Semi-analytical model for TSI estimation of inland water bodies from Sentinel 2 imagery

Abstract Regular monitoring of inland water bodies is essential to identify the areas deteriorating quality and take measures curb impairment. The present study aims develop a semi-analytical model for estimating Trophic State Index (TSI) from Sentinel 2 (S2) imagery. A algorithm, QAA-v6, parameterized S2 data (referred as QAA-S2) based on correlation between retrieved inherent optical properties (IOPs) imagery measured TSI Milford Lake in USA. accuracy estimation by this modified three different lakes located USA one lake India has increased almost 50%, when compared with that QAA-v6. analysis IOPs QAA-S2 using outputs atmospheric correction processors (ACPs) (namely C2RCC, Acolite Sen2Cor) was carried out C2RCC gave least mean absolute percentage error (MAPE<8%) estimation. single-scattering albedo (u) at B5 B6 bands reasonable (MAPE<12%) mark them computationally efficient estimators TSI. These results indicate usefulness transferability various parts globe