Earth observation of waterbodies through time is a powerful tool in understanding both the location of waterbodies and their temporal dynamics. Water Observations from Space (WOfS), developed and well-tested in Australia, is a service providing historical surface water observations derived from Landsat satellite imagery from 1987 to present day. WOfS provides better understanding of where water is usually present; where it is seldom observed; and where inundation of the surface has been occasionally observed by satellite. We applied the WOfS algorithm to Africa and validated its accuracy through image interpretation of satellite and aerial imagery using an online tool created by the NASA Servir program, Collect Earth Online. The Digital Earth Africa Product Development Task Team, composed of four regional geospatial organisations RCMRD, AfriGIST, AGRHYMET and OSS, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. In order to understand the accuracy and bias of the WOfS algorithm in Africa at both the continental-scale and regional zones, we generated 2900 sample points covering the continent including the main islands and distributed them into 7 Agro-ecological zones. We assessed whether the point was flooded, dry, or cloud covered, for 12 months in 2018, resulting in 34,800 assessed observations. As water information is available through WOfS in near real-time, it can be used for environmental monitoring, flood mapping, monitoring planned water releases, and management of water resources in highly regulated systems. WOfS is expected to be used by ministries and state departments of agriculture and water management in countries, international organizations, academia and the private sector.

A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current continental cropland extent maps of Africa are either inaccurate, have too coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland extent map for the African continent is therefore recognized as a gap in the current crop monitoring services. Using Digital Earth Africa’s Open Data Cube platform, and working in conjunction with multiple regional African geospatial institutions, we co-develop a 10 metre resolution cropland extent map over the African continent using a Random Forest machine learning classifier and an annual time-series of Sentinel-2 satellite images. Members of the regional African geospatial institutions (RCMRD, OSS, Afrigist, AGRHYMET, and NADMO) were instrumental in defining the specifications of the product, in developing and implementing a continental scale reference data collection strategy, and assisted with iterative model building. The cropland extent map comes packaged with three layers: a pixel-based classification, a pixel-based cropland probability layer, and an object-based segmentation filtered classification. All the components of Digital Earth Africa’s cropland extent map: models, reference data, code, and results are open source and freely available online through Digital Earth Africa’s mapping and analysis platforms. A fuller description of the dataset, including methods, the validation results, and how to access the different datasets can be seen on the DE Africa user guide: https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.html