A\ncentral focus for governing bodies in Africa is the need to secure the\nnecessary food sources to support their populations. It has been estimated that\nthe current production of crops will need to double by 2050 to meet future\nneeds for food production. Higher level crop-based products that can assist\nwith managing food insecurity, such as cropping watering intensities, crop\ntypes, or crop productivity, require as a starting point precise and accurate\ncropland extent maps indicating where cropland occurs. Current cropland extent\nmaps are either inaccurate, have coarse spatial resolutions, or are not updated\nregularly. An accurate, high-resolution, and regularly updated cropland area\nmap for the African continent is therefore recognised as a gap in the current\ncrop monitoring services.
Key Properties
Geographic Coverage: Continental Africa - approximately 37° North to 35 South
Temporal Coverage: 2019
Spatial Resolution: 10 x 10 meter
Update Frequency: TBD
Number of Bands: 3 Bands
Parent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMAD
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Cropland is\ndefined as:
\n\n"a\npiece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is\nsowed/planted and harvestable at least once within the 12 months after the\nsowing/planting date."
This\ndefinition will exclude non-planted grazing lands and perennial crops which can\nbe difficult for satellite imagery to differentiate from natural vegetation.
\n\nThe\nprovisional cropland extent maps have a resolution of 10 metres and were built\nusing Copernicus Sentinel-2 satellite images from 2019. The cropland extent\nmaps were built separately using extensive training data from Eastern, Western,\nand Northern Africa, coupled with a Random Forest machine learning model. A\ndetailed exploration of the methods used to produce the cropland extent map can\nbe found in the Jupyter Notebooks in DE Africa’s crop-mask GitHub\nrepository.
\n\nIndependent\nvalidation datasets suggest the following accuracies:
\n\nThe algorithms\nfor all regions tend to report more omission errors (labelling actual crops as\nnon-crops) than commission errors (labelling non-crops as crops). Where\ncommission errors occur, they tend to be focussed around wetlands and seasonal\ngrasslands which spectrally resemble some kinds of cropping.
Available Bands
Band ID | Description | Value range | Data type | NoData/Fill value |
|---|---|---|---|---|
mask | crop extent (pixel) |
|
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|
prob | crop probability (pixel) |
|
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|
filtered | crop extent (object-based) |
|
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|
mask: This band\ndisplays cropped regions as a binary map. Values of 1 indicate the\npresence of crops, while a value of 0 indicates the absence of\ncropping. This band is a pixel-based cropland extent map, meaning the\nmap displays the raw output of the pixel-based Random Forest classification.
\n\nprob: This band\ndisplays the prediction probabilities for the ‘crop’ class. As this service\nuses a random forest classifier, the prediction probabilities refer to the\npercentage of trees that voted for the random forest classification. For\nexample, if the model had 200 decision trees in the random forest, and 150 of\nthe trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75\n%. Thresholding this band at > 50 % will produce a map identical\nto mask.
\n\nfiltered: This band displays cropped regions as a\nbinary map. Values of 1 indicate the presence of crops, while a value\nof 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has\nbeen filtered using an image segmentation algorithm (see this paper for\ndetails on the algorithm used). During this process, segments smaller than 1 Ha\n(100 10m x 10m pixels) are merged with neighbouring segments, resulting in a\nmap where the smallest classified region is 1 Ha in size.\nThe filtered dataset is provided as a complement to\nthe mask band; small commission errors are removed by object-based\nfiltering, and the ‘salt and pepper’ effect typical of classifying pixels is\ndiminished.
\n\n
More details on this dataset can be found here.
