Review on sea water quality (SWQ) monitoring using satellite remote sensing techniques (SRST).
Journal:
Marine pollution bulletin
Published Date:
Aug 1, 2025
Abstract
Due to extensive anthropogenic activities in coastal areas and rivers connected to the seas, effective and timely monitoring the sea water quality (SWQ) is crucial for maintaining ecosystem health. SWQ monitoring involves examining the chemical, physical, and biological parameters of sea water. Satellite remote sensing techniques (SRST) make us able to measure many of the SWQ parameters effectively. Compared to traditional SWQ monitoring techniques, SRST offers significant advantages due to its global coverage, long-term observation capabilities, flexibility, cost-effectiveness, and efficiency. This paper reviews the literature from the past three decades on space-based SWQ monitoring using a semi-systematic review approach. It outlines the definition and characteristics of SWQ parameters estimated by SRST. In addition to exploring the evolution of satellite sensors, this study also focuses on the methodological aspects of SWQ monitoring using SRST. It finds that semi-empirical algorithms combined with multivariate statistical approaches outperform other methods, recent studies indicate that machine learning models often achieve superior accuracy, with R values frequently exceeding 0.90. The SWQ parameters reviewed some important physical, biological and chemical parameters, include sea surface temperature (SST), chlorophyll-a (Chl-a), sea surface salinity (SSS), coloured dissolved organic matter (CDOM), particulate organic carbon (POC), total suspended solids/matter (TSS/TSM), and Secchi disk depth (SDD). The review concludes that data from optical and passive microwave-based satellite sensors are widely and effectively used for SWQ monitoring. The most frequently monitored SWQ parameters are Chl-a, SST, CDOM, and more recently, POC, with indirect studies also addressing non-optically active variables like total nitrogen (TN) and total phosphorus (TP) through empirical correlations.
