Crop Statistic to Annual Map: Tracking spatiotemporal dynamics of crop-specific areas through machine learning and statistics disaggregating

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 10km resolution based on crop statistics disaggregating, multi-source data integrating and machine learning. 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. 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 have a reasonable level of consistency with independent reference map or statistics. Our products could be used as data basis for food security and environmental impact assessments.