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.

定期监测内陆水体对于确定水质恶化的区域并采取措施遏制恶化至关重要。本研究旨在开发一个半分析模型，用于从 Sentinel 2 (S2) 图像估计营养状态指数 (TSI)。基于从 S2 图像检索到的固有光学特性 (IOP) 与美国 Milford Lake 测量的 TSI 数据之间的相关性，为 S2 数据（称为 QAA-S2）参数化半分析算法 QAA-v6 . 与 QAA-v6 相比，该修改后的模型对美国三个不同湖泊和印度一个湖泊的 TSI 估计准确度提高了近 50%。使用三个大气校正处理器（ACP）（即 C2RCC、进行了 Acolite 和 Sen2Cor），C2RCC 给出了 TSI 估计的最小平均绝对百分比误差（MAPE<8%）。使用单散射反照率的 TSI 估计 (u ) 在 S2 的 B5 和 B6 波段是合理的 (MAPE<12%) 以将它们标记为 TSI 的计算有效估计量。这些结果表明 QAA-S2 在估算 TSI 时对全球各个地区的有用性和可转移性。