Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network

Abstract

Salinization of soil is one of the most important environmental issues in arid and semi-arid areas. Accordingly, agricultural production and ecological development have been profoundly influenced in these regions. Therefore, it is becoming increasingly important to assess soil salinization and its driving factors. However, soil salinity is difficult to accurately characterize by using single-factor and linear models. Thus, it is necessary to develop a robust modeling technique by integrating multiple biophysical indicators to quantitatively monitor soil salinity. In this paper, the Support Vector Machine (SVM) regression algorithm and Artificial Neural Network (ANN) algorithm were employed to better estimate the soil salinity in the Yanqi Basin, Xinjiang, China. The soil backscattering coefficient (σsoil0), Groundwater Depth (GD), Salinity Index (SI) and Surface Evapotranspiration (SET) were used as model parameters. σsoil0 was obtained from Sentinel-1A Synthetic Aperture Radar (SAR) data; GD and SI were calculated from Landsat-8 imagery; and SET was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) evapotranspiration product (MOD16). The performances of SVM and ANN in evaluating the nonlinear relationship between σsoil0, GD, SI, SET, and soil Electrical Conductivity (EC) were compared. The results showed that, the SVM regression algorithm performs better than ANN algorithm in monitoring soil salinity. The Root Mean Square Error (RMSE) and the coefficient of determination (R2) in the estimation obtained using the SVM regression algorithm were about 2.01 and 0.82, respectively, versus the training data set; the RMSE and R2 were 1.36 and 0.88, respectively, versus the testing data set. The accuracy was significantly higher than that using the ANN algorithm, which obtained an RMSE of 2.20 and R2 of 0.79 versus the training data set, and 2.25 and 0.68 versus the testing data set. The results of this study indicated that about 56.82% of the soil in the study area was affected by different degrees of salinity. It is obvious that SVM regression algorithm has great potential for estimating soil salinity using multi-source remote sensing data.