A 30m resolution annual cropland extent dataset of Africa in recent decades of the 21st century

Abstract. Accurate cropland mapping is essential for understanding agricultural dynamics in Africa, a critical global issue with significant implications for the Sustainable Development Goals (e.g., Zero Hunger). Large-scale cropland mapping encounters several challenges, including the varying landscape characteristics of cropland across different regions, extended cultivation periods, and the limited availability of reference data. The study developed a 30-meter resolution African annual cropland distribution (namely AFCD) dataset for Africa spanning the years 2000 to 2022. To extract this large-scale cropland distribution data, we employed random forest classification and Continuous Change Detection algorithms on the Google Earth Engine platform. Robust training samples were generated, and a locally adaptive model was applied for cropland extraction. The final output consists of annual binary Crop/Non-Crop maps from 2000 to 2022. Independent validation samples from numerous third-party sources confirm that the map’s accuracy is 0.86±0.01. A comparison of the cropland area estimates from AFCD with those of the FAO for Africa yielded an R-squared value of 0.86. According to our estimates, Africa’s cropland expanded from 194.35 Mha in 2000 to 210.92 Mha by 2022, marking a net increase of 8.53 %. Prior to 2005, changes in Africa’s cropland area were gradual, but after 2006, there was a marked acceleration in cropland expansion. Despite this continued growth, Africa also experienced significant cropland abandonment. By 2018, abandoned cropland accounted for 11.52 % of the total active cropland area. AFCD also avoided the misclassification of buildings, roads, and trees surrounding cropland, a common issue in LGRIP products. The study further highlights the unique advantage of AFCD in providing a dynamic annual cropland dataset at 30-meter resolution for Africa. This dataset is a crucial resource for understanding the spatial-temporal dynamics of cropland and can support policies on food security and sustainable land management. The cropland dataset is available at https://doi.org/10.5281/zenodo.14920706 (Lou et al., 2025).