Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency.

This study combined field survey data with Gaofen 5 (GF-5) satellite hyperspectral images of the Yuncheng Basin (China), considering 15 environmental variables. Random forest (RF) was used to select the optimal satellite hyperspectral model, sequentially introducing natural and farmland management factors into the model to analyze the spatial distribution of farmland soil organic carbon (SOC). Furthermore, RF factorial experiments determined the contributions of farmland management, climate, vegetation, soil, and topography to the SOC. Structural equation modeling (SEM) elucidated the driving mechanisms of SOC variations. Integrating satellite hyperspectral data and environmental variables improved the prediction accuracy and SOC-mapping precision of the model. The integration of natural variables significantly improved the RF model performance (R² = 0.78). The prediction accuracy enhanced with the introduction of crop phenology (R² = 0.81) and farmland management factors (R² = 0.87). The model that incorporated all 15 variables demonstrated the highest prediction accuracy (R² = 0.89) and greatest spatial SOC variability, with minimal uncertainty. Farmland management activities exerted the strongest influence on SOC (0.38). The proposed method can support future investigations on soil carbon sequestration processes in river basins worldwide.