A non-optically active lake salinity dataset in Inner Mongolia

Water salinity characterizes the physicochemical properties of natural water, serving as an essential parameter for assessing lake water quality. However, the efficiency of remote sensing inversion of water salinity is limited as salinity is a non-optically active parameter, leading to the lack of a pixel-scale lake salinity dataset. Recently, developments in high spatial resolution and machine learning approaches have provided opportunities to solve this issue. Here, we generate the Inner Mongolia lake salinity dataset (IMSAL) with Sentinel-2 data that contains 928 raster scenes with 10-meter spatial resolution for eight lakes from 2016 to 2024. This dataset has a varying temporal resolution due to differences in satellite overpasses and cloud interference.