Research on Hyperspectral Inversion of Soil Organic Carbon in Agricultural Fields of the Southern Shaanxi Mountain Area

Rapidly obtaining information on the content and spatial distribution of soil organic carbon (SOC) in farmland is crucial for evaluating regional soil quality, land degradation, and crop yield. This study focuses on mountain soils in various crop cultivation areas in Shangzhou District, Shangluo City, Southern Shaanxi, utilizing ZY1-02D hyperspectral satellite imagery, field-measured hyperspectral data, and field sampling data to achieve precise inversion and spatial mapping of the SOC content. First, to address spectral bias caused by environmental factors, the Spectral Space Transformation (SST) algorithm was employed to establish a transfer relationship between measured and satellite image spectra, enabling systematic correction of the image spectra. Subsequently, multiple spectral transformation methods, including continuous wavelet transform (CWT), reciprocal, first-order derivative, second-order derivative, and continuum removal, were applied to the corrected spectral data to enhance their spectral response characteristics. For feature band selection, three methods were utilized: Variable Importance Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Stepwise Projection Algorithm (SPA). SOC content prediction was conducted using three models: partial least squares regression (PLSR), stepwise multiple linear regression (Step-MLR), and random forest (RF). Finally, leave-one-out cross-validation was employed to optimize the L4-CARS-RF model, which was selected for SOC spatial distribution mapping. The model achieved a coefficient of determination (R2) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg&minus:1, and a mean absolute error (MAE) of 1.37 g kg&minus:1. The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy: (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy: and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. These findings provide scientific methods and technical support for SOC monitoring and management in mountainous areas and offer valuable insights for assessing the long-term impacts of different crops on soil ecosystems.