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.