, Blue</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>Geomedian - SWIR, NIR, Green</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_sens</Name>
                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                                        xlink:href="https://ows.digitalearth.africa/legend/gm_s2_semiannual/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>ndci</Name>
                <Title>NDCI - Red Edge, Red</Title>
                <Abstract>Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                                        xlink:href="https://ows.digitalearth.africa/legend/gm_s2_semiannual/ndci/legend.png"/>
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            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 490</Title>
                <Abstract>Blue band, centered on 490nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_1</Name>
                <Title>Vegetation Red Edge - 710</Title>
                <Abstract>Near infra-red band, centred on 710nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_2</Name>
                <Title>Vegetation Red Edge - 740</Title>
                <Abstract>Near infra-red band, centred on 740nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_3</Name>
                <Title>Vegetation Red Edge - 780</Title>
                <Abstract>Near infra-red band, centred on 780nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>narrow_nir</Name>
                <Title>Narrow Near Infrared - 870</Title>
                <Abstract>Near infra-red band, centred on 865nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, centered on 1610nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared (SWIR) - 2200</Title>
                <Abstract>Short wave infra-red band 2, centered on 2200nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                                        xlink:href="https://ows.digitalearth.africa/legend/gm_s2_semiannual/arcsec_sdev/legend.png"/>
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            <Style>
                <Name>log_edev</Name>
                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                                        xlink:href="https://ows.digitalearth.africa/legend/gm_s2_semiannual/log_edev/legend.png"/>
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            </Style>
        
            <Style>
                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                                        xlink:href="https://ows.digitalearth.africa/legend/gm_s2_semiannual/log_bcdev/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in geomedian/MAD calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>gm_s2_rolling</Name>
        <Title>Rolling Monthly GeoMAD (Sentinel-2)</Title>
        <Abstract>
Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create &#39;summary&#39; images or &#39;composites&#39; that combine multiple images into one image to reveal the median or &#39;typical&#39; appearance of the landscape for a certain time period.

One approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the &#39;geometric median&#39; (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An semiannual geomedian image is calculated from the surface reflectance values drawn from a 6 month period.

In addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:
- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.
- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.
- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.

More techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)

This product has a spatial resolution of 10 m and is available monthly, each covering a rolling three months period: January to March, February to April, March to May, and so on.
It is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data.

Rolling GeoMAD is useful for characterising landscapes with seasonal changes.

For more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312

</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Geomedian - Red, Green, Blue</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>Geomedian - SWIR, NIR, Green</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_sens</Name>
                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>ndci</Name>
                <Title>NDCI - Red Edge, Red</Title>
                <Abstract>Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>blue</Name>
                <Title>Blue - 490</Title>
                <Abstract>Blue band, centered on 490nm</Abstract>
                
            </Style>
        
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                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
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                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
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                <Name>red_edge_1</Name>
                <Title>Vegetation Red Edge - 710</Title>
                <Abstract>Near infra-red band, centred on 710nm</Abstract>
                
            </Style>
        
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                <Name>red_edge_2</Name>
                <Title>Vegetation Red Edge - 740</Title>
                <Abstract>Near infra-red band, centred on 740nm</Abstract>
                
            </Style>
        
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                <Name>red_edge_3</Name>
                <Title>Vegetation Red Edge - 780</Title>
                <Abstract>Near infra-red band, centred on 780nm</Abstract>
                
            </Style>
        
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                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
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                <Name>narrow_nir</Name>
                <Title>Narrow Near Infrared - 870</Title>
                <Abstract>Near infra-red band, centred on 865nm</Abstract>
                
            </Style>
        
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                <Name>swir_1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, centered on 1610nm</Abstract>
                
            </Style>
        
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                <Name>swir_2</Name>
                <Title>Shortwave Infrared (SWIR) - 2200</Title>
                <Abstract>Short wave infra-red band 2, centered on 2200nm</Abstract>
                
            </Style>
        
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                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in Geomedian/MAD calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/gm_s2_rolling/count/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>Surface Water - Water Observations</Title>

        <Abstract>
            Surface Water - Water Observations
        </Abstract>

        
        
            
        
    <Layer>
        <Title>Water Observations from Space (WOfS)</Title>

        <Abstract>
            Water Observations from Space (WOfS)
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>wofs_ls</Name>
        <Title>Water Observations from Space feature layer</Title>
        <Abstract>
Water Observations from Space (WOfS) provides surface water observations derived from satellite imagery for all of Africa. The WOfS product allows users to get a better understanding of where water is normally present in a landscape, where water is seldom observed, and where inundation has occurred occasionally. Data is provided as Water Observation Feature Layers (WOFLs), in a 1 to 1 relationship with the input satellite data. Hence there is one WOFL for each satellite dataset processed for the occurrence of water.

This product has a spatial resolution of 30 m and a temporal coverage of 1980s to current.

It is derived from Landsat Collection 2 surface reflectance product.

Daily water observations can be used to map historical flood and to understand surface water dynamics.

WOfS shows surface water on the day and time that satellite passed overhead, which might be before, during or after a flood peak. Given the time between satellite passes (approximately once every 16 days) it is unlikely that the satellite will capture the maximum extent of any given flood. Instead, it aims to provide large scale, regional information on surface water.

For more information on the algorithm, see https://doi.org/10.1016/j.rse.2015.11.003

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/) and for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki). For more information, see the Digital Earth Africa User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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       

        
        

        
        
        
        
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                <Title>Observations</Title>
                <Abstract>Observations</Abstract>
                
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            </Style>
        
            <Style>
                <Name>wet</Name>
                <Title>Wet Only</Title>
                <Abstract>Wet Only</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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    <Layer queryable="1">
        <Name>wofs_ls_summary_annual</Name>
        <Title>Water Observations from Space annual summary</Title>
        <Abstract>
Annual water summary is one of the statistical summaries of the Water Observation from Space (WOfS) product that shows what percentage of clear observations were detected as wet (ie. the ration of wet to clear as a percentage) from each calendar year.

This product has a spatial resolution of 30 m and a temporal coverage of 1980s to last calender year.

It is derived from Landsat Collection 2 surface reflectance product.

The annual summaries can be used to understand year to year changes in surface water extent.

WOfS shows surface water on the day and time that satellite passed overhead, which might be before, during or after a flood peak. Given the time between satellite passes (approximately once every 16 days) it is unlikely that the satellite will capture the maximum extent of any given flood. Instead, it aims to provide large scale, regional information on surface water.

