Optimization and Screening of Chl-a Inversion Model for Urban Water Bodies Based on Ground-Based Hyperspectra

Chlorophyll-a (Chl-a) serves as a crucial indicator of water quality, making the precise monitoring of its concentration essential for aquatic environment ecosystem protection. Water color retrieval technology has gained prominence in monitoring spatiotemporal variations in water quality. This study evaluated inversion models for Chl-a estimation in urban water bodies using ground-based hyperspectral data in Nanjing, China. The results indicate that the normalizing of water-leaving reflectance significantly enhances the correlation between water-leaving reflectance and measured Chl-a concentration. However, due to the complexity of urban water bodies and the diversity of interfering components, the three ratio algorithms of OC2V4, OC4V4, and TChla using blue–green bands yielded suboptimal inversion results. In contrast, the normalized fluorescence line height (NFH) algorithm exhibited a robust performance, yielding an R² of 0.70. Furthermore, the overall performance of the near-infrared–Red (NIR-red)-band algorithms showed a commendable overall performance (R² > 0.60), and the best four-band algorithm, 4BDA, achieved an R² of 0.72. Other index algorithms, such as the Yang index and the normalized difference Chl-a index (NDCI), also performed well (R² = 0.61). Notably, the classification of Chl-a concentrations did not significantly enhance the inversion accuracy of the empirical and semi-analytical models. Only the NFH algorithm using the fluorescence band greatly improved the inversion accuracy for low Chl-a concentrations (R² = 0.75), likely due to the influence of Chl-a and other substances on fluorescence peak positioning and height. Ultimately, the NFH model is identified as the optimal approach for Chl-a inversion across varying Chl-a concentrations in urban water bodies. This study provides critical technical support for the protection of aquatic environments and the management of urban water resources.