## 4 Discussion  
(1) Model selection. Conventional soil salinity monitoring is mostly obtained from field data collection combined with laboratory measurements. Multispectral remote sensing data acquisition is simple, low-cost, and easy to operate. The remote sensing salinity monitoring index model can be constructed directly through the spectral index, which can quickly measure the soil salinity in the study area. When selecting a model, considering the differences in terrain, climate, and vegetation coverage in different geographical locations of the study area, the precision results of the remote sensing salinity monitoring index models constructed by different spectral indices are inconsistent. As a result, it is suggested that any single model should not be directly selected to invert the salinity of the reclamation area in advance, as in this case, optimal accuracy cannot be guaranteed. (2) Model accuracy. The overall accuracy of the four types of remote sensing salinity monitoring index models was verified, and the remote sensing salinity monitoring index model constructed by the salinity index 1 and the normalized difference vegetation index of the green-red band was better for the inversion of soil salinization in the Alar reclamation area. From the comparison of the overall accuracy, it can be seen that the remote sensing salinity monitoring index models S1 DI1, S1 DI2, S1 DI3, and S1 DI4 constructed by the vegetation index are all very high. The reason may be that the remote sensing image is selected as the period of vigorous vegetation coverage, and the vegetation index can reflect the vegetation growth condition at this time. Vegetation growth is affected by different salinity contents, so it can more indirectly reflect the soil salinization status in the reclamation area. Among them, the S1 DI1 model, S1 DI2 model, S1 DI3 model, and S1 DI4 model showed a decreasing trend in the inversion effect of the cotton field saline soil in the Alar reclamation area. This is likely to be because the GRNDVI vegetation index, GNDVI vegetation index, NDVI vegetation index, and DVI vegetation index had an impact on cotton. The sensitivity of growth also shows a decreasing trend. In this paper, the remote sensing salinity monitoring index model is constructed according to different vegetation indexes and salinity index 1 (SI1). As a result, for different research areas, the vegetation index should be reasonably selected according to its own characteristics to construct the remote sensing salinity monitoring index model so as to accurately retrieve the soil salinity information. (3) Distribution of soil salinity in the reclamation area. The soil salinization in the Alar reclamation area was retrieved through the remote sensing salinity monitoring index parameter model S1 DI1, and the distribution of farmland in the inversion results was basically consistent with the previous research results [18]. The Alar Reclamation Area is a fine-soil plain alluvial by the Tarim River, resulting in relatively high salinity in the cotton field soil. The distribution of soil salinization in cotton fields in the reclamation area is affected by both human factors and natural factors. Soil moisture in cotton fields has a greater impact on soil salinity. The moisture in cotton field soil will migrate salt to the surface soil as cotton plants absorb and evaporate water. The Alar Reclamation Area is located in the arid area of southern Xinjiang, with strong sunlight and large evaporation. Most cotton fields use flood irrigation in spring or winter to reduce the soil salt return in cotton fields and drip irrigation under mulch to control the water volume and soil salinity, which greatly reduces the surface soil in cotton fields. Therefore, the cotton field in the reclamation area is mainly composed of non-saline soil [19].