Abstract:

Abstract The study is aimed to construction of top-soil salinity maps for three coastal districts of Ben Tre province, which is one of the Mekong Delta areas that have been seriously affected by saline intrusion in recent years. A total of 124 top-soil samples were collected and analyzed during the dry season in 2021, of which 59 and 65 samples were taken in January and March respectively. Multivariable linear regression model was then built based on measured salinity, by Electrical Conductivity parameter, and spectral bands extracted from Landsat 8 satellite images for the respective days. The model was built based on the Bayesian statistical method, processed by the R statistical program (version 4.0.3), which big difference between Bayesian and classical statistical methods is that this method does not need to evaluate the distribution of the inputs, therefore helping to optimize the quality and quantity of the collected dataset. The results of the model showed that the model efficiency reaches 67%, and the RMSE and IOA validation parameters meet the requirements: 1.31 and 0.63 respectively. The model also showed that the infrared and panchromatic spectral bands are the two spectral bands which are capable of predicting the salinity of the topsoil in this area. Therefore, the results of this study contribute to a potential method in building an effective and low-cost model to assess surface soil salinity in the study area.

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