Monitoring of Soil Salinity in the Weiku Oasis Based on Feature Space Models with Typical Parameters Derived from Sentinel-2 MSI Images

Soil salinization, as one of the types of land degradation, is a global threat. It not only poses serious ecological problems, but also poses great challenges for the sustainable utilization of land resources, especially in arid and semi-arid areas. The Weiku Oasis is undoubtedly one of the typical areas under severe salinization. The wide spread of saline soil brings numerous negative impacts to the local region. To prevent the escalation of soil salinization, timely monitoring of soil salinization is urgently needed for informed decision-making. Remote sensing technology can obtain large-scale datasets in a short period, allowing researchers to carry out the rapid and accurate investigation of soil salinization. Sentinel-2 images have a relatively high spatial resolution and provide red-edge bands data, referring to bands 5, 6, and 7, and the use of red-edge bands is a new approach to estimate soil salinization in the Weiku Oasis. In this study, we selected five typical indices (NDre1, RNDSI, MSAVI, NDWI, SI3, with the first two being red-edge indices) from twenty potential indices to construct multiple two-dimensional feature space models. Consequently, an optimal and novel monitoring index for soil salinization in the Weiku Oasis was developed. The result showed that: (1) The monitoring index MSAVI-RNDSI, which includes red-edge indices, had the highest inversion accuracy of R 2 = 0.7998 and MAE = 3.3444; (2) The red-edge salinity indices effectively captured the conditions of salinization, with the feature space model composed of red-edge indices achieving an average inversion accuracy of R 2 = 0.7902; (3) Land-use type was identified as the primary factor affecting the degree of soil salinization in the study area. The proposed approach provides a highly accurate and high-resolution soil salinity mapping strategy.