For more information on the algorithm, see https://doi.org/10.1016/j.rse.2015.11.003

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/) and for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki). For more information, see the Digital Earth Africa User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html).
</Abstract>
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            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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        <Dimension name="time" units="ISO8601" default="2025-01-01">1984-01-01,1985-01-01,1986-01-01,1987-01-01,1988-01-01,1989-01-01,1990-01-01,1991-01-01,1992-01-01,1993-01-01,1994-01-01,1995-01-01,1996-01-01,1997-01-01,1998-01-01,1999-01-01,2000-01-01,2001-01-01,2002-01-01,2003-01-01,2004-01-01,2005-01-01,2006-01-01,2007-01-01,2008-01-01,2009-01-01,2010-01-01,2011-01-01,2012-01-01,2013-01-01,2014-01-01,2015-01-01,2016-01-01,2017-01-01,2018-01-01,2019-01-01,2020-01-01,2021-01-01,2022-01-01,2023-01-01,2024-01-01,2025-01-01</Dimension>
        

        
        

        
        
        
        
            <Style>
                <Name>wofs_summary_annual_frequency</Name>
                <Title> Water frequency</Title>
                <Abstract>WOfS summary showing the frequency of water</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>legacy_wofs_summary_annual_frequency</Name>
                <Title> Water frequency (legacy colourmap)</Title>
                <Abstract>WOfS summary showing the frequency of water</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>wofs_summary_annual_wet</Name>
                <Title>Count of wet observations</Title>
                <Abstract>WOfS summary showing the count of water observations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>wofs_summary_annual_clear</Name>
                <Title>Count of clear observations</Title>
                <Abstract>WOfS annual summary showing the count of clear observations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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    <Layer queryable="1">
        <Name>wofs_ls_summary_alltime</Name>
        <Title>Water Observations from Space all-time summary</Title>
        <Abstract>
All time water summary is one of the statistical summaries of the Water Observation from Space (WOfS) product that shows what percentage of clear observations were detected as wet (ie. the ration of wet to clear as a percentage) over time.

This product has a spatial resolution of 30 m and a temporal coverage of 1980s to last calender year.

It is derived from Landsat Collection 2 surface reflectance product.

All time water summary can be used to understand water availability and flooding risk in a historical context.

WOfS shows surface water on the day and time that satellite passed overhead, which might be before, during or after a flood peak. Given the time between satellite passes (approximately once every 16 days) it is unlikely that the satellite will capture the maximum extent of any given flood. Instead, it aims to provide large scale, regional information on surface water.

For more information on the algorithm, see https://doi.org/10.1016/j.rse.2015.11.003

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/) and for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki). For more information, see the Digital Earth Africa User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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            <Style>
                <Name>wofs_summary_alltime_frequency</Name>
                <Title> Water frequency</Title>
                <Abstract>WOfS summary showing the frequency of Wetness</Abstract>
                
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            <Style>
                <Name>legacy_wofs_summary_alltime_frequency</Name>
                <Title> Water frequency (legacy colourmap)</Title>
                <Abstract>WOfS summary showing the frequency of Wetness</Abstract>
                
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                        <Format>image/png</Format>
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            </Style>
        
            <Style>
                <Name>wofs_summary_alltime_wet</Name>
                <Title>Count of wet observations</Title>
                <Abstract>WOfS summary showing the count of water observations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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                    </LegendURL>
                
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            <Style>
                <Name>wofs_summary_alltime_clear</Name>
                <Title>Count of clear observations</Title>
                <Abstract>WOfS summary showing the count of clear observations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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    </Layer>
    
    
        
    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>Surface Water - Water Quality</Title>

        <Abstract>
            Surface Water - Water Quality
        </Abstract>

        
        
            
        
    <Layer>
        <Title>Water Quality Monitoring Service (WQMS)</Title>

        <Abstract>
            Water Quality Monitoring Service (WQMS)
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>wq_annual</Name>
        <Title>Annual Water Quality Variables</Title>
        <Abstract>The Digital Earth Africa water quality service
provides annual water quality data for surface water bodies derived from a
variety of optical sensors, at a spatial resolution of 10 meters. The data is
available from the year 2000 to 2024.

More technical information about the Water Quality Service can be found in the
User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/&lt;insert page&gt;.html)
This service is accessible through OGC Web Service (https://ows.digitalearth.africa/),
for analysis in DE Africa Sandbox (sandbox.digitalearth.africa) and for direct download
click on a tile in the explorer page (https://explorer.digitalearth.africa/products/wq_annual).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>wq_annual_tsm</Name>
                <Title>TSM (Index)</Title>
                <Abstract>Total suspended matter concentration (mg/L)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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            </Style>
        
            <Style>
                <Name>wq_annual_tsi</Name>
                <Title>Trophic State Index</Title>
                <Abstract>Biological productivity in surface water bodies</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>wq_annual_ndvi</Name>
                <Title>Normalised Difference Vegeation Index (NDVI)</Title>
                <Abstract>Presence of vegetation in surface water bodies</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>wq_annual_fai</Name>
                <Title>Floating Algal Index (FAI)</Title>
                <Abstract>Presence and density of floating algal blooms in surface water bodies, derived from the Floating Algal Index. Negative values indicate open water; positive values indicate increasing algal or aquatic vegetation cover.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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            </Style>
        
            <Style>
                <Name>wq_annual_hue</Name>
                <Title>Surface Water Body Colour (Hue Angle)</Title>
                <Abstract>Surface water body colour</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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            </Style>
        
            <Style>
                <Name>wq_annual_owt</Name>
                <Title>Optical Water Types (OWT)</Title>
                <Abstract>Optical Water Types classification for inland waters following Spyrakos et al. (2018), based on dominant optical and biogeochemical properties.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>wq_annual_chla</Name>
                <Title>Chl-A (Index)</Title>
                <Abstract>Chlorophyll-a concentration (mg/m³) as an indicator of phytoplankton biomass and water productivity.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>wq_annual_water_mask</Name>
                <Title>Water Mask</Title>
                <Abstract>Binary water presence mask derived from WOfS</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>wq_annual_clear_water</Name>
                <Title>Clear Water</Title>
                <Abstract>Observations classified as clear (non-turbid) water</Abstract>
                
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            </Style>
        
            <Style>
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                <Title>Water Temperature - Annual Maximum</Title>
                <Abstract>Annual maximum surface water temperature</Abstract>
                
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            </Style>
        
            <Style>
                <Name>wq_annual_tirs_med</Name>
                <Title>Water Temperature - Annual Median</Title>
                <Abstract>Annual median surface water temperature</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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            </Style>
        
            <Style>
                <Name>wq_annual_tirs_min</Name>
                <Title>Water Temperature - Annual Minimum</Title>
                <Abstract>Annual minimum surface water temperature</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
         
    </Layer>
    
    
        
    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>Vegetation</Title>

        <Abstract>
            Vegetation
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>fc_ls</Name>
        <Title>Fractional cover (provisional)</Title>
        <Abstract>
Fractional cover describes the landscape in terms of coverage by green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.) and bare soil. It provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time.

This product has a spatial resolution of 30 m and a temporal coverage of 1980s to current. It is derived from Landsat Collection 2 Surface Reflectance product.

Fractional cover allows users to understand the large scale patterns and trends and inform evidence based decision making and policy on topics including wind and water erosion risk, soil carbon dynamics, land surface process monitoring, land management practices, vegetation studies, fuel load estimation, ecosystem modelling, and rangeland condition.

The fractional cover algorithm was developed by the Joint Remote Sensing Research Program. For more information on the product, see https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs.html

More techincal information about the fractional cover product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs.html)
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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       

        
        

        
        
        
        
            <Style>
                <Name>fc_rgb_unmasked</Name>
                <Title>Fractional cover (unmasked) - BS, PV, NPV</Title>
                <Abstract>Fractional cover representation, with green vegetation in green, brown vegetation in blue, and bare soil in red</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls/fc_rgb_unmasked/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>fc_rgb_masked</Name>
                <Title>Fractional cover (masked) - BS, PV, NPV</Title>
                <Abstract>Fractional cover representation, with green vegetation in green, brown vegetation in blue, and bare soil in red</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls/fc_rgb_masked/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ndvi_anomaly</Name>
        <Title>Mean NDVI and Anomalies</Title>
        <Abstract>
Standardised NDVI Anomalies provide a measure of vegetation health relative to long term average conditions by measuring the departure, in units of standard devaiations, away from the long-term mean. These monthly NDVI anomalies were calculated by combining NDVI observations from Landsat 8, Landsat 9, and Sentinel-2 for a given month, subtracting those values from the long term NDVI mean, then dividing by the long-term NDVI standard deviation. Postive values indicate vegetation is greener than average conditions, and are usually due to increased rainfall in a region. Negative values indicate additional plant stress relative to the long-term average.

