Large underestimation of smallholder agriculture in the tropics by classic remote sensing products: evidenced from the Kasaï River Basin

The Kasa & iuml; River Basin (KRB), the largest tributary system of the Congo basin, is undergoing rapid land-use transformation driven by population growth and expanding economic activities. However, existing global and regional land-cover products do not fully capture this highly heterogeneous, smallholder-dominated landscape or its temporal dynamics. Here, we develop a regionally calibrated 30 m resolution land-cover dataset for the KRB for 2017-2024 by combining 3,734 reference observations across five classes (urban, water, forest, savanna/grassland, cropland) with a random forest classifier trained on multi-source Earth observation datasets encoded as embeddings by Google's AlphaEarth Foundations. The resulting classification achieves 96%-100% accuracy for urban and water classes and 74%-87% for forest, savanna/grassland, and cropland classes, substantially outperforming current global products (30%-65%). This improvement results from using a large region-specific training dataset and a modeling strategy designed to capture land surface phenology; this enhances the detection of inter-annual dynamics in rotational and intermittent smallholder agriculture, characteristic for many tropical regions such as the KRB. We estimate that in 2024 cropland covered 16.2% of the KRB, while forest and savanna/grassland occupied 41.1% and 41.5%, respectively-values that contrast sharply with those from existing products, which report cropland extents of only 0.1%-4.7%. Transition analysis further reveals that over 53 128 km2 of forest and grassland in the KRB have been converted to cropland between 2017 and 2024. These results highlight the need for regionally calibrated land-cover products in heterogeneous tropical landscapes. By resolving the systematic biases present in existing global products, the dataset produced here provides an accurate, high-resolution benchmark that underpins environmental modeling, food security analysis, and land-use decision-making in the KRB and other tropical regions.