Sentinel 2 analysis of turbidity retrieval models in inland water bodies: The case study of Jordanian dams

Abstract

AbstractThis study investigates the potential of using Sentinel 2 multispectral satellite images to develop turbidity retrieval models and further estimate the turbidity values of inland water bodies in Jordan. Traditionally, laboratory analysis has been used to assess surface water quality, which is expensive, time‐consuming, and requires accessing the field physically. In contrast, remote sensing technologies can detect the water contaminant level at a consistent spatial and temporal coverage. Turbidity is an essential indicator of inland water quality as it directly reflects under‐water light penetration. This study was held in three Jordanian dams, King Talal Dam, Mujib Dam, and Wadi Al Arab Dam, which vary in their water quality level. Twenty water samples were collected from each dam. Forty samples were used to calibrate the models, and the rest samples were used to validate the predictive models. The results show that Sentinel 2 near‐infrared band to green band (B8/B3) achieved high fitting accuracies with R2 = 0.832 and root mean square error (RMSE) = 1.123 NTU. Overall, this study has demonstrated the ability of Sentinel 2 data to estimate the turbidity in different ranges of inland water bodies’ quality and indicates that remote sensing can be used as an efficient tool for monitoring inland water quality. This study presents empirical data that could act as a platform to extend future work to cover more sites and contexts.