Soil Salinity Mapping using Sentinel 2 for Agricultural Land Areas in Lagoon and Coastal Regions of Hue City, Vietnam

Soil salinity maps are crucial for agricultural development and soil quality improvement. However, the optimal techniques for soil salinity mapping using remote sensing remain debatable. Coastal and lagoon regions, such as those in Hue City, Vietnam, are particularly vulnerable to soil salinization, which can significantly impact land use and crop productivity.

This research was conducted in two communes within the lagoon and coastal region of Hue City, Vietnam. Sentinel-2 satellite imagery and ground measurements of soil electrical conductivity (EC) were collected and analyzed. Simple and multiple linear regression models were developed using spectral indices as input features to estimate soil salinity.

The multiple linear regression model incorporating SI3 and NDVI achieved a strong performance with an R² of 0.84 and an RMSE of 0.52 dS/m, while the SI3 index is the most suitable for a simple regression model. Significant differences in soil salinity were observed among land use types, with coastal forest lands exhibiting the highest EC values and paddy rice fields the lowest. These findings demonstrate the accuracy and usefulness of remote sensing techniques for monitoring soil salinity, providing valuable data for preventing soil degradation and supporting sustainable agricultural practices.