The eutrophication of reservoirs significantly impacts human health and environmental security. However, in situ water quality monitoring can be expensive once it includes equipment and human resources. An effective proxy for water quality is the Trophic State Index (TSI) Chlorophyll-a (chl-a) based. Remote sensing techniques have helped the authorities and scientific community to map TSI worldwide. Then, this study aimed to develop a remote sensing-based TSI algorithm and estimate the TSI spatiotemporal distribution in a reservoir in Brazil. The chl-a concentration was used as a proxy to TSI and classified into three classes: OligoMeso, EutroSuper, and Hyper. The calibrated algorithm was applied to the Jaguari-Jacareí reservoir to obtain TSI between 2013 and 2022. Classification results achieved an overall accuracy of 75% for a validation dataset. Although the general pattern of the TSI in the reservoir is majority OligoMeso, the results indicate two patterns established according to dry and wet seasons.