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Spectral techniques play a key role in the estimation of large-scale soil parameters, but most of the current estimation models are data-driven models lacking a physical basis. Therefore, in this study, we developed a novel radiative transfer model named the soil multifactor radiative transfer (SMRT) model that predicts the soil spectra as a function of the soil organic matter (SOM), particle size distribution (PSD), and iron oxide content. The SMRT model explains the soil spectral features commonly used in the data-driven estimation models and can be used to explore the mechanisms by which soil-related parameters affect soil spectra according to the model parameters. A total of 79 soil profile datasets were used in the experiments conducted in this study. The reflectance of the soil after air-drying can ignore the effect of moisture, thereby ensuring that the main influences on the soil spectra are the SOM, iron oxide content, and PSD. The SMRT model performed very well in the spectral simulation (R2 = 0.9681, RMSE = 0.0266, MAE = 0.0160). The absorption and scattering coefficients of the SMRT model explain the radiative transfer mechanism for the color representation of black soil and laterite soil. The spectral features around 1910 nm and 2210 nm are caused by not only the soil moisture (SM) and clay mineral content, but several other factors, including the SOM, iron oxide content, and PSD. Overall, these findings show that the SMRT model has a superior ability to describe the soil radiative transfer processes. However, as the coefficients in the SMRT model are dependent on the soil properties, the coefficients should be calibrated by an optimization algorithm and cannot be constants, which leads to the requirement for soil samples to calibrate the coefficients in practical applications. |
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