Crop Statistic to Annual Map: Tracking spatiotemporal dynamics of crop-specific areas in Africa, China and USA

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.

Data files are named according to the format ‘[Region]_[year].tif’, each data file has a certain number of bands which correspond to the crop types and respectively named as the short names of crop types (refer to Supplementary Table for the whole crop categories). Datasets are provided in a Geotiff format and in ESPG: 4326 (GCS_WGS_1984) spatial reference system at the spatial resolution of 10 km. Crop-specifics areas are measured in hectares.