This service provides data monthly from January 2017 at a spatial resolution of 30 m. The long term means and standard deviations used are available through the &#34;NDVI Climatology&#34; layer.

More technical information about the NDVI Anomaly service can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Anomaly_specs.html)

This service is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download click on a tile in the explorer page (https://explorer.digitalearth.africa/products/ndvi_anomaly)
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
        <EX_GeographicBoundingBox>
            <westBoundLongitude>-24.8740027469107</westBoundLongitude>
            <eastBoundLongitude>51.7379257135743</eastBoundLongitude>
            <southBoundLatitude>-35.2416360671221</southBoundLatitude>
            <northBoundLatitude>38.0420471664424</northBoundLatitude>
        </EX_GeographicBoundingBox>
        
            <BoundingBox CRS="EPSG:3857"
                 
                    
                         minx="-2768961.319776587" maxx="5759439.545135306"
                    
                    
                         miny="-4196767.198419044" maxy="4585367.371298524"
                    
                
            />
        
            <BoundingBox CRS="EPSG:4326"
                 
                     
                         minx="-35.2416360671221" maxx="38.0420471664424"
                     
                     
                         miny="-24.8740027469107" maxy="51.7379257135743"
                     
                 
            />
        
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                         minx="-2399999.999999996" maxx="4991999.999999995"
                    
                    
                         miny="-4223999.999032724" maxy="4511999.999211961"
                    
                
            />
        
        
        <Dimension name="time" units="ISO8601" default="2026-05-01">2017-01-01,2017-02-01,2017-03-01,2017-04-01,2017-05-01,2017-06-01,2017-07-01,2017-08-01,2017-09-01,2017-10-01,2017-11-01,2017-12-01,2018-01-01,2018-02-01,2018-03-01,2018-04-01,2018-05-01,2018-06-01,2018-07-01,2018-08-01,2018-09-01,2018-10-01,2018-11-01,2018-12-01,2019-01-01,2019-02-01,2019-03-01,2019-04-01,2019-05-01,2019-06-01,2019-07-01,2019-08-01,2019-09-01,2019-10-01,2019-11-01,2019-12-01,2020-01-01,2020-02-01,2020-03-01,2020-04-01,2020-05-01,2020-06-01,2020-07-01,2020-08-01,2020-09-01,2020-10-01,2020-11-01,2020-12-01,2021-01-01,2021-02-01,2021-03-01,2021-04-01,2021-05-01,2021-06-01,2021-07-01,2021-08-01,2021-09-01,2021-10-01,2021-11-01,2021-12-01,2022-01-01,2022-02-01,2022-03-01,2022-04-01,2022-05-01,2022-06-01,2022-07-01,2022-08-01,2022-09-01,2022-10-01,2022-11-01,2022-12-01,2023-01-01,2023-02-01,2023-03-01,2023-04-01,2023-05-01,2023-06-01,2023-07-01,2023-08-01,2023-09-01,2023-10-01,2023-11-01,2023-12-01,2024-01-01,2024-02-01,2024-03-01,2024-04-01,2024-05-01,2024-06-01,2024-07-01,2024-08-01,2024-09-01,2024-10-01,2024-11-01,2025-01-01,2025-02-01,2025-03-01,2025-04-01,2025-05-01,2025-06-01,2025-07-01,2025-08-01,2025-09-01,2025-10-01,2025-11-01,2025-12-01,2026-01-01,2026-02-01,2026-03-01,2026-04-01,2026-05-01</Dimension>
        

        
        

        
        
        
        
            <Style>
                <Name>style_ndvi_anomaly</Name>
                <Title>Standardised NDVI Anomaly</Title>
                <Abstract>Standardised NDVI Anomaly</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_anomaly/style_ndvi_anomaly/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean</Name>
                <Title>Mean NDVI</Title>
                <Abstract>Mean NDVI for the month</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_anomaly/style_ndvi_mean/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of valid observations included in calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_anomaly/style_count/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>fc_ls_summary_annual</Name>
        <Title>Fractional Cover annual summary</Title>
        <Abstract>
Fractional cover provides information about the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare soil for every 30 m x 30 m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall.

Each annual summary contains the 10th, 50th, and 90th percentiles estimated independently for the green vegetation, non-green vegetation, and bare fractions observed in a calendar year. It provides robust estimates of the lowest, median, and highest proportion values observed for each cover type, which can be used to characterise the land cover. The annual summaries are available from 1984 to the most recent full calendar year.

The fractional cover algorithm was developed by the Joint Remote Sensing Research Program. Before the percentile calculation, areas of water and cloud cover, as mapped in the Water Observations from Space (WOfS) Feature Layer, are excluded.
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <eastBoundLongitude>65.6673672518444</eastBoundLongitude>
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        <Dimension name="time" units="ISO8601" default="2025-01-01">1984-01-01,1985-01-01,1986-01-01,1987-01-01,1988-01-01,1989-01-01,1990-01-01,1991-01-01,1992-01-01,1993-01-01,1994-01-01,1995-01-01,1996-01-01,1997-01-01,1998-01-01,1999-01-01,2000-01-01,2001-01-01,2002-01-01,2003-01-01,2004-01-01,2005-01-01,2006-01-01,2007-01-01,2008-01-01,2009-01-01,2010-01-01,2011-01-01,2012-01-01,2013-01-01,2014-01-01,2015-01-01,2016-01-01,2017-01-01,2018-01-01,2019-01-01,2020-01-01,2021-01-01,2022-01-01,2023-01-01,2024-01-01,2025-01-01</Dimension>
        

        
        

        
        
        
        
            <Style>
                <Name>fc_rgb</Name>
                <Title>Median Fractional Cover</Title>
                <Abstract>Fractional cover medians - red is bare soil, green is green vegetation and blue is non-green vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/fc_rgb/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>green_veg_10</Name>
                <Title>Lowest Green Veg. Cover (10th Percentile)</Title>
                <Abstract>10th Percentile of Green Vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/green_veg_10/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>green_veg_50</Name>
                <Title>Median Green Veg. Cover</Title>
                <Abstract>50th Percentile of Green Vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/green_veg_50/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>green_veg_90</Name>
                <Title>Highest Green Veg. Cover (90th Percentile)</Title>
                <Abstract>90th Percentile of Green Vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/green_veg_90/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>non_green_veg_10</Name>
                <Title>Lowest Non-green Veg. Cover (10th Percentile)</Title>
                <Abstract>10th Percentile of Non Green Vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/non_green_veg_10/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>non_green_veg_50</Name>
                <Title>Median Non-green Veg. Cover</Title>
                <Abstract>50th Percentile of Non Green Vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/non_green_veg_50/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>non_green_veg_90</Name>
                <Title>Highest Non-green Veg. Cover (90th Percentile)</Title>
                <Abstract>90th Percentile of Non Green Vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/non_green_veg_90/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>bare_ground_10</Name>
                <Title>Lowest Bare Soil Cover (10th Percentile)</Title>
                <Abstract>10th Percentile of Bare Soil</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/bare_ground_10/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>bare_ground_50</Name>
                <Title>Median Bare Soil Cover</Title>
                <Abstract>50th Percentile of Bare Soil</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/bare_ground_50/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>bare_ground_90</Name>
                <Title>Highest Bare Soil Cover (90th Percentile)</Title>
                <Abstract>90th Percentile of Bare Soil</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/fc_ls_summary_annual/bare_ground_90/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ndvi_climatology_ls</Name>
        <Title>NDVI Climatology</Title>
        <Abstract>
Standardised NDVI Anomalies provide a measure of vegetation health relative to long term average conditions by measuring the departure, in units of standard devaiations, away from the long-term average. These NDVI climatologies (both mean and standard deviation) represent the long-term average baseline conditions of vegetation condition.

