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In this paper, the authors use remote-sensing images to monitor the water quality of reservoirs located in the semiarid region of Northeast Brazil. Sentinel-2 MSI TOA Level 1C reflectance images were used to remotely estimate the concentration of chlorophyll-a (chl-a), the main indicator of the trophic state of aquatic environments, in five reservoirs in the state of Ceará, Brazil. A three-spectral band retrieval model was calibrated using 171 water samples, collected from November 2015 through July 2018 in 5 reservoirs. For validation, 71 additional samples, collected from August 2018 through December 2019, were used to ensure a robust accuracy assessment. The TOA Level 1C products performed very well, achieving a relative RMSE of 28% and R2 = 0.80. Data on wind direction and speed, solar radiation and reservoir volume were used to generate a conceptual model to analyze the behavior of chl-a in the surface waters of the Castanhão reservoir. During 2019, the reservoir water quality showed strong variation, with concentration fluctuating from 30 to 95 µg/L We showed that the end of the dry season is marked by strong eutrophic conditions corresponding to very low water inflows into the reservoir. During the rainy season there is a large decrease in the chl-a concentration following the increase of the lake water storage. During the following dry season, satellite data show a progressive improvement of the trophic state controlled by wind intensity that promotes a better mixing of the reservoir waters and inhibiting the development of most phytoplankton. [ABSTRACT FROM AUTHOR]

Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google Earth Engine (GEE) which allows end-users to access algae bloom related products with high spatial (30 m) and temporal (~5 day) resolution. The proposed methodology uses Sentinel-2 images corrected for atmospheric and sun-glint effects to generate an image collection of the Normalized Difference Chlorophyll-a Index (NDCI) for the entire time-series. NDCI is used to estimate both Chl-a concentration, based on a non-linear fitting model, and Trophic State Index (TSI), based on a tree-decision model classification into five classes. Once the Chl-a and TSI algorithms had been calibrated and validated they were implemented in GEE as an Earth Engine App, entitled Algae Bloom Monitoring Application (AlgaeMAp). AlgaeMAp is the first online platform built within the GEE platform that offers high spatial resolution of water quality parameters. The App benefits from the huge processing capability of GEE that allows any user with internet access to easily extract detailed spatial (30 m) and long temporal Chl-a and TSI information (from August 2015 and with images every 5 days) throughout the most important reservoirs in the State of São Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America. [ABSTRACT FROM AUTHOR]