An Improved QAA-Based Method for Monitoring Water Clarity of Honghu Lake Using Landsat TM, ETM plus and OLI Data

Secchi disk depth (Z(SD)) is used to quantify water clarity as an important water-quality parameter, and one of the most used mechanistic models for Z(SD) is the quasi-analytical algorithm (QAA), of which the latest version is QAA_v6. There are two models in QAA for clear and turbid waters (referred to as QAA_clear and QAA_turbid). QAA_v6 switches between the two models by setting a threshold value for the remote sensing reflectance (R-rs, sr(-1)) at the selected reference band of 656 nm. However, some researchers found that this reference band or the threshold value does not apply to many turbid inland lakes. In Honghu Lake, the R-rs (656) (R-rs at 656 nm) in the whole lake is less than 0.0015 sr(-1); therefore, only QAA_turbid can be applied. Moreover, we found that QAA_clear resulted in overestimation while QAA_turbid resulted in significant underestimations. The waters of inland lakes usually continuously vary between clear and turbid water. We proposed a hypothesis that QAA_turbid and QAA_clear transition evenly, rather than being distinguished by one threshold value, and we developed a model that combined QAA_clear and QAA_turbid according to our assumption. This model simulated the process of continuous change in water clarity. The results showed that our model had a better performance with an RMSE that reduced from 0.5 to 0.28, an MAE that reduced from 0.43 to 0.21, and bias that reduced from -0.4 to -0.05 m compared with QAA_v6. We applied QAA_Honghu to Landsat TM, ETM+, and OLI data and obtained 205 Z(SD) maps with high spatial resolution in Honghu Lake. The results were consistent with the existing in situ measurements. From 1987-2020, the Z(SD) results of Honghu Lake showed an overall downward trend and a distinct seasonal pattern.