Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning

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dc.contributor.authorGao, Meng
dc.contributor.authorKnobelspiesse, Kirk
dc.contributor.authorFranz, Bryan A.
dc.contributor.authorZhai, Peng-Wang
dc.contributor.authorMartins, Vanderlei
dc.contributor.authorBurton, Sharon P.
dc.contributor.authorCairns, Brian
dc.contributor.authorFerrare, Richard
dc.contributor.authorFenn, Marta A.
dc.contributor.authorHasekamp, Otto
dc.contributor.authorHu, Yongxiang
dc.contributor.authorIbrahim, Amir
dc.contributor.authorSayer, Andrew
dc.contributor.authorWerdell, P. Jeremy
dc.contributor.authorXu, Xiaoguang
dc.date.accessioned2022-02-23T14:58:12Z
dc.date.available2022-02-23T14:58:12Z
dc.date.issued2021-12-14
dc.description.abstractRemote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90–120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20–30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.en_US
dc.description.sponsorshipThe authors would like to thank the ACEPOL science team for conducting the field campaign and HARP and HSRL teams for providing the data. We thank the NASA Ocean Biology Processing Group (OBPG) system team for supports in the high-performance computing (HPC). We appreciate the constructive discussions with Feng Xu, Jason Xuan and Sean Foley.en_US
dc.description.urihttps://www.frontiersin.org/articles/10.3389/frsen.2021.757832/fullen_US
dc.format.extent18 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2p9yy-lhcu
dc.identifier.citationGao M, Knobelspiesse K, Franz BA, Zhai P-W, Martins V, Burton SP, Cairns B, Ferrare R, Fenn MA, Hasekamp O, Hu Y, Ibrahim A, Sayer AM, Werdell PJ and Xu X (2021) Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning. Front. Remote Sens. 2:757832. doi: 10.3389/frsen.2021.757832en_US
dc.identifier.urihttps://doi.org/10.3389/frsen.2021.757832
dc.identifier.urihttp://hdl.handle.net/11603/24316
dc.language.isoen_USen_US
dc.publisherFrontiersen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofUMBC GESTAR II
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.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleAdaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learningen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-4695-5200en_US
dcterms.creatorhttps://orcid.org/0000-0001-9149-1789en_US

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