Research on estimation models of the spectral characteristics of soil organic matter based on the soil particle size

Abstract

Soil organic matter (SOM) is an important part of soil fertility and the main nutrient source for crop growth. The establishment of an effective SOM content estimation model can provide technical support for the improvement of saline soil and the implementation of precision agriculture. In this paper, a laboratory spectrometer was used to measure the spectral reflectance of saline soils with particle sizes of 1 mm, 0.50 mm, 0.25 mm and 0.15 mm collected from Kenli County. After spectral preprocessing and spectral transformation, the characteristic bands of the SOM spectrum were extracted by the successive projections algorithm (SPA). Finally, stepwise multiple linear regression (SMLR), principal component regression (PCR) and partial least squares regression (PLSR) were used to establish SOM content estimation models based on soil particle size. The results showed the following. (i) Soil particle size had a significant impact on soil spectral reflectance. The smaller the soil particle size was, the greater the soil spectral reflectance. (ii) The sensitive bands for SOM were mainly concentrated in the visible light region (400–760 nm). First derivative (FD) transformation can effectively improve the characteristic spectral information obtained from SOM. (iii) Among the three models established with the characteristic bands, the estimation ability of the PLSR model was better than that of the PCR and SMLR models. (iv) The FD of the original spectral reflectance of the 0.25 mm particles combined with the PLSR model gave the best estimation of the SOM content. When the soil particle size was less than 0.25 mm, the estimation results of the model were not improved. These results provide a basis for effective estimation of the SOM content and improvement of saline-alkali soil in Kenli County in the Yellow River Delta.