Phytoplankton Community Composition Retrieval from Space

Phytoplankton community composition (PCCs) - also referred to as functional groups play a key role in ocean biogeochemical cycling, climate regulation, and marine ecosystem dynamics. Accurate quantification of these groups from satellite ocean color data remains challenging due to spectral similarities among phytoplank- ton types and the limitations of existing empirical and semi-analytical models. In this study, we used an extreme gradient boosting (XGBoost) tree-based regression model to retrieve multiple PCCs and total chlorophyll-a concentrations from sim- ulated hyperspectral remote sensing top-of-atmosphere (TOA) ocean color data as well as some ancillary data. The intent is to mimic what could be gathered from the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission and auxiliary data sources to characterize to characterize the environment. In its final form, the model, validated on an out-of-sample set, demonstrated strong predictive performance across most functional groups, with R2 values exceeding 0.95. Dinoflag- ellate retrievals showed lower accuracy (R2 = 0.53). Further analysis revealed that temperature was a key predictor alongside hyperspectral TOA radiance, suggesting that integrating external temperature data could enhance future retrieval models. Furthermore, despite using only 10% of the available hyperspectral bands, feature importance analysis showed that specific spectral regions disproportionately con- tributed to model predictions. These findings highlight the potential of machine learning for phytoplankton classification and inform future algorithm development for hyperspectral ocean color missions.