Work

Semi-automatic paddy rice intensity mapping for southern China with multiple cropping systems

Year: 2025

Type: article

Abstract: Accurate paddy rice cropping intensity (PRCI) information is crucial for yield estimation, food policy formulation, and water resources management. However, the high cultivation intensity, fragmented farmland, complex crop rotation systems, and overlapping crop growth stages in southern China make it challenging to differentiate between rice varieties and other crops. To address this problem, we propose a semi-automatic framework for mapping PRCI in southern China. This framework first fused MODIS and Sentinel-2 data to generate high-spatiotemporal-resolution vegetation indices, forming the data foundation to identify the key phenological stages of paddy rice. Then, an automated division approach is designed to divide crop-growing seasons. Based on that, we developed a discriminant feature set for rice and identified potential rice-planting (PCP) regions using an unsupervised Gaussian mixture model (GMM). Finally, we obtained reliable 10-m PRCI maps from PCP regions by integrating statistical data, Sentinel-1 data, limited rice samples, and the time-weighted dynamic time warping (TWDTW) algorithm. Experimental results showed that our framework consistently achieved a high mapping accuracy of 85% across various regions with diverse landforms and agricultural practices. Furthermore, the framework showed a strong correlation with municipal-level statistical data (R2 = 0.99). Our experimental results demonstrate its adaptability under complex landscapes and crop rotation systems, indicating significant potential for broader application.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104862