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The spectral inherent optical properties (IOPs) of seawater are fundamental data products of the PACE Ocean Color Instrument (OCI) and critical to the interpretation of optical remote sensing. The total IOPs result from additive contributions of seawater constituents such as phytoplankton, nonalgal particles, colored dissolved organic matter (CDOM), and water. By carrying information about various biogeochemically important constituents, the IOPs are essential variables for advancing an understanding of the links and interactions between ocean biology, biogeochemistry, and Earth's climate. Current NASA IOP data products utilize semi-analytical algorithms that rely on spectral optimization and/or spectral deconvolution approaches to derive total and constituent IOPs from ocean color observations. These approaches are subject to significant limitations owing to the use of predefined spectral shapes for output IOPs, simultaneous solutions such that constituent IOPs are not independently obtained, and limited application to the UV spectral range. This proposal is motivated by a need for next-generation algorithms which will alleviate these limitations, take full advantage of PACE OCI capabilities, and achieve high level of scientific readiness before the mission launch.

Our goal is to develop and evaluate a three-step semi-analytical algorithm (3SAA) for deriving hyperspectral total and constituent IOPs from PACE OCI. The 3SAA will sequentially combine an inverse reflectance model with two absorption partitioning models. The reflectance model (LS2) will derive total spectral absorption and backscattering coefficients, and their corresponding nonwater coefficients, from OCI- derived remote sensing reflectance. The first partitioning model (ANW) will determine the phytoplankton and nonphytoplankton absorption components from the LS2-derived nonwater absorption coefficient, and the second model (ADG) will further partition the ANW-derived nonphytoplankton absorption coefficient to derive the nonalgal particulate and CDOM components. A major advantage of LS2 is that the key equations linking reflectance to IOPs were developed from radiative transfer simulations regardless of light wavelength and with no assumptions about the spectral shapes of IOPs, hence the model provides independent solutions at any arbitrary light wavelength. Similarly, the ANW and ADG models relax restrictive assumptions about spectral shapes of component absorption coefficients. The overall 3SAA approach provides a suitable framework to optimize performance of each model independently, and to quantify uncertainties associated with individual component models as well as the entire sequence of models.

We will improve the LS2 model by developing a refined neural network algorithm commensurate with OCI capabilities to obtain the hyperspectral diffuse attenuation coefficient of irradiance. We will also refine our previous and develop new absorption partitioning models to optimize their performance with hyperspectral input data including the UV spectral range. These tasks will yield the best-performing sequence of 3SAA component models (LS2, ANW, and ADG) for applications with PACE OCI measurements, including the UV range. The performance of the entire 3SAA and its component models will be evaluated with quantified uncertainties provided for all data products. We will provide an implementable algorithm pre-launch and a methodology for post-launch algorithm maintenance and data product validation activities. On these tasks we will collaborate with both the PACE Science and Applications Team and the NASA Ocean Biology Processing Group. Strong evidence from our earlier studies indicates that the proposed 3SAA approach will advance the capabilities for estimating hyperspectral IOPs from measurements with PACE OCI, thus reducing the risk of failing to achieve mission goals related to the generation of IOP products and advancing the science enabled by these products.

As members of the PACE Science Team we propose to pursue analytical and theoretical studies as part of the measurement suite area "Inherent Optical Properties (IOPs) of the Ocean". Our goal is to improve field measurements of particulate absorption coefficients and remote sensing estimates of absorption coefficients of phytoplankton and non-phytoplankton components and particulate carbon pools associated with these components. The main objectives are to: (1) Develop a protocol and quantify the uncertainties of a new filter-pad approach and the existing filter-pad methods to measuring the particulate absorption coefficient; (2) Develop a model to partition the absorption coefficient of seawater into phytoplankton, non-algal particulate (NAP), and colored dissolved organic matter (CDOM) components with a key novel aspect of separating NAP from CDOM; and (3) Conduct a pilot study of the relationship between NAP absorption and NAP organic carbon to enable a capability for remote sensing of carbon pools associated with separate phytoplankton and non-phytoplankton components. The overall approach to address these objectives will be based primarily on the analysis of existing laboratory and field data, but will also encompass a combination of limited number of laboratory and field measurements to collect new data, and the application of remote sensing data from high spectral resolution sensor HICO. With regard to Objective 1 we will examine the filter-pad methods for determining the absorption coefficient of particles with high spectral resolution(~ 1 nm) over a broad spectral range from UV through NIR; specifically the traditional transmittance (T) and transmittance-reflectance (T-R) methods as well as the inside-sphere (IS) method which is the most recent refinement with the filter placed inside an integrating sphere. The IS method offers several advantages over the T and T-R methods, leading to improved accuracy and precision of absorption measurements. We will determine complete protocols including new optimal correction algorithms for pathlength amplification factor (which is the main source of uncertainty) and will quantify uncertainties for all three methods, benefiting the interpretation of historical data and acquisition of future data of particulate absorption. We anticipate that the IS approach will serve as the new recommended (preferable) method for measurements of particle absorption. With regard to Objective 2 we will develop a model to provide for the first time a capability for estimating the three major absorption components separately (phytoplankton, NAP, and CDOM) from the total absorption coefficient of seawater, which is derivable from remote sensing. In this development, we will use the existing quality-verified field data of absorption coefficients from various regions of the world's ocean and will expand the approach that has been successful for partitioning the absorption coefficient into phytoplankton and non-phytoplankton (NAP+CDOM combined) components (Zheng and Stramski 2013). The significance of the proposed partitioning model is associated with relationships between the component absorption coefficients and biogeochemical stocks, such as DOC, particulate non-algal and phytoplankton carbon, chlorophyll-a, as well as phytoplankton community structure and primary productivity. As a prototyping activity (Objective 3) we will examine one such unexplored link, specifically the relationship between the NAP absorption and NAP organic carbon, which will provide a basis for estimating separate pools of phytoplankton and non-algal organic carbon from remote sensing. This project will create new and advance existing algorithms for deriving ocean color data products, in particular IOPs and carbon stocks associated with separate phytoplankton and non-phytoplankton components, which will contribute to scientific goals of PACE mission to understand ocean carbon cycling and ecology.