Remote Sensing of Sun-Induced Fluorescence in a Deep Lake: Disentangling Quenching Mechanisms Improves Relationship With Chlorophyll-a Concentration Estimates - Remika S. Gupana; Daniel Odermatt; Abolfazl Irani Rahaghi; Camille Minaudo; Mortimer Werther; Claudia Giardino; Alexander Damm (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Sun-induced fluorescence (SIF) from phytoplankton has historically been used as a proxy for chlorophyll-a (chl-a) concentration estimates in water bodies using optical earth observation data. However, the relationship is often affected by spectral features caused by elastic scattering, and by the shifting incidence of different fluorescence quenching mechanisms. This study found that disentangling photochemical quenching (PQ) and nonphotochemical quenching (NPQ) cases improves SIF-based chl-a estimates. Furthermore, we defined strategies that can distinguish the two quenching mechanisms. We assembled a unique dataset collected between 2018 and 2022 by an autonomous profiler in Lake Geneva (Western Europe). We used NPQ-influenced chl-a estimates from the fluorometer and NPQ-corrected chl-a estimates to distinguish between PQ and NPQ cases. The correlation between SIF yield and chl-a is weak when considering the entire dataset (R2 = 0.37 and median absolute percentage difference (MAPD) = 74%). It increases strongly when comparing PQ (R2 = 0.72 and MAPD = 49%) and NPQ cases (R2 = 0.48 and MAPD = 68%) separately. Analyzing a subset of in situ measurements acquired around Sentinel-3 overpasses (±3 h) improved the performance metrics for both PQ (R2 = 0.82 and MAPD = 35%) and NPQ cases (R2 = 0.43 and MAPD = 61%). However, when applying the same approach to Sentinel-3 Ocean and Land Color Instrument data, we found that the errors in remote sensing reflectance products disable such an adaptation. We conclude that enhanced atmospheric correction in the red-to-near-infrared region for oligo-mesotrophic lakes is needed to demonstrate the upscaling of our in-situ-based results. This will enhance satellite-based SIF yield retrievals and, subsequently, obtain SIF-related phytoplankton physiology products.