Inversion of soil organic carbon content based on the two-point machine learning method

Abstract

Soil organic carbon (SOC) is vital for the global carbon cycle and environmentally sustainable development. Meanwhile, the fast, convenient remote sensing technology has become one of the notable means to monitor SOC content. Nowadays, limitations are found in the inversion of SOC content with high-precision and complex spatial relationships based on scarce ground sample points. It is restrained by the spatial difference in the relationship between SOC content and remote sensing spectra due to the problem of different spectra for the same substance and the influence of topographic and environment (e.g. vegetation and climate). In this regard, the two-point machine learning (TPML) method, which can overcome above problems and deal with complex spatial heterogeneity of relationships between SOC and remote sensing spectra, is used to invert the SOC content in Hailun County, Heilongjiang Province, combined with derived variables from Sentinel-1, Sentinel-2, topography and environment. Based on 10-fold cross-validation and t-test, results indicate that the TPML method boasts the highest inversion accuracy, followed by random forest, gradient boosting regression tree, partial least squares regression and support vector machine. The average r, MAE, RMSE, and RPD of TPML are 0.854, 0.384 %, 0.558 %, and 1.918. Further, the TPML method has been proven to be equal to evaluating the uncertainty of inversion results, by comparing the actual and theoretical error of the inversion result in one subset. The spatial inversion result of SOC content with 10 m resolution by TPML is smoother and has more real details than other models, which are consistent with the distribution of SOC content in different land use types. This study provides both theoretical and technical guidance for using TPML method combined with spectral information of remote sensing to predict soil attributes and offer accurate uncertainty estimation, thereby opening up the opportunity for low-cost, high-precision, and large-scale SOC inversion.