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A non-optically active lake salinity dataset by satellite remote sensing.

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This study introduces a new dataset for lake water salinity in Inner Mongolia, generated using machine learning and satellite data. This resource aids in monitoring and managing lake salinization for ecosystem health.

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Water Quality Monitoring

Background:

  • Water salinity is crucial for assessing lake water quality but is challenging to measure remotely.
  • Existing models lack regional applicability, and pixel-scale salinity datasets are scarce.

Purpose of the Study:

  • To develop a large-scale lake salinity dataset using remote sensing data.
  • To apply machine learning for accurate salinity inversion.
  • To provide data for lake salinization prevention and management.

Main Methods:

  • Constructed an extreme gradient boosting (XGB) salinity model.
  • Utilized Sentinel-2 remote sensing reflectance data.
  • Generated the Inner Mongolia lake salinity (IMSAL) dataset with 10-meter resolution.

Main Results:

  • The IMSAL dataset includes 928 raster scenes for eight lakes (2016-2024).
  • Model validation confirmed good consistency and reliability with measured salinities.
  • The dataset reveals spatial patterns and long-term variations in lake salinity.

Conclusions:

  • The IMSAL dataset offers valuable insights into lake salinity dynamics.
  • This resource supports sustainable lake ecosystem development and management.
  • Machine learning enhances the potential for large-scale salinity monitoring.