Paddy Rice Mapping Based on Phenology Matching and Cultivation Pattern Analysis Combining Multi-Source Data in Guangdong, China

Abstract

Paddy rice mapping is crucial for cultivation management, yield estimation, and food security. Guangdong, straddling tropics and subtropics, is a major rice-producing region in China. Mapping paddy rice in Guangdong is essential. However, there are 2 main difficulties in tropical and subtropical paddy rice mapping, including the lack of high-quality optical images and differences in paddy rice planting times. This study proposed a paddy rice mapping framework using phenology matching, integrating Sentinel-1 and Sentinel-2 data to incorporate prior knowledge into the classifiers. The transplanting periods of paddy rice were identified with Sentinel-1 data, and the subsequent 3 months were defined as the growth periods. Features during growth periods obtained by Sentinel-1 and Sentinel-2 were inputted into machine learning classifiers. The classifiers using matched features substantially improved mapping accuracy compared with those using unmatched features, both for early and late rice mapping. The proposed method also improved the accuracy by 6.44% to 16.10% compared with 3 other comparison methods. The model, utilizing matched features, was applied to early and late rice mapping in Guangdong in 2020. Regression results between mapping area and statistical data validate paddy rice mapping credibility. Our analysis revealed that thermal conditions, especially cold severity during growing stages, are the primary determinant of paddy rice phenology. Spatial patterns of paddy rice in Guangdong result from a blend of human and physical factors, with slope and minimum temperature emerging as the most important limitations. These findings enhance our understanding of rice ecosystems’ dynamics, offering insights for formulating relevant agricultural policies.