NDVI climatologies are available for each month at a spatial resolution of 30 m, and were calculated by aggregating data from 1984-2020.

More technical information about the NDVI Anomaly service can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Climatology_specs.html)

This service is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download click on a tile in the explorer page (https://explorer.digitalearth.africa/products/ndvi_climatology_ls)
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <eastBoundLongitude>51.7379257135743</eastBoundLongitude>
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            />
        
        

        
        

        
        
        
        
            <Style>
                <Name>style_ndvi_mean_jan</Name>
                <Title>Mean NDVI for January</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_jan/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_feb</Name>
                <Title>Mean NDVI for February</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_feb/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_mar</Name>
                <Title>Mean NDVI for March</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_mar/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_apr</Name>
                <Title>Mean NDVI for April</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_apr/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_may</Name>
                <Title>Mean NDVI for May</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_may/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_jun</Name>
                <Title>Mean NDVI for June</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_jun/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_jul</Name>
                <Title>Mean NDVI for July</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_jul/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_aug</Name>
                <Title>Mean NDVI for August</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_aug/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_sep</Name>
                <Title>Mean NDVI for September</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_sep/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_oct</Name>
                <Title>Mean NDVI for October</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_oct/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_nov</Name>
                <Title>Mean NDVI for November</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_nov/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_mean_dec</Name>
                <Title>Mean NDVI for December</Title>
                <Abstract>Long-term Mean NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_mean_dec/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_jan</Name>
                <Title>Std. Dev. NDVI for January</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_jan/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_feb</Name>
                <Title>Std. Dev. NDVI for February</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_feb/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_mar</Name>
                <Title>Std. Dev. NDVI for March</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_mar/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_apr</Name>
                <Title>Std. Dev. NDVI for April</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_apr/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_may</Name>
                <Title>Std. Dev. NDVI for May</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_may/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_jun</Name>
                <Title>Std. Dev. NDVI for June</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_jun/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_jul</Name>
                <Title>Std. Dev. NDVI for July</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_jul/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_aug</Name>
                <Title>Std. Dev. NDVI for August</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_aug/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_sep</Name>
                <Title>Std. Dev. NDVI for September</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_sep/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_oct</Name>
                <Title>Std. Dev. NDVI for October</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_oct/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_nov</Name>
                <Title>Std. Dev. NDVI for November</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_nov/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_ndvi_std_dec</Name>
                <Title>Std. Dev. NDVI for December</Title>
                <Abstract>Long-term Standard Deviation NDVI Climatology (1984-2020)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_ndvi_std_dec/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_count_jan</Name>
                <Title>Clear count for January</Title>
                <Abstract>Count of valid observations included in calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_count_jan/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_count_feb</Name>
                <Title>Clear count for February</Title>
                <Abstract>Count of valid observations included in calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ndvi_climatology_ls/style_count_feb/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>style_count_mar</Name>
                <Title>Clear count for March</Title>
                <Abstract>Count of valid observations included in calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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                        <Format>image/png</Format>
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                        <Format>image/png</Format>
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                <Title>Clear count for September</Title>
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                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                <Title>Clear count for October</Title>
                <Abstract>Count of valid observations included in calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>style_count_nov</Name>
                <Title>Clear count for November</Title>
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                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
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                <Title>Clear count for December</Title>
                <Abstract>Count of valid observations included in calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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        <Abstract>
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    <Layer>
        <Title>Agriculture</Title>

        <Abstract>
            Agriculture
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            ESA WorldCereal
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        <Title>ESA WorldCereal Temporary Cropland Extent</Title>
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            <Keyword>africa</Keyword>
            
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            <Style>
                <Name>style_temporarycrops</Name>
                <Title>Temporary crops</Title>
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        <KeywordList>
            
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            <Keyword>africa</Keyword>
            
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        <!-- No time dimension -->
        

        
        

        
        
        
        
            <Style>
                <Name>style_activecropland</Name>
                <Title>Active cropland</Title>
                <Abstract>Stand-Alone Style</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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    <Layer queryable="1">
        <Name>esa_worldcereal_maize_main</Name>
        <Title>ESA WorldCereal Main-Season Maize</Title>
        <Abstract>ESA WorldCereal 10m 2021 v1.0.0 layer esa_worldcereal_maize_main. For more info see https://esa-worldcereal.org/en/products/global-maps</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
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        <!-- No time dimension -->
        

        
        

        
        
        
        
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                <Name>style_maize</Name>
                <Title>Maize</Title>
                <Abstract>Stand-Alone Style</Abstract>
                
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        <Name>esa_worldcereal_maize_active</Name>
        <Title>ESA WorldCereal Active Maize Cropland</Title>
        <Abstract>ESA WorldCereal 10m 2021 v1.0.0 layer esa_worldcereal_maize_active. For more info see https://esa-worldcereal.org/en/products/global-maps</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
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        <!-- No time dimension -->
        

        
        

        
        
        
        
            <Style>
                <Name>style_activecropland</Name>
                <Title>Active cropland</Title>
                <Abstract>Stand-Alone Style</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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    <Layer queryable="1">
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        <Abstract>ESA WorldCereal 10m 2021 v1.0.0 layer esa_worldcereal_maize_irrigation. For more info see https://esa-worldcereal.org/en/products/global-maps</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
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        <!-- No time dimension -->
        

        
        

        
        
        
        
            <Style>
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                <Abstract>Stand-Alone Style</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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        <Name>esa_worldcereal_wintercereals</Name>
        <Title>ESA WorldCereal Winter Cereals</Title>
        <Abstract>ESA WorldCereal 10m 2021 v1.0.0 layer esa_worldcereal_wintercereals. For more info see https://esa-worldcereal.org/en/products/global-maps</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
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        <!-- No time dimension -->
        

        
        

        
        
        
        
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                <Name>style_wintercereals</Name>
                <Title>Winter cereals</Title>
                <Abstract>Stand-Alone Style</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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        <Name>esa_worldcereal_wintercereals_irrigation</Name>
        <Title>ESA WorldCereal Winter Cereals – Irrigation</Title>
        <Abstract>ESA WorldCereal 10m 2021 v1.0.0 layer esa_worldcereal_wintercereals_irrigation. For more info see https://esa-worldcereal.org/en/products/global-maps</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
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        <!-- No time dimension -->
        

        
        

        
        
        
        
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                <Title>Irrigated</Title>
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                        <Format>image/png</Format>
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        <Title>Elevation and topography</Title>

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        <Title>Shuttle Radar Topography Mission Digital Elevation Model (30 m)</Title>
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A Digital Elevation Model (DEM) is a digital representation of Earth’s topography. It helps to understand the land surface characteristics in the height dimension. It is also used as an ancillary dataset to calculate illumination and viewing geometry of a satellite imagery. DE Africa provides access to the Shuttle Radar Topography Mission (SRTM) v 3.0 (SRTMGL1) product.

