Enhanced Algorithm for Water Transparency Estimation in Turbid Plateau Waters Using Orbita Hyperspectral (OHS) Imagery

Abstract

Deteriorating water environments in plateau lakes are increasingly influenced by climate change and human activities. Water transparency, critical for understanding underwater lightfield environments, is commonly quantified as the Secchi disk depth ( ${Z} _{\text {SD}}$ , m). Despite advances in ${Z} _{\text {SD}}$ semianalytical model, their application in turbid plateau lakes faces challenges due to differences in quasi-analytical algorithm (QAA) and image limitations. To address these challenges, this study introduced a novel hybrid QAA model (QAAhybrid) specifically designed to estimate ${Z} _{\text {SD}}$ using orbita hyperspectral (OHS) images, a new hyperspectral image in China. The algorithm’s uncertainty and image quality were evaluated and compared using the error propagation theory and noise equivalent ${Z} _{\mathbf {SD}}$ (NEZSD). Several main findings can be drawn: 1) the QAAhybrid, categorized as moderately turbid and extremely turbid waters using a remote sensing reflectance ratio and outperformed other QAA models; 2) the new ${Z} _{\text {SD}}$ model produced a mean absolute percentage difference (MAPD) of 10.89%, demonstrating better accuracy compared to the existing ${Z} _{\text {SD}}$ models and had an MAPD of 23.35% when applied to OHS images; and 3) documented ${Z} _{\text {SD}}$ from OHS images showed that Dianchi Lake had a trend of increasing from the lake center toward the shore, while Erhai Lake had a trend of decreasing from north to south. These findings emphasize the feasibility of the new ${Z} _{\text {SD}}$ semianalytical model and OHS data in water quality monitoring, providing a reliable approach for water environmental management.