Change analysis of surface water clarity in the Persian Gulf and the Oman Sea by remote sensing data and an interpretable deep learning model.

Abstract

The health of an ecosystem and the quality of water can be determined by the clarity of the water. The Persian Gulf and Oman Sea have a unique ecosystem, and monitoring their water clarity is necessary for sustainable development. Here, various criteria such as hue angle, chlorophyll-a, Forel-Ule index, organic carbon (OC), precipitation, sea surface salinity (SSS), Secchi disk depth (SDD), and sea surface temperature (SST) were analyzed from 2002 to 2018 using MODIS-Aqua Imagery, statistical tests, and deep learning (DL) models to monitor the water clarity of the Persian Gulf and the Oman Sea. The study found differences in criteria across different regions, with coastal areas showing higher Forel-Ule index and chlorophyll-a values. Positive trends in the Persian Gulf and the Oman Sea were attributed to the Forel-Ule index and OC, while negative trends were seen in SSS and SST in the Persian Gulf. The convolutional neural network (CNN) model was found to perform better than long short-term memory (LSTM) in predicting water clarity. Interpretation techniques were used to determine the importance of criteria in monitoring water clarity, with the Forel-Ule index, hue angle, and OC showing the greatest interaction. Sensitivity analysis revealed that chlorophyll-a and SSS had the most significant impact on water clarity prediction. Overall, this study using DL models and MODIS-Aqua Imagery can help improve water quality and protect the environment.