| \n Attribute \n | \n \n |
| \n rate_time \n | \n \n Annual rates of change (in metres per year)\n calculated by linearly regressing annual shoreline distances against time\n (excluding outliers). Negative values indicate retreat and positive values\n indicate growth. \n | \n
| \n sig_time \n | \n \n Significance (p-value) of the linear relationship\n between annual shoreline distances and time. Small values (e.g. p-value <\n 0.01) may indicate a coastline is undergoing consistent coastal change\n through time. \n | \n
| \n se_time \n | \n \n Standard error (in metres) of the linear\n relationship between annual shoreline distances and time. This can be used to\n generate confidence intervals around the rate of change given by rate_time\n (e.g. 95% confidence interval = se_time * 1.96). \n | \n
| \n outl_time \n | \n \n Individual annual shoreline are noisy estimators\n of coastline position that can be influenced by environmental conditions\n (e.g. clouds, breaking waves, sea spray) or modelling issues (e.g. poor tidal\n modelling results or limited clear satellite observations). To obtain\n reliable rates of change, outlier shorelines are excluded using a robust\n Median Absolute Deviation outlier detection algorithm, and recorded in this\n column. \n | \n
| \n sce \n | \n \n Shoreline Change Envelope (SCE). A measure of the\n maximum change or variability across all annual shorelines, calculated by\n computing the maximum distance between any two annual shoreline (excluding\n outliers). This statistic excludes sub-annual shoreline variability. \n | \n
| \n nsm \n | \n \n Net Shoreline Movement (NSM). The distance\n between the oldest (2000) and most recent annual shoreline (excluding\n outliers). Negative values indicate the coastline retreated between the\n oldest and most recent shoreline; positive values indicate growth. This\n statistic does not reflect sub-annual shoreline variability, so will\n underestimate the full extent of variability at any given location. \n | \n
| \n max_year, min_year \n | \n \n The year that annual shorelines were at their\n maximum (i.e. located furthest towards the ocean) and their minimum (i.e.\n located furthest inland) respectively (excluding outliers). This statistic\n excludes sub-annual shoreline variability. \n | \n
| \n outl_time \n | \n \n Individual annual shoreline are noisy estimators\n of coastline position that can be influenced by environmental conditions\n (e.g. clouds, breaking waves, sea spray) or modelling issues (e.g. poor tidal\n modelling results or limited clear satellite observations). To obtain\n reliable rates of change, outlier shorelines are excluded using a robust\n Median Absolute Deviation outlier detection algorithm, and recorded in this\n column. \n | \n
Digital Earth Africa provides access to Normalized Radar Backscatter data, for which 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/ALOS2 PALSAR is a global 25 m resolution dataset that combines data from many images captured by JAXA’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 25m and is available annually for 2007 to 2010 (ALOS/PALSAR) and 2015 to 2021 (ALOS-2/PALSAR-2). ALOS/ALOS2 PALSAR mosaic data is part of a global dataset provided by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Research Center. The relevant coverage and metadata of ALOS/ALOS2 PALSAR dataset can be viewed on DE Africa Metadata Explorer.
Key Properties
Available Bands
Band ID | Description | Data type | No data |
|---|---|---|---|
HH | HH backscatter | uint16 | 0 |
HV | HV backscatter | uint16 | 0 |
date | Observation date (days since launch) | uint16 | 0 |
linci | Local incidence angle | uint8 | 0 |
mask | Processing mask information | uint8 | 0 |
GeoMAD is\nthe Digital Earth Africa (DE Africa) surface reflectance geomedian and triple\nMedian Absolute Deviation data service. It is a cloud-free composite of\nsatellite data compiled over specific timeframes. This service is ideal for longer-term\ntime series analysis, cloudless imagery and statistical accuracy.
\n\nGeoMAD has\ntwo main components: Geomedian and Median Absolute Deviations (MADs)
\n\nThe\ngeomedian component combines measurements collected over the specified\ntimeframe to produce one representative, multispectral measurement for every\npixel unit of the African continent. The end result is a comprehensive dataset\nthat can be used to generate true-colour images for visual inspection of\nanthropogenic or natural landmarks. The full spectral dataset can be used to\ndevelop more complex algorithms.
\n\nFor each\npixel, invalid data is discarded, and remaining observations are mathematically\nsummarised using the geomedian statistic. Flyover coverage provided by\ncollecting data over a period of time also helps scope intermittently cloudy\nareas.
\n\nVariations\nbetween the geomedian and the individual measurements are captured by the three\nMedian Absolute Deviation (MAD) layers. These are higher-order statistical\nmeasurements calculating variation relative to the geomedian. The MAD layers\ncan be used on their own or together with geomedian to gain insights about the\nland surface and understand change over time.
Key Properties
Band ID | Description | Value range | Data type | No data value |
|---|---|---|---|---|
B02 | Geomedian B02 (Blue) |
|
|
|
B03 | Geomedian B03 (Green) |
|
|
|
B04 | Geomedian B04 (Red) |
|
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|
B05 | Geomedian B05 (Red edge 1) |
|
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|
B06 | Geomedian B06 (Red edge 2) |
|
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B07 | Geomedian B07 (Red edge 3) |
|
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|
B08 | Geomedian B08 (Near infrared (NIR) 1) |
|
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|
B8A | Geomedian B8A (NIR 2) |
|
|
|
B11 | Geomedian B11 (Short-wave infrared (SWIR) 1) |
|
|
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B12 | Geomedian B12 (SWIR 2) |
|
|
|
SMAD | Spectral Median Absolute Deviation |
|
|
|
EMAD | Euclidean Median Absolute Deviation |
|
|
|
BCMAD | Bray-Curtis Median Absolute Deviation |
|
|
|
COUNT | Number of clear observations |
|
|
|
Bands can be subdivided as follows:
\n\nGeoMAD is the\nDigital Earth Africa (DE Africa) surface reflectance geomedian and triple\nMedian Absolute Deviation data service. It is a cloud-free composite of\nsatellite data compiled over specific timeframes. This service is ideal for\nlonger-term time series analysis, cloudless imagery and statistical accuracy.
