Perimeter-Area Soil Carbon Index (PASCI): modeling and estimating soil organic carbon using relevant explicatory waveband variables in machine learning environment

Abstract

Soil Organic Carbon (SOC) is the most important indicator of soil health and determines long-term crop productivity. Here, we applied the Random Forest regression model to soil hyperspectral data to determine the important spectral bands and regions for SOC retrieval. Multiple existing studies already identified specific wavelength bands that could be good indicators of SOC. However, there is no hyperspectral-based method that is currently available to simultaneously investigate these identified specific wavelength regions for SOC. To help fill this gap, we developed the Perimeter-Area Soil Carbon Index (PASCI) that utilized optimal SOC spectral bands and then evaluated its robustness for SOC prediction and retrieval against other existing indices. The results of regression analysis between SOC and PASCI values showed a significant relationship (r2 = 0.76; p < 0.05). A significant statistical relationship (r2 = 0.73) was also observed between SOC and the sum indices. The results from this study have advanced our understanding of the optimal spectral bands for SOC. Finally, the PASCI could be applied to hyperspectral and multispectral images to remotely quantify, predict, and map SOC.

Keywords

• Soil carbon index
• hyperspectral index
• random forest
• soil carbon prediction