Assessing factors impacting the spatial discrepancy of remote sensing based cropland products: A case study in Africa

•Spatial agreement among four remote-sensing-based landcover & cropland products was measured over Africa.•The Sahel zone and the West African coast are the two regions in Africa with high spatial disagreement and low cropland mapping accuracy.•The impact of four environmental factors (elevation dispersion, field size, land-cover richness and cloud frequency) was assessed.•Land-cover richness is the driving factor with the largest contribution to the spatial disagreement among cropland products over Africa.•The frequent clouds and fragmentation of agriculture fields limit the accurate mapping of cropland over The Sahel zone and the West African coast. Many African countries are facing increasing risks of food insecurity due to rising populations. Accurate and timely information on the spatial distribution of cropland is critical for the effective management of crop production and yield forecast. Most recent cropland products (2015 and 2016) derived from multi-source remote sensing data are available for public use. However, discrepancies exist among these cropland products, and the level of discrepancy is particularly high in several Africa regions. The overall goal of this study was to identify and assess the driving factors contributing to the spatial discrepancies among four cropland products derived from remotely sensed data. A novel approach was proposed to evaluate the spatial agreement of these cropland products and assess the impact of environmental factors such as elevation dispersion, field size, land-cover richness and frequency of cloud cover on these spatial differences. Results from this study show that the overall accuracies of the four cropland products are below 65%. In particular, large disagreements are seen on datasets covering Sahel zone and along the West African coasts. This study has identified land-cover richness as the driving factor with the largest contribution to the spatial disagreement among cropland products over Africa, followed by the high frequency of cloud cover, small and fragmented field size, and elevation complexity. To improve the accuracy of future cropland products for African regions, the data producers are encouraged to take a multi-classification approach and incorporate multi-sensors into their cropland mapping processes.