Identifying and quantifying local uncertainty and discrepancy in the comparison of global cropland extent through a synergistic approach

Abstract

Spatiotemporally consistent information on global cropland extent is essential for resource management and scientific research. Multiple cropland datasets derived from remotely sensed products are currently available. However, significant discrepancies and uncertainties among them lead to acreage estimates that diverge considerably from official statistics, thus constraining their applicability. To this end, a new stratified optimization synergistic approach (SOSA) is here proposed to create hybrid cropland maps of China, circa 2000–2020, by fusing five existing land cover maps (i.e., CLCD, GLC_FCS30, Globeland30, GlobalCrop, and ESA_CCI), and sub-national statistics. Given the underlying challenges associated with cost-effective large-scale cropland mapping, this approach seeks to strike a balance between data value, veracity, and affordability. SOSA streamlines the commonly used protocol procedure for determining the optimal agreement level and the best product combination. Preliminary validation of the resulting cropland maps was performed, and the evaluation demonstrated that the synergy cropland map exhibited greater spatial accuracy and closer agreement with statistics compared to any individual input map. This hints that synergistic approaches can bolster cropland mapping performance and amplify consistency with statistical data. Our results are expected to serve as a valuable reference for data users, aid in the future improvement of cropland mapping to support forward-thinking applications, and enhance our understanding and modeling of agricultural systems worldwide.