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 of deteriorating water quality and to take measures to curb the impairment. The present study aims to develop a semi-analytical model for estimating Trophic State Index (TSI) from Sentinel 2 (S2) imagery. A semi-analytical algorithm, QAA-v6, is parameterized for S2 data (referred to as QAA-S2) based on the correlation between the retrieved inherent optical properties (IOPs) from S2 imagery and the measured TSI data of Milford Lake in the USA. The accuracy of estimation of TSI by this modified model from three different lakes located in USA and from one lake in India has increased by almost 50%, when compared with that of QAA-v6. A correlation analysis of the retrieved IOPs from QAA-S2 using the outputs of three atmospheric correction processors (ACPs) (namely C2RCC, Acolite and Sen2Cor) was carried out and C2RCC gave the least mean absolute percentage error (MAPE<8%) for TSI estimation. TSI estimation using single-scattering albedo (u) at B5 and B6 bands of S2 was reasonable (MAPE<12%) to mark them as computationally efficient estimators of TSI. These results indicate the usefulness and transferability of the QAA-S2 to the various parts of the globe for estimating TSI. HIGHLIGHTS A parameterized semi-analytical model (QAA-S2) is proposed for the estimation of TSI from Sentinel 2 imagery.; C2RCC-retrieved reflectance data provide the best results for the model; three atmospheric correction processors, namely C2RCC, Acolite and Sen2Cor, are compared.; Single-scattering albedo at B5 and B6 of S2L1C and indices derived from backscattering coefficients are identified as good estimators for TSI.;

Keywords

• acolite
• c2rcc
• milford lake
• qaa
• sentinel 2l1c
• tsi estimation