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The lack of previous global ocean color satellite mission with spectral capabilities similar to those from PACE calls for a framework to assess and develop the best approaches and anticipate potential problems. This proposal directly applies to the first area of basic and applied research called out in the solicitation: "Theoretical and analytical studies associated with the use of OCI-analog hyperspectral data for the development of an algorithm or approach for one or more ocean ... products from OCI". We will exploit potential PACE capabilities by (1) developing algorithms to derive phytoplankton composition and (2) producing global, complete, hyperspectral water leaving radiances by assimilating the satellite radiances in an Earth System Model. The algorithms will be developed by deriving hyperspectral water leaving radiance spectra for various phytoplankton concentration and composition using a radiation model, the Ocean- Atmosphere Spectral Irradiance Model (OASIM). The changes in spectral shape and magnitude of these simulations will be used as the basis to derive the critical bands that are representative of the various phytoplankton groups and begin to develop the algorithms in a controlled environment (where the concentration and composition of phytoplankton is pre-defined). We will then test and refine the algorithms in a more 'natural' settings (all phytoplankton groups included and allowed to vary depending on the physical and biogeochemical conditions) using global hyperspectral simulated water leaving radiances developed during the first PACE Science Team (Figure 1). Prior to the algorithm development, this simulated water leaving radiances dataset will be calibrated using existing airborne (i.e. AVIRIS, PRISM) and spaceborne (i.e. HICO) instruments. Once the algorithms have been refined we will validate these using an in situ database of phytoplankton composition, also developed during the first PACE Science Team. We will further exploit the capabilities of PACE by setting up a framework to assimilate hyperspectral water leaving radiances, as well as other products that can be derived from the model, producing Level-4 data products that are temporally and spatially complete. Comprehensive quantitative error and uncertainty analysis will be integral in each of the stages of the proposal. This project builds on the efforts by the PI and Co-Is during the first PACE Science Team and on ongoing collaboration with the PACE Project Team. Our approach can provide valuable information in preparation for PACE and also enhance the usefulness of PACE data as soon as they become available after launch.

We propose to develop an algorithm to derive phytoplankton composition using a radiation model that provides hyperspectral data similar to what we expect from the PACE mission. Because there has not been any previous global mission with similar capabilities, there is a need for assessing the best approaches and anticipate potential problems if we are to get the most information out of this mission. Here we propose to use the Ocean-Atmosphere Spectral Irradiance Model (OASIM) to derive the total water-leaving radiance of single and mixed phytoplankton functional groups. We will then use an extensive dataset to develop an algorithm or algorithms to derive phytoplankton composition from these hyperspectral data. We will test this algorithm against in situ data that were withheld from the algorithm development. Finally we will apply this algorithm to a state-of-the-art biogeochemical model (NASA Ocean Biogeochemical Model) that has been shown to represent reasonably well the global distribution of phytoplankton composition. Using this model, we will assess how well this newly developed algorithm does in representing the natural global distribution of phytoplankton groups. Comprehensive quantitative error and uncertainty analysis will be integral in each of the stages of the proposal. Additionally, we propose simulations to test different configurations of the sensor to understand the capabilities and limitations associated with engineering options. Although we acknowledge that there may be formidable challenges throughout these steps, we believe that we can learn some valuable information from these challenges.