Remote sensing technology provides an efficient and low-cost approach for acquiring large-scale soil information, offering notable advantages for soil organic matter (SOM) mapping. However, in recent years, the bare soil period of cultivated land in Northeast China has significantly shortened, posing serious challenges to traditional SOM prediction and mapping methods that rely on optical imagery. Meanwhile, current approaches that integrate optical imagery, radar imagery, and environmental covariates have yet to fully exploit the potential of remote sensing data in SOM mapping. To address this, this study focuses on the typical black soil region in Northeastern China, acquiring median synthetic images from different time periods (crop sowing, growing, and harvest stages) along with vegetation and radar indices. Six data groups were created by integrating environmental covariate data. Four machine learning models—XGBoost, BRT, ET, and RF—were used to analyze the SOM prediction accuracy of different groups. The group and model with the highest prediction accuracy were selected for SOM mapping in cultivated land. The results show that: (1) in the same model, incorporating radar images and their related indices significantly improves SOM prediction accuracy; (2) when using four machine learning models for SOM prediction, the RF model, which integrates optical images, radar images, vegetation indices, and radar indices from the crop sowing and growing periods, achieves the highest accuracy (R² = 0.530, RMSE = 6.130, MAE = 4.822); (3) in the optimal SOM prediction model, temperature, precipitation, and elevation are relatively more important, with radar indices showing greater importance than vegetation indices; (4) uncertainty analysis and accuracy verification at the raster scale confirm that the SOM mapping results obtained in this study are highly reliable. This study made significant progress in SOM prediction and mapping by employing a radar–optical image fusion strategy combined with crop growth information. It helped address existing research gaps and provided new approaches and technical solutions for remote sensing-based SOM monitoring in regions with short bare soil periods.

Rice trade has played a significant role in ensuring global rice consumption security. However, the spatial coupling of paddy fields based on rice trade can also transfer ecological environmental effects and threaten the sustainability of local systems. As the largest rice producer and consumer in the world, as well as one of the countries with the lowest per capita water resources, studying the inter-provincial rice flow in China and its water resource utilization efficiency is of great significance for ensuring national rice security and water resource security. Research has found that, China's rice consumption decreased by 11.97 Mt from 2000 to 2020, rice supply-demand relationship shifted from a state of shortage to one of surplus. The inter-provincial rice flow in China increased from 17.88 Mt to 46.51 Mt, Huang-Huai-Hai Plain (HHHP) and South China (SC) are the core rice inflow regions, while rice outflow from Northeast China Plain (NECP) continues to increase. The spatial coupled area of paddy fields in China increased from 3.15 Mha to 6.11 Mha. The types and areas of spatial coupling of paddy field between NECP and other agricultural regions showed an increasing trend. Affected by the decrease in per capita rice consumption, the actual and theoretical water consumption of paddy fields decreased by 24.03 billion m3 and 25.22 billion m3. The difference between actual and theoretical water consumption increased from 2.73 billion m3 to 3.92 billion m3, indicating that rice is being transferred from areas with low water resource utilization efficiency to those with high efficiency. The inter-provincial spatial coupling of paddy fields has resulted in an additional water resource consumption of 13.73 billion m3 in NECP.