Co-Production of a 10-m Cropland Extent Map for Continental Africa using Sentinel-2, Cloud Computing, and the Open-Data-Cube

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 recognised 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.