Soil organic matter (SOM) is a key indicator of cropland quality and carbon cycling. Accurate SOM mapping is essential for sustainable soil management and carbon sink assessment. This study investigated the effects of interannual climatic variability on SOM prediction using remote sensing and machine learning. Youyi Farm in the Sanjiang Plain, Heilongjiang Province, was selected as the study area, covering three representative years: 2019 (flood), 2020 (normal), and 2021 (drought). Based on multi-temporal Sentinel-2 imagery and environmental covariates, Random Forest models were used to evaluate single- and dual-period combinations. Results showed that combining bare-soil and crop-season images consistently improved accuracy, with optimal combinations varying by year (R² = 0.544–0.609). Incorporating temperature, precipitation, and elevation enhanced model performance, particularly temperature, which contributed most to prediction accuracy. Feature selection further improved model stability and generalization. Spatially, SOM showed a pattern of higher values in the northeast and lower in the central region, shaped by topography and cultivation. This study innovatively integrates interannual climatic variability with remote sensing temporal combination and feature selection, constructing a climate-adaptive SOM mapping framework and providing new insights for accurate inversion of cropland SOM under extreme climates, highlights the importance of multi-temporal imagery, environmental factors, and feature selection for robust SOM mapping under different climatic conditions, providing technical support for long-term cropland quality monitoring.

The accurate mapping of soil texture, a key determinant of soil’s hydrological and nutritional behavior, is essential for agricultural drought assessment, yet the application of multi-temporal satellite data for this purpose remains largely unexplored. In this study, we first identified the optimal prediction period by evaluating the performance of single-date imagery (satellite images captured on individual observation dates). Subsequently, dual-phase imagery (DPI) was developed to increase mapping accuracy. Finally, these refined predictions quantified soil texture’s response to drought and its corresponding thresholds. Results demonstrated that: (1) the bare soil period in April provided peak prediction accuracy for all texture fractions (Sand: R² = 0.617, RMSE = 10.21%; Silt: R² = 0.606, RMSE = 8.648%; Clay: R² = 0.604, RMSE = 1.945%); (2) Significant accuracy gain from DPI using April-August imagery fusion (Sand: R² = 0.677, RMSE = 9.386%; Silt: R² = 0.660, RMSE = 8.034%; Clay: R² = 0.658, RMSE = 1.807%); (3) sand content was the most critical factor influencing crop drought stress, with a threshold of 31%. By integrating multi-temporal satellite observations with quantitative drought evaluation for high-resolution soil texture mapping and precision agricultural management in Northeast China’s black soil region.

Ensuring stable soybean supply requires understanding how production patterns evolve and whether policy consistently dominates production dynamics. This study examines 36 prefecture-level units in Northeast China from 2000 to 2024 by integrating production growth decomposition, the production concentration index, center-of-gravity and standard deviational ellipse models, spatial autocorrelation (Moran’s I), and the geographical detector. The results indicate the following: (1) Production fluctuated upward, with its spatial core consolidating in the northern high-latitude zone and the regional centroid moving 123.3 km northeastward overall. Concentration increased more than sown-area concentration, indicating a gradual, spatially uneven recovery in yield contributions rather than a completed shift to yield-led growth. (2) Spatial differentiation was fundamentally governed by hydrothermal endowments rather than monotonic policy dominance. Annual mean temperature consistently exhibited the strongest independent explanatory power (reaching q = 0.39 in 2024), followed by agricultural machinery power (q = 0.22) and fertilizer input (q = 0.24). (3) The influence of agricultural fiscal support intensity was stage-specific and conditional (significant in 2010 and 2020, but waning to q = 0.14 in 2024), functioning primarily through synergistic interactions with natural endowments and input levels. Consequently, regional soybean layout reflects a multifactor interplay where climatic suitability sets the production ceiling, while factor configuration and targeted fiscal incentives regulate localized capacity. Policy interventions should shift from uniform area expansion subsidies toward differentiated, climate-resilient input optimization in core and marginal regions.