How Much Hyperspectral Information Does Chlorophyll Retrieval Really Need?

Abstract: Satellite ocean color algorithms translate water-leaving radiance into ecological information at spatial and temporal scales that cannot be achieved by field sampling alone. One important variable derived from water-leaving radiance is chlorophyll-a concentration ($\mathrm{CHL\text{-}a}$), a widely used indicator of phytoplankton biomass and physiology. Empirical retrieval algorithms are commonly used for $\mathrm{CHL\text{-}a}$ estimation, but their performance can vary across sensors and optically diverse waters. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission provides unprecedented spectral resolution, expanding the visible spectral information available for ocean color retrievals and raising a practical algorithm design question: how can this fine resolution spectrum be used to derive the next generation of interpretable $\mathrm{CHL\text{-}a}$ retrieval algorithms? We use symbolic regression to identify sparse equations that estimate $\log_{10}(\mathrm{CHL\text{-}a})$ from multi- and hyperspectral remote sensing reflectances. The analysis first tests the standard multispectral ocean color algorithm (OC3--OC6) inputs to ask whether symbolic regression recovers standard band ratio structure, then extends the search to hyperspectral PACE-like reflectances. The best expression discovered achieved a held out root mean square deviation of 0.253 in log$_{10}$ $\mathrm{CHL\text{-}a}$, compared with 0.307 for a fitted OC6 polynomial on the same split. Ablations showed that the predictive skill of hyperspectral models is almost 12\% better than the best standard ocean color retrieval models in root mean squared deviation against held out data. When evaluated by environmental regime, differences were larger for high-chlorophyll samples, which often represent turbid water conditions.