The main source of Sentinel-2 reflectivity data is downloaded from ESA. The rest of the auxiliary data sources are on-site measurement data and are generated independently.

The Sentinel-2 reflectivity data were not further processed because they had already been processed by ESA. The soil salinity used for calculation of this model was also collected by the 5-point method and measured the conductivity data in the laboratory. This dataset is based on comparing verification data from various machine learning such as random forest, gradient regression, and support vector machines, and selecting random forest algorithm to carry out soil salt inversion calculation.

This dataset is based on comparing verification data from various machine learning such as random forest, gradient regression, and support vector machines, and selecting random forest algorithm to carry out soil salt inversion calculation. The data resolution is 10m. Among them, the training samples of the continuous forest algorithm include 30cm surface soil conductivity data from measured soil surface in Turpan Hami City, Aibi Lake Basin, Aksu Xinhe County, Kashgar Shufu County, etc. The measurement method for soil conductivity is electrode method, which meets the measurement requirements of (HJ802 -2016). The accuracy of this data is good in areas with low vegetation coverage, but the accuracy has not yet been tested in areas with high vegetation coverage. There are null values in some areas of the physical data, mainly because the cloud amount in this area is too large in the month, and Sentinel-2 satellite cannot obtain effective images to accurately retrieve the soil salinity in this area, so there are null values.