Jumbo Content

ROSES Proposals

Next Generation Algorithms Based on PACE Capabilities to Obtain Inherent Optical Properties of Seawater Associated with Phytoplankton, Nonalgal Particles, and Colored Dissolved Organic Matter

PI: Dariusz Stramski - Scripps Institution of Oceanography, University of California San Diego
Co-I: Rick A. Reynolds (Scripps Institution of Oceanography, University of California San Diego)
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.