Differentiating phytoplankton taxa in lakes using hyperspectral in situ reflectance and imaging microscopy

Phytoplankton play a central role in aquatic ecosystems, influencing biogeochemical cycles, food web dynamics, and overall water quality. Monitoring their composition is essential for assessing water ecosystem health and detecting environmental changes. Chlorophyll-a concentration is widely used as a proxy for phytoplankton abundance in inland waters. Together with colored dissolved organic matter and total suspended matter, these parameters can be retrieved from remote sensing reflectance data. However, identifying the detailed taxonomic composition of phytoplankton in lakes remains a major challenge. Spectral matching algorithms offer promising solutions to overcome this limitation. In this study, we investigated the potential of retrieving phytoplankton taxa composition from high-resolution in situ spectroscopy measurements by applying radiative transfer inversion and validating the results against phytoplankton abundance data obtained from an imaging microscope. First, we assessed the performance of our approach in retrieving four phytoplankton taxa under cloud-free conditions. Then, we extended the analysis to two seasons, covering multiple consecutive blooms using data acquired independently of cloudiness. The high agreement between the imaging microscopy results and those obtained from in situ spectroscopy indicates that remote sensing with radiative transfer inversions can track the evolution of phytoplankton blooms. The results suggest that low phytoplankton concentrations and the lack of unique spectral features for some taxa may prevent the accurate identification of phytoplankton composition through spectroscopy. In addition, the natural variability in cell size, along with physiological changes such as fluctuations in intracellular chlorophyll-a content, impacts the empirical conversion from cell cross section to intracellular chlorophyll-a content.