Mapping and monitoring of soil organic carbon using regression analysis of spectral indices

The soil carbon sinking ability is dominantly controlled by local topographical settings, soil-crop management and traditional farming practices on which the food demand of the major population is dependent. The degradation of natural resources causing poor soil health is likely to strain the hilly and mountain ecosystem. This study aims to map soil organic carbon (SOC) of rice-fallow system under varying slopes and its changes during the past 20 years under traditional management practice using geospatial tools and techniques. Regres-sion models of SOC were derived from remote sensing (RS)-based indices using multiple linear regression -stepwise (MLR-stepwise), partial least square regression (PLSR) and principal component analysis-regression (PCA-R). The MLR-stepwise model was found to be superior in performance with high R2 (0.87) and least RMSE (0.026) compared to PLSR (R2 = 0.71 and RMSE = 0.05) and PCA-R (R2 = 0.27 and RMSE = 0.11) models for SOC prediction.

Automated summary

The soil carbon sinking ability is mainly controlled by factors such as topography, soil-crop management, and traditional farming practices, which are also related to the food demand of the population. The degradation of natural resources leading to poor soil health is likely to put strain on hilly and mountain ecosystems. This study utilizes geospatial tools and techniques to map the distribution of soil organic carbon (SOC) in rice-fallow systems with varying slopes and investigate its changes over the past 20 years under traditional management practices. Regression models of SOC were developed using remote sensing (RS)-based indices, with the MLR-stepwise model performing the best in terms of SOC prediction.