Crop Statistic to Annual Map: 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. 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 map

SUBMITTER: Li X 

PROVIDER: S-EPMC12267438 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature