Soil salinity prediction using satellite-based variables and machine learning: Case study in Tra Vinh province, Mekong Delta, Vietnam

Abstract

The precision of estimating soil salinity is considered a key task in solving soil salinity problems and irrigation management of agriculture. This problem is increasingly important in the Mekong Delta, where it is severely affected by this phenomenon in the context of climate variability. Therefore, this paper aims to construct a soil salinity map with high accuracy using machine learning and Sentinel 2A, namely Xgboost (XGB) and Random Forest (RF). The province of Tra Vinh in the Mekong Delta has been selected as the case study. 68 soil salinity samples were collected in August 2024, and 25 conditioning factors extracted from the Sentinel 2A image were used as input data for the machine-learning model. Three statistical indices, namely root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), were used to evaluate the effectiveness of machine learning models. The results showed that with an R2 value of 0.86, the XGB model was superior to the RF model with an R2 value of 0.67. Furthermore, Tra Vinh province, the coastal region, and along the Mekong River are more severely affected by soil salinity with an electrical conductivity (EC) value of more than 10. This region, more affected by soil salinity, is related to rising tides and sea levels in the context of climate variability. This study plays an important role and can support farmers in regions affected by soil salinity in building investment measures to reduce the impacts of soil salinity on the development of agriculture.