This elevation model has a spatial resolution of 30 m and is derived from data collected by NASA&#39;s Shuttle Radar Topography Mission in 2000.

It is provided by NASA&#39;s Land Processes Distributed Active Archive Center (LP DAAC).

For more information, see https://lpdaac.usgs.gov/products/srtmgl1v003/

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki).
</Abstract>
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                <Title>Greyscale</Title>
                <Abstract>Greyscale elevation</Abstract>
                
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                <Abstract>Coloured elevation</Abstract>
                
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    <Layer queryable="1">
        <Name>nasadem</Name>
        <Title>NASA Digital Elevation Model (30 m)</Title>
        <Abstract>
NASADEM provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the Shuttle Radar Topography Mission (SRTM).

The product is indexed directly from Microsoft&#39;s Planetary Computer, for more information, see https://planetarycomputer.microsoft.com/dataset/nasadem
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
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            <Style>
                <Name>greyscale</Name>
                <Title>Greyscale</Title>
                <Abstract>Greyscale elevation</Abstract>
                
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                <Title>Coloured</Title>
                <Abstract>Coloured elevation</Abstract>
                
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    <Layer queryable="1">
        <Name>dem_cop_30</Name>
        <Title>Copernicus Digital Elevation Model (30 m)</Title>
        <Abstract>
The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. This DSM is derived from an edited DSM named WorldDEM, where flattening of water bodies and consistent flow of rivers has been included. In addition, editing of shore- and coastlines, special features such as airports, and implausible terrain structures has also been applied.

he WorldDEM product is based on the radar satellite data acquired during the TanDEM-X Mission, which is funded by a Public Private Partnership between the German State, represented by the German Aerospace Centre (DLR) and Airbus Defence and Space. OpenTopography is providing access to the global 30m (GLO-30) DSM through the public AWS S3 bucket established by Sinergise.

This data is indexed directly from Open Topography, for more information, see https://portal.opentopography.org/raster?opentopoID=OTSDEM.032021.4326.3
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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                <Name>greyscale</Name>
                <Title>Greyscale</Title>
                <Abstract>Greyscale elevation</Abstract>
                
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                <Title>Coloured</Title>
                <Abstract>Coloured elevation</Abstract>
                
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    <Layer queryable="1">
        <Name>srtm_deriv</Name>
        <Title>SRTM-derived Slope and Flatness Indices (30 m)</Title>
        <Abstract>
Derivative products of a Digital Elevation Model (DEM) can be used to better understand the Earth&#39;s topography and model illumination, geometry, and aspect. DE Africa provides three Shuttle Radar Topography Mission (SRTM) derivative products:

 - Slope: rate of elevation change, expressed as a percentage
 - Multi-resolution Valley Bottom Flatness (MrVBF): topographic index used to identify valley bottoms, which are useful for hydrological and sedimentation modelling. In this product, 0 indicates areas which are not valley bottom areas, while 1 - 7 indicate increasingly flatter and larger valley bottoms
 - Multi-resolution Ridge Top Flatness (MrRTF): topographic index used for landform classification. It uses a scale of 0 - 7 inclusive; 0 indicates steep or low locations while 1 - 7 indicate increasingly larger areas of high flat land

The latter two identify the dominant nature of the topography as either ridge-like or valley-like (Gallant and Dowling, 2003).

These derivative products have a spatial resolution of 30 m and are derived from data collected by NASA&#39;s Shuttle Radar Topography Mission in 2000.

SRTM data is provided by NASA&#39;s Land Processes Distributed Active Archive Center (LP DAAC).

For more information on SRTM, see https://lpdaac.usgs.gov/products/srtmgl1v003/
For more information on generating the derivative product, see https://github.com/digitalearthafrica/dem-derivative

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki).
</Abstract>
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            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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                <Title>Slope</Title>
                <Abstract>Slope (percentage) derived from SRTM elevation model</Abstract>
                
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                <Title>Multi-resolution Valley Bottom Flatness (MrVBF)</Title>
                <Abstract>Multi-resolution Valley Bottom Flatness (MrVBF) derived from SRTM elevation model</Abstract>
                
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                <Title>Multi-resolution Ridge Top Flatness (MrRTF)</Title>
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    <Layer>
        <Title>Land cover</Title>

        <Abstract>
            Land Cover
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    <Layer queryable="1">
        <Name>cci_landcover</Name>
        <Title>ESA CCI Landcover</Title>
        <Abstract>
As part of the ESA Climate Change Initiative (CCI), the Land Cover project is concerned with the generation of the land cover ECV. Land cover is defined as the physical material at the surface of the earth. Land covers include grass, asphalt, trees, bare ground, water, etc.

The project&#39;s objective is to critically revisit all algorithms required for the generation of a global land product in the light of GCOS requirements, and to design and demonstrate a prototype system delivering in a consistent way over years and from various EO instruments global land cover information matching the needs of key users&#39; belonging to the climate change community. The focus is placed on the ESA and Member States missions providing near daily global surface reflectance observation at moderate spatial resolution (MERIS FR &amp; RR, SPOT VEGETATION) but the contribution of ESA SAR sensors will also be investigated to tackle specific land cover discrimination issue.

For more information, see https://climate.esa.int/en/projects/land-cover/about/
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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            <Style>
                <Name>style_colours</Name>
                <Title>Coloured</Title>
                <Abstract>Land cover colours</Abstract>
                
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    <Layer queryable="1">
        <Name>esa_worldcover_2020</Name>
        <Title>ESA WorldCover 2020</Title>
        <Abstract>
The ESA WorldCover 2020 product is a global, 10 m resolution land cover product, which Digital Earth Africa is making accessible over Africa.

The product uses Sentinel-1 and Sentinel-2 data to produce 11 land cover classes, according to the GAO LCCS schema.

For more information, see https://esa-worldcover.s3.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf
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        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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                <Title>Coloured</Title>
                <Abstract>Land cover colours</Abstract>
                
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    <Layer queryable="1">
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        <Title>ESA WorldCover 2021</Title>
        <Abstract>
The ESA WorldCover 2021 product is a global, 10 m resolution land cover product, which Digital Earth Africa is making accessible over Africa.

The product uses Sentinel-1 and Sentinel-2 data to produce 11 land cover classes, according to the GAO LCCS schema.

For more information, see https://worldcover2021.esa.int/data/docs/WorldCover_PUM_V2.0.pdf
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        <KeywordList>
            
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            <Keyword>landsat</Keyword>
            
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                <Name>style_colours</Name>
                <Title>Coloured</Title>
                <Abstract>Land cover colours</Abstract>
                
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    <Layer queryable="1">
        <Name>io_lulc_v2</Name>
        <Title>Impact Observatory Land Use/Land Cover</Title>
        <Abstract>
Time series of annual global maps of land use and land cover (LULC), currently available from 2017 to 2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.

This dataset was generated by Impact Observatory, who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.

