Tracking spatiotemporal dynamics of crop-specific areas through machine learning and statistics disaggregating

Abstract. Mapping spatiotemporal dynamics of crop-specific areas is of great significance in addressing challenges faced by agricultural systems. But comparable multi-phase crop maps in year series have not yet been developed in most regions of the global. In this study, we developed a framework for updating annual crop-specific area maps at 10 km resolution based on crop statistics disaggregating, multi-source data integrating and machine learning, taking factors related with crop distribution in different regions and complex agricultural systems into accounts. Experiments were conducted in three study areas (Africa, China, and USA) respectively corresponding to three conditions of the information coverage of crop distribution (low, median, and high). In our framework, we collected related spatial indicator used in previous studies and trained random forest regression models to predict spatiotemporal dynamics of crop-specific areas based on them. Annual crop statistics were further disaggregated based on probabilistic layer and harmonized based on multiple constraints. Our framework is a good attempt to integrate two strategies (top-down and bottom-up), creating more possibility for crop mapping to integrate statistic with remote sensing. Finally, our results include maps of crop-specific areas covering 42 types from 1961–2022 in Africa, maps of crop-specific areas covering 14 types from 1980–2022 in China and maps of crop-specific areas covering 15 types from 2008–2022 in USA. Results show that our products has a relatively good consistency with independent reference map or statistics. Our products provide approximate estimates for spatiotemporal dynamics of crop-specific areas in multiple regions over several decades, which could be used as data basis for food security and environmental impact assessments.