A hyperspectral approach for retrieving inherent optical properties, phytoplankton pigments, and associated uncertainties from non-water absorption

Brice Grunert^1*Audrey Ciochetto¹Colleen Mouw²

• ¹Cleveland State University, Cleveland, United States
• ²University of Rhode Island, Kingston, Rhode Island, United States

Aquatic ecosystems and associated biogeochemical cycles are dynamic and driven by spatiotemporally diverse processes, including increasing impacts from more extreme weather and climate-related stressors. Ocean color datasets collected by airborne and satellite sensors provide platforms capable of observing distinct ecosystem features at requisite spatial and temporal scales; however, many of the tools used, including novel tools developed for hyperspectral datasets, rely on assumptions to retrieve component optical properties that are tied to specific ecosystem traits, such as phytoplankton pigments and spectral features affiliated with carbon concentration and composition. The original Derivative Analysis and Iterative Spectral Evaluation of Absorption (DAISEA) algorithm was produced as a means to identify spectral features in hyperspectral absorption spectra free of explicit spectral assumptions in an effort to bypass these limitations. Here, we provide an update to the original DAISEA algorithm that includes improved retrieval of colored dissolved organic matter plus non-algal particulate absorption and phytoplankton absorption, Gaussian components affiliated with phytoplankton pigments and estimates of uncertainty for all retrieved parameters. Spectral root mean square error (RMSE) for the majority of spectra and wavelengths was < 20%, with no bias at visible wavelengths. Relationships between phytoplankton pigment concentrations and modeled Gaussian peak height showed errors of 5-14%, indicating strong potential for DAISEA to estimate pigment concentrations in future applications. Finally, we considered the impact of simulated noise and spectral resolution on model performance. Across absorption spectra, simulated noise led to modest changes in mode performance, while spectra resolution varying from 1-5 nm did not significantly alter model performance. Based on these findings, we expect DAISEA to pair well with remote sensing inversion schemes that retrieve spectral non-water absorption free of spectral assumptions.