Tracking and analyzing the spatio-temporal changes of rice planting structure in Poyang Lake using multi-model fusion method with sentinel-2 multi temporal data

Accurate and efficient extraction of rice planting structures, coupled with comprehensive analysis of their spatiotemporal dynamics and driving factors, is crucial for rice yield estimation and optimized water resource management in the Poyang Lake region. However, traditional approaches face significant limitations: single machine learning models often yield insufficient classification accuracy, while existing fusion models typically involve complex processing workflows and exhibit low computational efficiency. To address these challenges, this study developed an efficient and simplified fusion model based on a scoring strategy to determine rice planting structures from 2018 to 2023, followed by an in-depth analysis of their spatiotemporal patterns and underlying drivers. The evaluation results demonstrated that four individual classification models of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Decision Tree (GBDT) achieved Overall Accuracy of 85.29%–90.07%, Kappa coefficients of 0.786–0.855, User Accuracy of 80.51%–93.02%, and Mapping Accuracy of 80.87%–92.63%. The proposed scoring-based fusion model significantly enhanced these metrics, improving Overall Accuracy by 3.36%–9.16%, Kappa coefficient by 5.15%–14.38%, User Accuracy by 0.37%–11.13%, and Mapping Accuracy by 0.48%–10.71%. Spatiotemporal analysis revealed distinct trends in rice cultivation patterns: single-cropping rice and regenerated rice showed consistent expansion, both in planting area and proportion, with a spatial tendency towards flat regions. Conversely, double-cropping rice exhibited a gradual decline, with its cultivation areas contracting towards the central lake region. These shifts were primarily driven by socioeconomic factors, particularly rural labor migration and rising fertilizer prices, which have incentivized farmers to adopt less labor-intensive and lower-input cultivation systems, such as single-cropping and regenerated rice. The findings offer a novel methodological framework for precise extraction of crop planting structures, and a scientific foundation for local governments to develop targeted water resource management strategies.