For more information, see https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02.
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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                <Name>style_colours</Name>
                <Title>Coloured</Title>
                <Abstract>Land cover colours</Abstract>
                
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    <Layer>
        <Title>Meteorology</Title>

        <Abstract>
            Meteorology
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    <Layer queryable="1">
        <Name>rainfall_chirps_monthly</Name>
        <Title>Rainfall (monthly)</Title>
        <Abstract>
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set.

Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates climatology (CHPclim model), 0.05° resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.

CHIRPS is a USGS and Climate Hazards Centre initiative with funding from USAID, NASA, and NOAA. Find out more on the CHIRPS website https://www.chc.ucsb.edu/data/chirps.
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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       

        
        

        
        
        
        
            <Style>
                <Name>rainfall_monthly</Name>
                <Title>Monthly rainfall - CHIRPS</Title>
                <Abstract>Monthly rainfall from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) 35+ year quasi-global rainfall data set.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/rainfall_chirps_monthly/rainfall_monthly/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>Satellite images</Title>

        <Abstract>
            Satellite images
        </Abstract>

        
        
            
        
    <Layer>
        <Title>Radar backscatter</Title>

        <Abstract>
            Radar backscatter
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>s1_rtc</Name>
        <Title>Normalised radar backscatter (Sentinel-1)</Title>
        <Abstract>

Synthetic Aperture Radar (SAR) data have been shown to provide different and complementary information to the more common optical remote sensing data. Radar backscatter response is a function of topography, land cover structure, orientation, and moisture characteristics—including vegetation biomass—and the radar signal can penetrate clouds, providing information about the earth’s surface where optical sensors cannot. Digital Earth Africa provides access to Normalized Radar Backscatter data, for which Radiometric Terrain Correction (RTC) has been applied so data acquired with different imaging geometries over the same region can be compared.

The twin Sentinel-1 satellites, launched in 2014 and 2016, are operated by the European Space Agency (ESA) as part of European Commission&#39;s Copernicus Programme. They currently collects C-band SAR data every 6 to 12 days over Africa.

This product contains radar measurement in C-band and in VV and VH polarizations. It has a spatial resolution of approximate 20 m, a temporal coverage of July 2018 to current and is updated as new images are acquired. Data is provided as Gamma0 in linear power, which can be converted to backscatter in decibel unit using 10*log10(DN).

This dataset is processed by Sinergise Sentinel Hub using Sentinel-1 GRD product provided by ESA as input. RTC has been calcuated using 30 m Copernicus Digital Elevation Model.

More techincal information about the Sentinel-1 Normalized Backscatter can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_specs.html)

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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                <Title>VV, VH and VH/VV</Title>
                <Abstract>False colour representation of VV, VH and VH/VV for R, G and B respectively</Abstract>
                
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                <Title>VV</Title>
                <Abstract>VV band</Abstract>
                
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                <Title>VH</Title>
                <Abstract>VH band</Abstract>
                
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                <Title>Radar Vegetation Index</Title>
                <Abstract>Dual-pol radar vegetation index for Sentinel-1</Abstract>
                
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        <Abstract>
            Surface reflectance
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        <Name>s2_l2a</Name>
        <Title>Surface reflectance (Sentinel-2)</Title>
        <Abstract>
Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth&#39;s surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.

DE Africa provides Sentinel 2 Level-2A surface reflectance data from European Commission&#39;s Copernicus Programme. Sentinel-2 is an Earth observation mission that systematically acquires optical imagery at up to 10 m spatial resolution. The mission is based on a constellation of two identical satellites in the same orbit, 180° apart for optimal coverage and data delivery. Together, they cover all Earth&#39;s land surfaces, large islands, inland and coastal waters every 3-5 days. Each of the Sentinel-2 satellites carries a wide swath high-resolution multispectral imager with 13 spectral bands.

This product has a temporal coverage of 2017 to current and is updated as new images are acquired. Images in different spectral bands are provided at spatial resolutions of 10, 20 or 60 m. The surface reflectance values are scaled to be between 0 and 10,000.

Sentinel-2 Level-2A data are provided by the European Space Agency (ESA).  Data prior to 2017 are processed from Level-1C to Level-2A with ESA&#39;s Sen2Cor software by Sinergise. All images are converted to Cloud Optimised GeoTIFF format by Element 84, Inc.

For more information on the Sentinel-2 Level-2A surface reflectance product, see https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_specs.html

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
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       

        
        

        
        
        
        
            <Style>
                <Name>simple_rgb</Name>
                <Title>Simple RGB</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/s2_l2a/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/s2_l2a/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/s2_l2a/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndci</Name>
                <Title>NDCI - Red Edge, Red</Title>
                <Abstract>Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/s2_l2a/ndci/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>aerosol</Name>
                <Title>Narrow Blue - 440</Title>
                <Abstract>Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 490</Title>
                <Abstract>Blue band, centered on 490nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_1</Name>
                <Title>Vegetation Red Edge - 710</Title>
                <Abstract>Near infra-red band, centred on 710nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_2</Name>
                <Title>Vegetation Red Edge - 740</Title>
                <Abstract>Near infra-red band, centred on 740nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_3</Name>
                <Title>Vegetation Red Edge - 780</Title>
                <Abstract>Near infra-red band, centred on 780nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>narrow_nir</Name>
                <Title>Narrow Near Infrared - 870</Title>
                <Abstract>Near infra-red band, centred on 865nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, centered on 1610nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared (SWIR) - 2200</Title>
                <Abstract>Short wave infra-red band 2, centered on 2200nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>scl</Name>
                <Title>Scene Classification</Title>
                <Abstract>Sentinel-2 Scene Classification Layer</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/s2_l2a/scl/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ls9_sr</Name>
        <Title>Surface reflectance (Landsat 9)</Title>
        <Abstract>
Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth&#39;s surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.

DE Africa provides access to Landsat Collection 2 Level-2 Surface Reflectance products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.

More techincal information about the Landsat Surface Reflectance product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html).

Landsat 9 product has a spatial resolution of 30 m and a temporal coverage of 2021 to present.

Landsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.

For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
        <EX_GeographicBoundingBox>
            <westBoundLongitude>-28.2007692100628</westBoundLongitude>
            <eastBoundLongitude>65.0237243210835</eastBoundLongitude>
            <southBoundLatitude>-48.5136806901255</southBoundLatitude>
            <northBoundLatitude>39.9526082331562</northBoundLatitude>
        </EX_GeographicBoundingBox>
        
            <BoundingBox CRS="EPSG:3857"
                 
                    
                         minx="-3139295.2684428184" maxx="7238407.880905214"
                    
                    
                         miny="-6192742.461312992" maxy="4859057.825134323"
                    
                
            />
        
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       

        
        

        
        
        
        
            <Style>
                <Name>simple_rgb</Name>
                <Title>True colour - RGB</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - SWIR, NIR, Green</Title>
                <Abstract>False colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls9_sr/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls9_sr/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls9_sr/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue</Title>
                <Abstract>Blue band</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green</Title>
                <Abstract>Green band</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red</Title>
                <Abstract>Red band</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR)</Title>
                <Abstract>Near infra-red band</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared 1 (SWIR1)</Title>
                <Abstract>Shortwave infrared band 1</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared 2 (SWIR2)</Title>
                <Abstract>Shortwave infrared band 2</Abstract>
                
            </Style>
        
            <Style>
                <Name>pixel_quality</Name>
                <Title>Pixel Quality</Title>
                <Abstract>Pixel Quality</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls9_sr/pixel_quality/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ls8_sr</Name>
        <Title>Surface reflectance (Landsat 8)</Title>
        <Abstract>
Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth&#39;s surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.