\n\nGeoMAD has two\nmain components: Geomedian and Median Absolute Deviations (MADs).
\n\nThe geomedian component combines measurements\ncollected over the specified timeframe to produce one representative,\nmultispectral measurement for every pixel unit of the African continent. The\nend result is a comprehensive dataset that can be used to generate true-colour\nimages for visual inspection of anthropogenic or natural landmarks. The full\nspectral dataset can be used to develop more complex algorithms.
\n\nFor each pixel, invalid data is discarded, and\nremaining observations are mathematically summarised using the geomedian\nstatistic. Flyover coverage provided by collecting data over a period of time\nalso helps scope intermittently cloudy areas.
\n\nVariations between the geomedian and the\nindividual measurements are captured by the three Median Absolute Deviation\n(MAD) layers. These are higher-order statistical measurements calculating\nvariation relative to the geomedian. The MAD layers can be used on their own or\ntogether with geomedian to gain insights about the land surface and understand\nchange over time.
Key Properties
Band ID | Description | Value range | Data type | No data value |
|---|---|---|---|---|
SR_B2 | Geomedian SR_B2 (Blue) |
|
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|
SR_B3 | Geomedian SR_B3 (Green) |
|
|
|
SR_B4 | Geomedian SR_B4 (Red) |
|
|
|
SR_B5 | Geomedian SR_B5 (NIR) |
|
|
|
SR_B6 | Geomedian SR_B6 (SWIR 1) |
|
|
|
SR_B7 | Geomedian SR_B7 (SWIR 2) |
|
|
|
SMAD | Spectral Median Absolute Deviation |
|
|
|
EMAD | Euclidean Median Absolute Deviation |
|
|
|
BCMAD | Bray-Curtis Median Absolute Deviation |
|
|
|
COUNT | Number of clear observations |
|
|
|
More details on this dataset can be found here.
Water\nObservations from Space (WOfS) is a service that draws on satellite imagery to\nprovide historical surface water observations of the whole African continent.\nWOfS allows users to understand the location and movement of inland and coastal\nwater present in the African landscape. It shows where water is usually\npresent; where it is seldom observed; and where inundation of the surface has\nbeen observed by satellite.
WOfS annual summary shows the frequency of a pixel being classified as wet over an annual period (calendar year). This is calculated by looking at:
Key Properties
Available Bands
Band ID | Description | Value range | Data type | No data value |
|---|---|---|---|---|
count_wet | How many times a pixel was wet |
|
|
|
count_clear | How many times a pixel was clear |
|
|
|
frequency | Frequency of water detection at a location |
|
|
|
Interpreting WOfS
The WOfS service should be interpreted with caveats in the following situations:
Note that WOfS is not intended for studying ocean. Validation has been centred around inland and near-coastal waterbodies.
\n\nMore details on this dataset can be found here.
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|---|---|---|
Annual coastlines |
| Vector - line |
Rate of change statistics |
| Vector - point |
Coastal change hotspots zoom 1 (15 km) |
| Vector - point |
Coastal change hotspots zoom 2 (5 km) |
| Vector - point |
Coastal change hotspots zoom 3 (1 km) |
| Vector - point |
Building on the data from the Digital Earth Africa Coastlines service, which provides shorelines, rates of change, and coastal hotspots, this African Coastlines Advance and Retreat Dashboard presents a user-friendly visualization of shoreline movement trends from 2000 to 2021. The dashboard highlights significant areas of coastal advance and retreat across all African coastal countries, offering critical insights for coastal managers and policymakers in addressing the challenges of coastal erosion and change.
Further information on the Coastlines Service can be found on the Digital Earth Africa documentation page.
Environmental and development impacts of DE Africa products through diverse, cross-sector uptake and usage.
Improving the lives of Africans through access to tailored information for decision making, from continental down to country and local levels.
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The ability to map shoreline positions for each year provides valuable insights into whether changes to the coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers, and policy makers to assess impact from the range of drivers impacting the coastlines and potentially assist planning and forecasting for future scenarios
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At the continental scale, Africa's water resources are abundant, but issues including resource accessibility and variability lead to large scale water insecurity, impacting 1 in 3 Africans (400 million people) and severely hindering development progress.
Despite these freshwater resources, water shortages are common across the continent.
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Since it was founded in 2019, DE Africa has established strong partnerships with a diverse range of national, regional and international stakeholders and has developed a trusted and increasingly well-known brand. After successful inception and establishment phases, Digital Earth Africa is now entering into a third Scaling Impact Phase (Phase III) with aims to drive uptake and imbed the platform as a self-sustaining public data infrastructure.
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Its coastline is one of the country's greatest natural, economic and cultural resources with invaluable seaside infrastructure and diverse biological resources.
The region is home to over 50% of the country's population as well as various key industries that account for 68% of total GDP, including fishing and tourism.
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