Spatio-temporal water quality variability and regionalization of tropical reservoirs based on Landsat imagery

Abstract

Reservoirs are aquatic environments with complex interactions between bio-chemical and physical components. Many of those parameters can be estimated from remote sensing tools, especially since increasing technological progress allows the analysis of those environments in high spatial and temporal resolutions. This work aimed to analyze the water quality of four reservoirs, with high energy productivity, located in the center-southern region of Brazil from bio-optical and thermal water parameters. Therefore, hundreds of Landsat 5 (TM), 7 (ETM+), and 8 (OLI) satellite images were processed by the Google Earth Engine cloud computing platform to estimate water surface temperature, chlorophyll-a density (chl-a), Secchi depth (SD), and trophic state index (TSI). To calibrate the equations, 11 in situ monitoring points were used in 27 scenes from the TM and ETM+ sensors and 30 from the OLI sensor, within 6 days interval between the monitoring date and the image acquisition. Additionally, records from two weather stations near the reservoirs were analyzed to evaluate meteorological influences on water parameters. SD has influenced TSI in three reservoirs, while chl-a has acted as a proxy in only one. Moderate correlations were observed between air temperature, rainfall, and global radiation and the bio-optical and thermal water parameters. Remote sensing has detected a clear spatio-temporal variation, which allowed regionalizing the reservoirs. Reservoir regionalization based on the mapped parameters may be able not only to visualize the spatial dynamics of the waters but also as a tool for the management of water bodies with multiple users and issues.