High-precision mapping of soil organic matter considering annual climate variability and multidimensional environmental factors

Le résumé fourni par la source

Accurate mapping of soil organic matter (SOM) is critical for sustainable land management and climate change assessment. Taking Youyi Farm in Northeast China—located within one of the world's four major black soil regions—as a case study, 188 soil samples were collected and analyzed to construct a high-quality SOM dataset. Based on Sentinel-2 imagery, a multi-temporal remote sensing dataset representing three climatically contrasting years (2019 flood, 2020 normal, and 2021 drought) was developed, integrating spectral bands, vegetation indices, and environmental covariates. Four machine learning models—Random Forest (RF), Gradient Boosting Decision Trees (GBDT), eXtreme Gradient Boosting (XGBoost), and Particle Swarm Optimization–Support Vector Regression (PSO-SVR)—were systematically compared. PSO-SVR achieved the best performance using bare-soil spectral information alone (R 2 =0.544, RMSE=1.159%). Incorporating growing-season vegetation indices (NDVI, EVI, LSWI) improved R 2 by 2.0%–5.5% relative to the bare-soil baseline depending on the climatic year, and further integrating 16 environmental covariates increased R 2 to 0.617 in the drought year—a 13.4% relative improvement. SHAP analysis identified channel network base level, the green band (B3), and mean annual temperature as the dominant predictors, revealing how topographic drainage, spectral reflectance, and temperature-dependent microbial processes jointly control SOM spatial variability. Model accuracy followed the order drought > normal > flood year, providing practical guidance for selecting optimal remote sensing acquisition periods under different climatic conditions. Overall, explicitly accounting for interannual climatic variability substantially enhances the accuracy and robustness of SOM prediction in black soil regions.

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