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Using Heshan Farm, a representative black soil region, as a case study, this study explores the variability in optical and radar remote sensing signals in response to extreme variations in hydrothermal regimes across different climatic years. A multisource integration framework combining optimal bare-soil period imagery, multitemporal growing-season data, and stable environmental covariates was developed to improve the accuracy and stability of SOM retrieval. Using optical and radar remote sensing data from 2019–2021, three strategies were compared: bare-soil imagery, multitemporal fusion, and the integration of environmental covariates. The results showed that the optimal observation period differed by year, with the highest accuracy achieved in June 2020 ( R 2  = 0.660, RMSE = 6.003 g/kg). The integration of growing-season imagery (primarily September) improved the performance with R 2 increasing by 0.030 and RMSE decreasing by 0.286 g/kg. Further inclusion of topographic, climatic, and soil physicochemical variables yielded maximum accuracy ( R 2  = 0.708, RMSE = 5.577 g/kg). Spatial analysis revealed elevated SOM concentrations in low-lying southern areas, whereas uncertainty analysis identified 2021 as the most stable prediction year (average uncertainty = 2.67 g/kg). Overall, the integration of multitemporal remote sensing data with environmental covariates substantially improved the precision and robustness of SOM spatial prediction.

international journal of digital earth

Quantitative estimation of soil organic matter content in black soil region under varying climatic conditions using optical radar remote sensing technology