Unpacking Smallholder Rainfed and Floodplain Cropland in sub-Saharan African Drylands: A Multi-Sensor Mapping Approach

Agriculture in the tropics is often challenged by low productivity, climate change and growing food demands. In the mostly rainfed systems of the semi-arid tropics, floodplain croplands enable year-round crop production and allow the cultivation of marketable crops, but the extent and spatial distribution of floodplain croplands remain largely unknown. We tested combinations of Sentinel-2, Sentinel-1, and PlanetScope time series to distinguish floodplain and rainfed cropland across Nampula Province (~80,000 km²) in Mozambique. We used a Random Forest classifier paired with post-processing based on class probabilities, elevation, and hydrological data, to reduce map errors. Time series data from multi-sensor constellations with post-processing delivered more accurate results compared to a single- or double-sensor approach, with overall accuracies of up to 88.4% ± 0.8% and class-specific user and producer accuracies of up to 53.4% ± 4.4% and 71.5% ± 7.8%, respectively, for floodplain cropland and 73.4% ± 2.2% and 92.7% ± 1.2% for rainfed cropland. While S2 features yielded the highest single-sensor accuracy, integrating S1 and PS data improved results for both classes, underlining the value of multi-sensor approaches. Our study revealed that 92.6% of the cropland in the region is rainfed (2,105,000 ha ± 64,000 ha), while 7.4% is floodplain cropland (about 167,000 ha ± 22,000 ha), concentrated in suburban regions. The final map revealed a fragmented cropland pattern between rainfed and floodplain fields, which is driven by socio-environmental dynamics. Our proposed multi-sensor mapping workflow contributes to a more nuanced understanding of the tropical dryland agriculture system.