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Salinisation of agricultural soils poses an important constraint to sustainable agricultural development, especially in the Huinong region of Ningxia, northwestern China, where salinised soils are widely distributed. However, due to the limitations of monitoring technology, the detailed situation of soil salinisation in this region is not known. Currently, the multispectral instrument (MSI) on board the Sentinel-2 satellite provides a good opportunity for monitoring soil salinity dynamics. Therefore, in this study, the feasibility of using the multispectral instrument (MSI) on board the Sentinel-2 satellite, combined with a machine learning model, to accurately monitor the soil salinity content during the spring and summer seasons was explored. And three additional red-edge bands (B5-B7) were used instead of the traditional red band (B4) to generate potential soil salinity indices. A screening method based on the PLS-VIP criterion was used to screen the spectral covariates, and three machine learning methods, namely, Random Forest (RF), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), were employed to build the inverse model of soil salt content. The results showed that the Random Forest model based on Sentinel-2 imagery performed the best in the inversion with good prediction results, with R2 and RE of 0.825 and 0.207 and 0.711 and 0.271 in spring and summer, respectively.The study also revealed that soil salinity varied significantly between seasons, and was higher in spring than in summer. This result is of great significance as a guide for soil salinity monitoring and land reclamation in arid or semi-arid regions.
Wang et al. (Mon,) studied this question.