Fusing Hyperspectral Proximal Sensing to Enhance Chlorophyll-a Estimation From Sentinel-2

Accurate quantification of chlorophyll-a (Chla) is vital for understanding, assessing, and managing eutrophication, aquatic ecosystem health, and biogeochemical cycling. However, satellite-based Chlaestimation in Case Ⅱ waters remains challenging due to complex optical properties, limited synchronized field data, and uncertainties in spectral-temporal matching. This study introduces a novel approach that integrates Sentinel-2 multispectral imagery with coincident hyperspectral proximal sensing (HPS) measurements to enhance the Chlaestimation accuracy in optically complex waters. First, a band-conversion fusion method with high consistency (slope=1,R2=0.95) was proposed by leveraging strong correlations between Sentinel-2 and HPS spectra. Subsequently, a high-precision machine learning-based fused Chlamodel (R2=0.93,NRMSE=24.2%,MAPE=31.6%) was developed using a fusion dataset combining HPS measurements (N=1728) and Sentinel-2 data (N=100). This model significantly outperformed the unfused Sentinel-2-only model and four existing literature algorithms. When applied to Sentinel-2 time-series data (2016–2024) over Lake Taihu, the fused model revealed a notable eutrophication mitigation trend, with Chladecreasing from 30.78 μg/L to 19.56 μg/L. Comparative analyses demonstrated that fusing HPS data improved Sentinel-2–derived Chlaestimation performance because strict temporal synchronization of HPS measurements reduced spectral uncertainty and enhanced sensitivity to Chlavariability. Recalibrating Sentinel-2 spectra reducedMAPEfrom 41% to 9.7%, and expanding field-matched datasets enhanced the model reliability and robustness by 23.4% and 10.8%, respectively. The study underscores the transformative potential of multisource data fusion for advancing satellite-based water quality monitoring, particularly in optically complex aquatic systems. The proposed framework offers a transferable methodology for estimating other biogeochemical parameters through integrated spaceborne and proximal sensing approaches, providing a foundation for improved environmental monitoring and management.