Synergistic estimation of soil salinity based on Sentinel-1 image texture and Sentinel-2 salinity spectral indices

Abstract

Soil salinization, one of the important factors leading to global land degradation, seriously affects sustainable agricultural development. Accurate and frequent monitoring of soil salt content (SSC) contributes to the management and restoration of salinized soil. Our study aimed to evaluate the potential to synthetically estimate the salinity in bare soil with different remote sensing sensors (medium-resolution Sentinel-1 and Sentinel-2). The salinity data of 134 surface soil samples were collected in the Shahaoqu irrigation area (SIA) in the Hetao Irrigation District of Inner Mongolia. Simultaneously, the remote sensing images of Sentinel-1 and Sentinel-2 of SIA were obtained. A total of 46 predictors, including 12 remote sensing data (10 multispectral bands, 1 VV, and 1 VH), 8 polarization combination indices, 16 salinity spectral indices, and 10 texture features, were obtained and calculated from the remote sensing image data. Three machine learning algorithms, random forest (RF), support vector machine (SVM), and extreme learning machine (ELM), were used for the construction of soil salinity prediction models based on different combinations of the 46 predictors. The results showed that the remote sensing image data from multiple sensors could be used to estimate soil salinity more accurately than that of a single sensor. Among the three regression models (RF, SVM, and ELM) in our study, RF was the best prediction model (R 2 v ¼ 0.71, RMSE v ¼ 0.140%, and MAE ¼ 0.094%). The RF model combining Sentinel-1 SAR and Sentinel-2 multispectral imagery was the most effective in revealing the spatial distribution of soil salinity. The importance analysis of the predictor variables showed that texture features were the main explanatory variables for SSC prediction, with contrast being the most important predictor. The results of our study verified the potential to predict SSC by combining multisource remote sensing data and machine learning models in arid and semiarid regions, which could provide some reference for soil salinization monitoring and control in the bare soil.

A longitudinal analysis of soil salinity changes using remotely sensed imageries

“…Regression analysis is a commonly used statistical tool to study relationships between factors, making it straightforward to analyze multifactor data 54 . This study employs SVR 44 , 55 – 59 , RF 22 , 58 , 60 – 62 , DT 63 , 64 , and the XGBoost 16 , 65 – 68 methods to investigate the association between remotely sensed imagery data and field soil salinity. The Scikit-learn for Python (version 1.3.0) is used to implement these algorithms 69 .…”