DE Africa provides access to Landsat Collection 2 Level-2 Surface Reflectance products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.

More techincal information about the Landsat Surface Reflectance product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html).

Landsat 8 product has a spatial resolution of 30 m and a temporal coverage of 2013 to present.

Landsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.

For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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       

        
        

        
        
        
        
            <Style>
                <Name>simple_rgb</Name>
                <Title>True colour - RGB</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - SWIR, NIR, Green</Title>
                <Abstract>False colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls8_sr/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls8_sr/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls8_sr/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue</Title>
                <Abstract>Blue band</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green</Title>
                <Abstract>Green band</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red</Title>
                <Abstract>Red band</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR)</Title>
                <Abstract>Near infra-red band</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared 1 (SWIR1)</Title>
                <Abstract>Shortwave infrared band 1</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared 2 (SWIR2)</Title>
                <Abstract>Shortwave infrared band 2</Abstract>
                
            </Style>
        
            <Style>
                <Name>pixel_quality</Name>
                <Title>Pixel Quality</Title>
                <Abstract>Pixel Quality</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls8_sr/pixel_quality/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ls7_sr</Name>
        <Title>Surface reflectance (Landsat 7)</Title>
        <Abstract>
Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth&#39;s surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.

DE Africa provides access to Landsat Collection 2 Level-2 Surface Reflectance products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.

More techincal information about the Landsat Surface Reflectance product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html).

Landsat 7 product has a spatial resolution of 30 m and a temporal coverage of 1999 to present.

Landsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.

For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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       

        
        

        
        
        
        
            <Style>
                <Name>simple_rgb</Name>
                <Title>True colour - RGB</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - SWIR, NIR, Green</Title>
                <Abstract>False colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls7_sr/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls7_sr/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls7_sr/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue</Title>
                <Abstract>Blue band</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green</Title>
                <Abstract>Green band</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red</Title>
                <Abstract>Red band</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR)</Title>
                <Abstract>Near infra-red band</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared 1 (SWIR1)</Title>
                <Abstract>Shortwave infrared band 1</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared 2 (SWIR2)</Title>
                <Abstract>Shortwave infrared band 2</Abstract>
                
            </Style>
        
            <Style>
                <Name>pixel_quality</Name>
                <Title>Pixel Quality</Title>
                <Abstract>Pixel Quality</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls7_sr/pixel_quality/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ls5_sr</Name>
        <Title>Surface reflectance (Landsat 5)</Title>
        <Abstract>
Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth&#39;s surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.

DE Africa provides access to Landsat Collection 2 Level-2 Surface Reflectance products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.

More techincal information about the Landsat Surface Reflectance product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html).

Landsat 5 product has a spatial resolution of 30 m and a temporal coverage of 1984 to 2012.

Landsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.

For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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       

        
        

        
        
        
        
            <Style>
                <Name>simple_rgb</Name>
                <Title>True colour - RGB</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - SWIR, NIR, Green</Title>
                <Abstract>False colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls5_sr/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls5_sr/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls5_sr/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue</Title>
                <Abstract>Blue band</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green</Title>
                <Abstract>Green band</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red</Title>
                <Abstract>Red band</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR)</Title>
                <Abstract>Near infra-red band</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_1</Name>
                <Title>Shortwave Infrared 1 (SWIR1)</Title>
                <Abstract>Shortwave infrared band 1</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir_2</Name>
                <Title>Shortwave Infrared 2 (SWIR2)</Title>
                <Abstract>Shortwave infrared band 2</Abstract>
                
            </Style>
        
            <Style>
                <Name>pixel_quality</Name>
                <Title>Pixel Quality</Title>
                <Abstract>Pixel Quality</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls5_sr/pixel_quality/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>Surface temperature</Title>

        <Abstract>
            Surface temperature
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>ls9_st</Name>
        <Title>Surface temperature (Landsat 9)</Title>
        <Abstract>
Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.

DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.

More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html).

Landsat 9 product has a spatial resolution of 30 m and a temporal coverage of 2021 to present.

Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey.

For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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                <Name>surface_temperature</Name>
                <Title>Surface temperature - Celsius</Title>
                <Abstract>Surface temperature in degrees Celsius</Abstract>
                
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                        <Format>image/png</Format>
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                <Title>Surface temperature uncertainty - Celsius</Title>
                <Abstract>Surface temperature uncertainty in degrees Celsius</Abstract>
                
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                <Name>surface_temperature_masked</Name>
                <Title>Surface temperature (cloud masked) - Celsius</Title>
                <Abstract>Surface temperature in degrees Celsius</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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        <Title>Surface temperature (Landsat 8)</Title>
        <Abstract>
Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.

DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.

More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html).

Landsat 8 product has a spatial resolution of 30 m and a temporal coverage of 2013 to present.

Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey.

For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
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            <Style>
                <Name>surface_temperature</Name>
                <Title>Surface temperature - Celsius</Title>
                <Abstract>Surface temperature in degrees Celsius</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
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                <Title>Surface temperature uncertainty - Celsius</Title>
                <Abstract>Surface temperature uncertainty in degrees Celsius</Abstract>
                
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                        <Format>image/png</Format>
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                <Name>surface_temperature_masked</Name>
                <Title>Surface temperature (cloud masked) - Celsius</Title>
                <Abstract>Surface temperature in degrees Celsius</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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        <Title>Surface temperature (Landsat 7)</Title>
        <Abstract>
Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.

DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.

More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html).

Landsat 7 product has a spatial resolution of 30 m and a temporal coverage of 1999 to present.

Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey.

For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
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            <Keyword>landsat</Keyword>
            
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    <Layer queryable="1">
        <Name>ls5_st</Name>
        <Title>Surface temperature (Landsat 5)</Title>
        <Abstract>
Surface temperature measures the Earth’s surface temperature and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature is also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.

DE Africa provides access to Landsat Collection 2 Level-2 Surface Temperature products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.

More techincal information about the Landsat Surface Temperature product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html).

Landsat 5 product has a spatial resolution of 30 m and a temporal coverage of 1984 to 2012.

Landsat Level- 2 Surface Temperature Science Product courtesy of the U.S. Geological Survey.

For more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
        </KeywordList>
        
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            <northBoundLatitude>40.7157419926884</northBoundLatitude>
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       

        
        

        
        
        
        
            <Style>
                <Name>surface_temperature</Name>
                <Title>Surface temperature - Celsius</Title>
                <Abstract>Surface temperature in degrees Celsius</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls5_st/surface_temperature/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>surface_temperature_uncertainty</Name>
                <Title>Surface temperature uncertainty - Celsius</Title>
                <Abstract>Surface temperature uncertainty in degrees Celsius</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls5_st/surface_temperature_uncertainty/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>surface_temperature_masked</Name>
                <Title>Surface temperature (cloud masked) - Celsius</Title>
                <Abstract>Surface temperature in degrees Celsius</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/ls5_st/surface_temperature_masked/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>Satellite image mosaics</Title>

        <Abstract>
            Satellite image mosaics
        </Abstract>

        
        
            
        
    <Layer>
        <Title>Monthly radar backscatter</Title>

        <Abstract>
            Monthly radar backscatter
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>s1_monthly_mosaic</Name>
        <Title>Monthly mosaic (Sentinel-1)</Title>
        <Abstract>

Synthetic Aperture Radar (SAR) data have been shown to provide different and complementary information to the more common optical remote sensing data. Radar backscatter response is a function of topography, land cover structure, orientation, and moisture characteristics—including vegetation biomass—and the radar signal can penetrate clouds, providing information about the earth’s surface where optical sensors cannot. Digital Earth Africa provides access to Normalized Radar Backscatter data, for which Radiometric Terrain Correction (RTC) has been applied so data acquired with different imaging geometries over the same region can be compared.

