A novel semi-empirical soil multi-factor radiative transfer model for soil organic matter estimation based on hyperspectral imagery

Abstract

Soil organic matter (SOM) concentration is an important factor affecting soil quality, and rapid and wide-scale monitoring of SOM concentration is a key step toward sustainable agriculture. Hyperspectral technology is widely used in soil composition monitoring, due to its rich spectral information. The complex imaging environment of hyperspectral imagery and the mixed pixel problem have led to the current applications of soil condition estimation mostly using data-driven methods. However, the estimation process based on data-driven methods cannot be explained by radiative transfer theory. Therefore, in this paper, a semi-empirical soil multi-factor radiative transfer (SESMRT) model combining a soil radiative transfer model and data-driven model is proposed for SOM estimation based on hyperspectral imagery. The radiative transfer model in the SESMRT model fully considers the soil components, such as SOM, soil moisture, soil iron oxides, and particle size distribution, and achieves high-precision simulation of soil spectra by resolving the mechanism of the influence of soil components on spectra. The SOM spectra calculated by the radiative transfer model are then used to estimate the SOM concentration based on a data-driven model. The SOM spectra eliminate the interference of other factors in the spectrum and significantly enhance the correlation between the spectrum and SOM. Thus, the SOM concentration can be estimated with a high degree of accuracy (R2 = 0.660 ± 0.034, RMSE = 3.923 ± 0.236, RPIQ = 2.003 ± 0.118 for GF5 and R2 = 0.685 ± 0.030, RMSE = 3.543 ± 0.311, RPIQ = 2.309 ± 0.207 for HyMap). Finally, the comparison of the estimated results for 2017 and 2019 shows that the SOM concentration of the cultivated soils in the study area increased, while the opposite is observed for the soils around the mining areas.