Machine learning based aerosol and ocean color joint retrieval algorithm for multiangle polarimeters over coastal waters

dc.contributor.authorAryal, Kamal
dc.contributor.authorZhai, Peng-Wang
dc.contributor.authorGao, Meng
dc.contributor.authorFranz, Bryan A.
dc.contributor.authorKnobelspiesse, Kirk
dc.contributor.authorHu, Yongxiang
dc.date.accessioned2025-04-01T14:54:49Z
dc.date.available2025-04-01T14:54:49Z
dc.date.issued2024-08-05
dc.description.abstractNASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, recently launched in February 2024, carries two multiangle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and SRON Spectropolarimeter for Planetary Exploration One (SPEXone). Measurements from these MAPs will greatly advance ocean ecosystem and aerosol studies as their measurements contain rich information on the microphysical properties of aerosols and hydrosols. The Multi-Angular Polarimetric Ocean coLor (MAPOL) joint retrieval algorithm has been developed to retrieve aerosol and ocean color information, which uses a vector radiative transfer (RT) model as the forward model. The RT model is computationally expensive, which makes processing a large amount of data challenging. FastMAPOL was developed to expedite retrieval using neural networks to replace the RT forward models. As a prototype study, FastMAPOL was initially limited to open ocean applications where the ocean Inherent Optical Properties (IOPs) were parameterized in terms of one parameter: chlorophyll-a concentration (Chla). In this study we further expand the FastMAPOL joint retrieval algorithm to incorporate NN based forward models for coastal waters, which use multi-parameter bio-optical models. In addition, aerosols are represented by six components, i.e., fine mode non absorbing insoluble (FNAI), brown carbon (BrC), black carbon (BC), fine mode non absorbing soluble (FNAS), sea salt (SS) and non-spherical dust (Dust). Sea salt and dust are coarse mode aerosols, while the other components are fine mode. The sizes and spectral refractive indices are fixed for each aerosol component, while their abundances are retrievable. The multi-parameter bio-optical model and aerosol components are chosen to represent the coastal marine environment. The retrieval algorithm is applied to synthetic measurements in three different configurations of MAPs in the PACE mission: HARP2 observations only, SPEXone observations only and combined HARP2 and SPEXone observations. The retrieval results from synthetic measurements show that for aerosol retrieval the SPEXone-only configuration works equally well with the HAPR2-only configuration. On the other hand, for ocean color retrieval the SPEXone instrument provides better information due to its larger spectral coverage. For the surface parameters (wind speed), HARP2 measurements provide better information due to its wide field of view. Combined measurement configuration HARP2+SPEXone performed the best to retrieve all aerosol, ocean color, and surface parameters. We also studied the impact of sun glint to aerosol and ocean color retrievals. The retrieval test revealed that wind speed and absorbing aerosol retrieval improves significantly when including measurements at glint geometries. Furthermore, the retrieval algorithm is equipped with modules for atmospheric correction and bidirectional reflectance distribution (BRDF) correction to obtain the remote sensing reflectance, which enables ocean biogeochemistry studies using the PACE polarimeter data.
dc.description.sponsorshipNational Aeronautics and Space Administration (80NSSC20M0227).
dc.description.urihttps://opg.optica.org/oe/abstract.cfm?uri=oe-32-17-29921
dc.format.extent22 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2lwju-erjv
dc.identifier.citationAryal, Kamal, Peng-Wang Zhai, Meng Gao, Bryan A. Franz, Kirk Knobelspiesse, and Yongxiang Hu. "Machine Learning Based Aerosol and Ocean Color Joint Retrieval Algorithm for Multiangle Polarimeters over Coastal Waters." Optics Express 32, no. 17 (August 12, 2024): 29921-29942. https://doi.org/10.1364/OE.522794.
dc.identifier.urihttps://doi.org/10.1364/OE.522794
dc.identifier.urihttp://hdl.handle.net/11603/37828
dc.language.isoen_US
dc.publisherOptica Publishing Group
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rightsPublic Domain
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/
dc.subjectMachine learning
dc.subjectRemote sensing
dc.subjectOptical properties
dc.subjectAtmospheric correction
dc.subjectNear infrared
dc.subjectOcean color
dc.subjectAtmospheric and Ocean Optics Group
dc.titleMachine learning based aerosol and ocean color joint retrieval algorithm for multiangle polarimeters over coastal waters
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0003-0871-8650
dcterms.creatorhttps://orcid.org/0000-0003-4695-5200

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