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Accurate maps of rice paddy and cropping intensity at the high spatial resolution are crucial for rice production estimates and food security, yet are inadequate for the entire East and Southeast Asia. Here, we proposed a novel algorithm to map rice paddies and cropping intensity, integrating phenology and machine learning approaches with multi-source remote sensing data. Specifically, by generating random training samples via a buffer approach within X-Means clustering of Sentinel-2 time series, we identified rice paddies and cropping intensity using flooding-transplanting and tillering-heading signals. Multiple Random Forest classifiers were then combined to produce 10-m resolution rice paddy and cropping intensity maps for East and Southeast Asia in 2023. Our rice paddy and cropping intensity maps achieved an overall accuracy of 95% and 91%, respectively, based on 102,075 validation samples collected through field surveys, visual interpretation, and multi-source rice datasets. The mapped rice paddy areas exhibited significant linear correlations with official statistics, with correlation coefficients (r) of 0.95 at both national and provincial scales. Compared to existing rice paddy maps, our method yields superior reliability and accuracy in large-scale rice paddy extraction, effectively reducing commission errors associated with dense, small water bodies.
Jin et al. (Sun,) studied this question.