The Sentinel-1 monthly mosaics are generated by combining RTC images acquired within each calendar month using a multitemporal compositing algorithm. This algorithm calculates a weighted average of valid pixels, assigning higher weights to pixels with higher local resolution (e.g., slopes facing away from the sensor). This local resolution weighting approach minimizes noise and improves spatial homogeneity in the composites.

This product contains radar measurement in C-band and in VV and VH polarizations. It has a spatial resolution of 20 m, a temporal coverage of January 2020 to current and is updated as new mosaics are generated. Data is provided as Gamma0 in linear power, which can be converted to backscatter in decibel unit using 10*log10(DN).

This dataset is processed by Sinergise Sentinel Hub using the Sentinel-1 RTC product as input.

More techincal information about the Sentinel-1 Normalized Backscatter can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_Monthly_Mosaic_specs.html)

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
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            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
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                <Abstract>VV band</Abstract>
                
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                <Title>VH</Title>
                <Abstract>VH band</Abstract>
                
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                <Name>rvi</Name>
                <Title>Radar Vegetation Index</Title>
                <Abstract>Dual-pol radar vegetation index for Sentinel-1</Abstract>
                
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        <Title>Annual radar backscatter</Title>

        <Abstract>
            Annual radar backscatter
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    <Layer queryable="1">
        <Name>alos_palsar_mosaic</Name>
        <Title>Annual mosaic (ALOS/PALSAR)</Title>
        <Abstract>
Synthetic Aperture Radar (SAR) data have been shown to provide different and complementary information to the more common optical remote sensing data. Radar backscatter response is a function of topography, land cover structure, orientation, and moisture characteristics—including vegetation biomass—and the radar signal can penetrate clouds, providing information about the earth’s surface where optical sensors cannot. Digital Earth Africa provides access to Normalized Radar Backscatter data, for which Radiometric Terrain Correction (RTC) has been applied so data acquired with different imaging geometries over the same region can be compared.

The ALOS/PALSAR annual mosaic is a global 25 m resolution dataset that combines data from many images captured by JAXA&#39;s PALSAR and PALSAR-2 sensors on ALOS-1 and ALOS-2 satellites respectively.

This product contains radar measurement in L-band and in HH and HV polarizations. It has a spatial resolution of 25 m and is available annually for 2007 to 2010 (ALOS/PALSAR) and 2015 to 2018 (ALOS-2/PALSAR-2). Data is provided as digital number (DN), which can be converted to backscatter in decibel unit using 10*log10(𝐷𝑁^2)-83.0.

It is part of a global dataset provided by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Research Center.

For more information on the product, see https://docs.digitalearthafrica.org/en/latest/data_specs/ALOS_PALSAR_annual_mosaic_specs.html

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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        <Name>jers_sar_mosaic</Name>
        <Title>Annual mosaic (JERS)</Title>
        <Abstract>
Synthetic Aperture Radar (SAR) data have been shown to provide different and complementary information to the more common optical remote sensing data. Radar backscatter response is a function of topography, land cover structure, orientation, and moisture characteristics—including vegetation biomass—and the radar signal can penetrate clouds, providing information about the earth’s surface where optical sensors cannot. Digital Earth Africa provides access to Normalized Radar Backscatter data, for which Radiometric Terrain Correction (RTC) has been applied so data acquired with different imaging geometries over the same region can be compared.
The JERS annual mosaic is generated from images acquired by the SAR sensor on the Japanese Earth Resources Satellite-1 (JERS-1) satellite.

This product contains radar measurement in L-band and HH polarization. It has a spatial resolution of 25 m and is available for 1996. Data is provided as digital number (DN), which can be converted to backscatter in decibel unit using 10*log10(𝐷𝑁^2)-84.66.

It is part of a global dataset provided by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Research Center.

For more information on the product, see https://docs.digitalearthafrica.org/en/latest/data_specs/ALOS_PALSAR_annual_mosaic_specs.html

This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).
</Abstract>
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            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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        <Title>Urban</Title>

        <Abstract>
            Urban
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    <Layer>
        <Title>World Settlement Footprint</Title>

        <Abstract>
            World Settlement Footprint and Evolution
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        <Title>World Settlement Footprint 2015</Title>
        <Abstract>
The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets.The World Settlement Footprint (WSF) 2015 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively). The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries (Marconcini et al., 2020).

For more information, see https://austriaca.at/?arp=0x003c9b4c.
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
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        <Title>World Settlement Footprint 2019</Title>
        <Abstract>
The World Settlement Footprint WSF 2015 version 2 (wsf2019 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the wsf2019 v2 is a pilot product generated by combining multiple datasets.The World Settlement Footprint (WSF) 2015 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively). The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries (Marconcini et al., 2020).

For more information, see https://austriaca.at/?arp=0x003c9b4c.
</Abstract>
        <KeywordList>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
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        <Title>World Settlement Footprint Evolution</Title>
        <Abstract>
The World Settlement Footprint (WSF) Evolution is a 30m resolution dataset derived from Landsat-5/7 outlining the global settlement extent on a yearly basis from 1985 to 2015.

For more information, see https://austriaca.at/?arp=0x003c9b4c.
</Abstract>
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            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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    <Layer>
        <Title>Vegetation</Title>

        <Abstract>
            Vegetation
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    <Layer queryable="1">
        <Name>gmw</Name>
        <Title>Global Mangrove Watch</Title>
        <Abstract>
The Global Mangrove Watch (GMW) aims to provide geospatial information about mangrove extent and changes to the Ramsar Convention, national wetland practitioners, decision makers and NGOs. It is part of the Ramsar Science and Technical Review Panel (STRP) work plan for 2016-2018 and a Pilot Project to the Ramsar Global Wetlands Observation System (GWOS), which is implemented under the GEO-Wetlands Initiative. The primary objective of the GMW has been to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation.

DE Africa provides access to GMW mangrove extent data over Africa. This dataset is available for the years 1996, 2007, 2008, 2009, 2010, 2015, and 2016. The extents have been rasterized to a spatial resolution of 0.0002 deg (~22 m).

For more information, see https://data.unep-wcmc.org/datasets/45.
</Abstract>
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            <Keyword>WOfS</Keyword>
            
            <Keyword>africa</Keyword>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>landsat</Keyword>
            
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                <Title>Mangroves</Title>
                <Abstract>Global mangrove watch mangroves</Abstract>
                
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                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.digitalearth.africa/legend/gmw/mangrove/legend.png"/>
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            </Style>
         
    </Layer>
    
    
        
    </Layer>
    
    
        
    </Layer>
    
    
        
    </Layer>
    
    
    
</Capability>
</WMS_Capabilities>