Abstract
Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient (R2) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.
Highlights
Salt-affected soil is a general term that refers to saline soil and alkaline soil
After the transformation of the raw spectral curve in four forms, four spectral curves were drawn for each spectral form, each curve representing different soil salt content (Figure 5)
When the first derivative (FD) spectrum curve is near 600 nm, the higher the soil salt content, the larger the value
Summary
Salt-affected soil is a general term that refers to saline soil and alkaline soil. The content of soluble salt substances in saline soil typically exceeds 2 g/kg, which affects the normal development of crops. Salt soil and alkaline soil are mixed; they are collectively referred to as salt-affected soil. The total area of the salt-affected soil resources in China is approximately 9.9 million km, which are mainly distributed in the northeast plain, the arid and semi-arid areas in the northwest, the Huang-Huai-Hai plain, and the eastern coastal areas. The arid area in the northwest is China’s largest salt-affected soil distribution area, with a total area of approximately 1.3 million km, which includes Qinghai, Xinjiang, western Inner Mongolia, Gansu Hexi Corridor, and northern Ningxia
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