Derive phytoplankton size classes, detrital matter, particulaste organic matter and particulate inorganic matter from ocean color observation

The Earth’s oceans have absorbed nearly half of anthropogenic CO2 released into the atmosphere. One of the most important oceanic processes in sequestering atmospheric CO2 is net primary production by phytoplankton that fix inorganic carbon into organic matter in the sun-lit, surface ocean and its subsequent vertical transport to the ocean’s interior. To better understand and quantify this biological pump requires detailed knowledge of spatial and temporal variations of phytoplankton community structure and other carbon pools. Ocean color remote sensing has revolutionized our understanding of global distribution of phytoplankton biomass and rates of primary production. However, significant uncertainty remains because (i) primary production varies among different phytoplankton species, which cannot be estimated adequately using a single measure of total biomass; (ii) other carbon pools, such as dissolved organic carbon, also play a role; and (iii) presence of mineral particles alters fluxes of organic carbon. We propose to develop advanced inversion algorithms to infer global distributions of phytoplankton in three size classes (micro, nano, and pico), colored detrital matter, dissolved and particulate matter from remotely sensed ocean color data to support the studies of oceanic sequestration of atmospheric CO2. Dr. Xiaodong Zhang, a Professor with the Department of Earth System Science and Policy at the University of North Dakota will lead this effort as the Science-Investigator. His prior studies have led to the development of individual algorithms based on the field measurements that derive phytoplankton size classes and colored detrital matter from the spectral absorption coefficients and derive size and composition distribution of various biogeochemical stocks from the volume scattering functions. We will refine, revise and combine these individual algorithms into applications that can be used for satellite ocean color observation. Specifically, the developed algorithms will allow the retrieval of (i) phytoplankton concentrations in three size classes of micro (> 20 µm), nano (2 – 20 µm) and pico (< 2 µm) ranges; (ii) absorption at 410 nm and spectral slope of the colored detrital matter; (iii) backscattering by very small particles (of sizes < 0.2 µm); (iv) concentrations of particulate organic matter (POM) and particulate inorganic matter (PIM). The first two products will be estimate from ocean color – derived spectral absorption coefficient and the last two product from ocean color –derived spectral backscattering coefficient. We will test the developed algorithms over global ocean and estimate the associated uncertainty using collocated NASA ocean color observation and field measurements as well as additional field experiments. The proposed project aligns with the areas of interest of the Earth Science Division of the Science and Exploration Directorate at NASA Goddard Space Flight Center (GSFC). In particular, it is relevant to the focus of NASA’s Ocean Biology and Biogeochemistry program and NASA’s upcoming PACE satellite mission. Dr. Jeremy Werdell at NASA GSFC, the Project Scientist for the PACE mission, will collaborate with us. His research and knowledge on in-water bio-optical algorithm development and validation of remotely-sensed data products will provide well-need expertise on satellite algorithm development and testing with NASA’s ocean color data.

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