Laboratory Research on Polarized Optical Properties of Saline-Alkaline Soil Based on Semi-Empirical Models and Machine Learning Methods

Abstract

Currently, soil salinization is a serious problem affecting agricultural production and human settlements. Remote sensing techniques have the advantages of a large monitoring range, rapid acquisition of information, implementation of dynamic monitoring, and low impact on the ground surface. Over the past two decades, many semi-empirical bidirectional polarized distribution function (BPDF) models have been proposed to accurately calculate the polarized reflectance (Rp) on the soil surface. Although there have been some studies on the BPDF model based on traditional machine learning methods, there is a lack of research on the BPDF model based on deep learning, especially using laboratory measurement spectrum data as the processing object, with limited research results. In this paper, we collected saline-alkaline soil in the field as the observation object and measured the Rp at multiple angles in the laboratory environment. We used semi-empirical models (the Nadal–Bréon model, Litvinov model, and Xie–Cheng model) and machine learning methods (support vector regression, random forest, and deep neural networks regression) to simulate and predict the surface Rp of saline-alkaline soils and compare them with experimental results. The measured values of the laboratory are compared and fitted, and the root mean squared error, R-squared, and correlation coefficient are calculated to express the prediction effect. The results show that the predictions of the BPDF model based on machine learning methods are generally better than those of the semi-empirical BPDF model, which is improved by 3.06% at 670 nm and 19.75% at 865 nm. The results of this study also provide new ideas and methods based on deep learning for the prediction of Rp on the surface of saline-alkaline soils.