A Hyperspectral Inversion Model of Forest Soil Organic Carbon in Subtropical Red Soil Area Based on Orthogonal Partial Least Square

Abstract

Soil organic carbon (SOC) is a measureable component of soil organic matter, the widely used partial least squares (PLS) have limited ability in screening variables, a large amount of redundancy in soil hyperspectral data leads to the complexity and instability of the inversion model. In this study, the Eucalyptus plantation soil in subtropical red soil area of southern China was analyzed, orthogonal partial least square (OPLS) was applied to construct models, combined with recursive feature elimination (RFE) for bands screening, and the organic carbon content inversion models with full-band, significant-band, and an RFE feature set was established. The results showed that the number of important principal components of the OPLS inversion model was lower than that of PLS, indicating that the addition of orthogonal verification improved accuracy in the selection of independent variables. Using first derivative and logarithmic first derivative transformation can significantly reduce the redundant data and enhance the sensitivity of hyperspectra to SOC. In conclusion, the OPLS method improves the prediction of traditional SOC linear modelling, reduces the number of dependent variables, and the amount of computation during modelling, which significantly improves the accuracy and stability of the established models.