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.

Mapping the high-precision spatiotemporal dynamics of soil organic carbon (SOC) in croplands is crucial for enhancing soil fertility and carbon sequestration and ensuring food security. We conducted field surveys and collected 1121 soil samples from cropland in Changzhi, northern China, in 2010 and 2020. Random Forest (RF) models combined with 19 environmental covariates were used to map the topsoil (0–20 cm) SOC in 2010 and 2020, and uncertainty maps were used to calculate the dynamic changes in cropland SOC between 2010 and 2020. Finally, RF and Structural Equation Modeling (SEM) were employed to explore the effects of climate, vegetation, topography, soil properties, and agricultural management on SOC variation in croplands. Compared to the prediction model using only natural variables (RF_C), the model incorporating agricultural management (RF_A) significantly improved the simulation accuracy of SOC. The coefficient of determination (R²) increased from 0.77 to 0.85, while the Root Mean Square Error (RMSE) decreased from 1.74 to 1.53 g kg⁻¹, and the Mean Absolute Error (MAE) was reduced from 1.10 to 0.94 g kg⁻¹. The uncertainty in our predictions was low, with an average value of only 0.39–0.66 g kg⁻¹. From 2010 to 2020, SOC in the Changzhi croplands exhibited an overall increasing trend, with an average increase of 1.57 g kg⁻¹. Climate change, agricultural management, and soil properties strongly influence SOC variation. Mean annual precipitation (MAP), drainage condition (DC), and net primary productivity (NPP) were the primary drivers of SOC variability. Our findings highlight the effectiveness of agricultural management for predicting SOC in croplands. Overall, the study confirms that improved agricultural management has great potential to increase soil carbon stocks, which may contribute to sustainable agricultural development.

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.

Quantifying current topsoil organic matter (SOM) differences associated with cropland retirement is challenging because soil observations are spatially uneven and historical SOM maps are scarce. We integrated 10,262 measurements of 0–20 cm SOM collected in 2023 with remote sensing and environmental covariates and annual land-cover histories from 1999 to 2023 in western Shanxi, China. Random forest (RF), support vector regression, and multiple linear regression were compared using five-fold cross-validation. Ten bootstrap RF prediction runs were used to derive a mean SOM map and a pixel-wise standard deviation layer representing between-run prediction variability. RF achieved the best performance (R² = 0.52; RMSE = 5.43 g kg⁻¹; MAE = 3.41 g kg⁻¹). Strict one-to-one matching showed that predicted SOM was 3.084 g kg⁻¹ higher in converted croplands than in cropland reference areas. The mean difference was larger for forestland-covered converted croplands (4.960 g kg⁻¹) than for grassland-covered converted croplands (3.019 g kg⁻¹). Earlier conversion cohorts generally showed larger spatial contrasts. A complementary local comparison yielded a mean difference of 3.004 g kg⁻¹ and revealed substantial spatial heterogeneity. These results represent current predicted spatial differences associated with reconstructed land-cover histories rather than directly measured temporal SOM gains or causal effects of the Grain-for-Green Program.