Soil Salinity Estimation based on Sentinel-1/2 Texture Features and Machine Learning

Soil salinization is a vital factor in global land degradation, seriously affecting sustainable agricultural development. Efficient monitoring of soil salinity using satellite remote sensing is critical for saline soil management. Currently, research on soil salinity extraction using satellite remote sensing primarily relies on the spectral information of remote sensing images, but insufficient consideration was given to the texture features of the imagery and the integration of spectral and texture information.To fully explore the effectiveness of texture features and the integration of texture features and spectral information in soil salinity estimation, experiments were conducted in Shahaoqu Irrigation Area, Inner Monglia, China from April through August, 2019. For this end, the experiments utilized measured soil salinity data and the textural and spectral data from Sentinel-1/2. The effectiveness of Sentinel-1/2 texture features in soil salinity estimation was esamined using Out of Bag score, and soil salinity inversion models were constructed based on the texture features, spectral information and 4 machine learning models(Random Forest, Cubist, Support Vector Machines, Back Propagation). The results indicated that Sentinel-1 texture features were more sensible to bare soil salinity (the top four most sensible texture featrues were HOM, ENT, COR, CON) while Sentinel-2 texture features were more sensible to vegetated soil salinity (the top four were VAR, CON, HOM, ENT). Additionally, when combining texture features and spectral information, the models exhibited improved performance, and Random Forest had the best performance in both bare soil (R ² =0.688, RMSE=0.207) and vegetated soil (R ² =0.494, RMSE=0.304).