Oral Session 11

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

Ocean color remote sensing over coastal waters is challenging due to the complex optical properties of water constituents and the presence of absorbing aerosols causing difficulty in atmospheric correction. Measurements from multi-angle polarimeters (MAPs) can be used to overcome these challenges as they contain rich information about optical properties of aerosols and hydrosols. NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, recently launched in February 2024, carries two MAPs: UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and SRON Spectropolarimeter for Planetary Exploration One (SPEXone). The two instruments provide complementary measurement capabilities, with HARP2 observing a wide swath at many viewing angles and four key wavelengths, while SPEXone collects hyperspectral data over a wider spectral range, but for a narrow swath at five key viewing geometries. In this meeting, we will present the results from the recently developed joint aerosol and ocean color retrieval algorithm called FastMAPOL/component (Fast Multi-Angular Polarimetric Ocean color/component). FastMAPOL/component incorporates neural network-trained forward models to expedite the retrieval process and uses multi-parameter bio-optical model along with component representation of aerosols suitable for coastal waters. It can be configured to process data from both polarimeters in PACE mission. The algorithm is equipped with modules for atmospheric correction and bidirectional reflectance distribution correction to obtain remote sensing reflectance, which enables ocean biogeochemistry studies using the PACE data. The presentation will include retrieval capabilities of different configurations (HARP2-only, SPEXone-only, HARP2+SPEXone) of polarimeters from PACE mission using synthetic and real measurements with validation results using AERONET Ocean Color measurements.

*Kamal Aryal, University of Maryland Baltimore County, USA, https://orcid.org/0000-0003-0871-8650

Pengwang Zhai, University of Maryland Baltimore County, USA

Meng Gao, NASA Goddard Space Flight Center, Ocean Ecology Laboratory, USA

Bryan A.  Franz, NASA Goddard Space Flight Center, Ocean Ecology Laboratory, USA

Kirk Knobelspiesse, NASA Goddard Space Flight Center, Ocean Ecology Laboratory, USA

Vanderlei Martins, University of Maryland Baltimore County, USA

Yongxiang Hu, NASA Langley Research Center, USA

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