Stability of Physically Modeled versus Data-Extracted End-Members for Multispectral Decomposition of Coastal Water Reflectance

AbstractThe definition of end-members plays a central role in spectral decomposition of aquatic remote sensing reflectance, especially in highly turbid coastal waters where spectral signatures are strongly mixed and sensor dependent. End-members are defined as the purest reflectance spectra of water constituents and often could not be derived directly from observational data. Such data-driven end-members are often sensitive to noise, atmospheric correction uncertainties, and the reduced spectral resolution of multispectral sensors. Here, we examine how physically modeled end-members (MEMs) and end-members extracted from observations (EEMs) compare in terms of stability across different sensor types in the Dutch Wadden Sea.Physically modeled end-members were generated using a validated bio-optical forward model constrained by realistic ranges of optically active constituents. In parallel, EEMs were extracted from in-situ hyperspectral reflectance and from Sentinel-2 MSI and Sentinel-3 OLCI data using a geometric end-member extraction approach. The stability of MEMs and EEMs was evaluated through geometric inclusion analyses, spectral similarity measures, and reflectance reconstruction following Gaussian-based spectral decomposition.The comparison shows that MEMs remain consistent across in-situ hyperspectral and satellite-derived multispectral datasets, while EEMs tend to lose representativeness when applied to multispectral observations. This degradation is mainly linked to band aggregation effects and increased sensitivity to atmospheric correction uncertainties. In contrast, MEMs preserve their spectral geometry and reconstruction capability under these conditions.By separating the role of end-member definition from subsequent retrieval steps, this study demonstrates that physically constrained end-members provide a more robust foundation for multispectral spectral decomposition in optically complex coastal waters. These findings are particularly relevant for operational satellite monitoring applications where stability and transferability are essential.KeywordsRemote Sensing, Water constituents, Spectral Separation, Bio-optical Model, Remote Sensing Reflectance