Predictive model for monitoring water turbidity in a subtropical lagoon using Sentinel-2A/B MSI images

Abstract

Ensuring prompt and effective water quality monitoring is increasingly important. Remote sensing has been shown to be an effective tool for simplifying and speeding up this process. The aim of this study is to develop an empirical model to map the spatial and temporal dynamics of turbidity in Mirim Lagoon, located in southern Brazil. To achieve this, Sentinel-2A/B MSI sensor data were combined with turbidity data collected in situ. The model was applied to monthly images (with cloud cover ≤ 20%) in 2019 and 2020 using the Google Earth Engine (GEE) platform. Mean turbidity values in the lagoon did not vary significantly, remaining between 30 and 75 NTU overall. However, there were differences in turbidity levels between the northern and southern regions of the lagoon in some months of the investigated years. By applying this methodology and analyzing the results, we were able to better understand the behavior of turbidity throughout the lagoon and gain insights into the quality of this important freshwater source.