Extracting cropping patterns from remotely sensed images in a mixed single-, double-, and triple-cropping region of...

Climate change and population growth present significant challenges to global food security, underscoring the critical importance of sustainable and efficient agricultural production. Crop rotation is a key agricultural practice that enhances food production, improves soil fertility, reduces pest and disease pressure, and maintains agro-ecological balance. The complexity and diversity of cropping patterns, particularly in the fragmented farmland of southern China, limit the availability of high-resolution crop rotation maps in precision agriculture. To improve the consistency between cropping intensity (CI) estimation and crop pattern (CP) mapping, this study developed a hierarchical framework for extracting cropland, CI, and CP from remotely sensed images. Using the Google Earth Engine (GEE) platform, a 10-m binary cropland/non-cropland map was first generated from the time-series Normalized Difference Vegetation Index (NDVI). Then, CI was derived within cropland regions using an intelligent algorithm that counts the number of growth cycles. Finally, taking advantage of crop phenology and CI constraints, nine cropping patterns were extracted from a diversified cropping region. Comparing with field survey data, the results revealed overall accuracies of 98.97%, 96.47%, and 87.92% for the cropland/non-cropland map, cropping intensity map, and cropping pattern map, respectively. These findings demonstrate the reliability of the generated maps and the potential of the proposed framework for revealing diverse cropping patterns in complex cropping regions.