Enhancing Chlorophyll-a Estimation in Optically Complex Waters Using ZY-1 02E Hyperspectral Imagery: An Integrated Approach Combining Optical Classification and Multi-Index Blending Models

Chlorophyll-a (Chl-a) concentration is a key parameter for assessing the degree of eutrophication and the algal bloom risk in water bodies. Accurate and robust monitoring of Chl-a is crucial for effective water quality management of inland and coastal optically complex Case-II waters. This study proposes a stratified integrated framework that combines optical water type (OWT) classification and multi-index blending models and evaluates the capability of ZY-1 02E hyperspectral imagery in the retrieval of Chl-a concentration in Case-II waters. This research is based on ZY-1 02E hyperspectral remote sensing images and ground synchronous measurement data from four typical water bodies in China (Dongpu Reservoir, Nanyi Lake, Tangdao Bay, and Moon-lake Reservoir). Using Fuzzy C-Means (FCM) clustering combined with spectral feature analysis, three different OWTs were identified, and the bands sensitive to Chl-a for each water type were recognized. Subsequently, the most suitable semi-empirical indices (BR, TBI) were selected, and a new suspended matter correction index (SMCI) was constructed by integrating spectral bands and TSM data specifically for high-turbidity waters to facilitate the retrieval of Chl-a concentration. The RMSE and MAPE of the model constructed based on the unclassified dataset were 3.1586 μg·L⁻¹ and 30.82%, respectively. When the stratified ensemble method based on optical water type classification was employed, the overall RMSE and MAPE were reduced to 1.5832 μg·L⁻¹ and 16.36%. The results demonstrate that this hierarchical ensemble framework significantly improved the retrieval accuracy of Chl-a concentration. An uncertainty assessment of the Chl-a retrieval model for highly turbid waters incorporating SMCI was conducted using the Monte Carlo method, revealing a mean coefficient of variation of 0.0567 and a coverage rate of 95.65% for the 95% confidence interval, indicating high predictive stability and reliability of the model. This study emphasizes the importance of the integrated framework strategy that combines OWTs classification and multi-index blending models for accurate and robust remote sensing estimation of Chl-a concentration under optically complex environmental conditions. It confirms the application potential of ZY-1 02E hyperspectral data in monitoring Chl-a in inland and near-coastal waters at medium and small scales.