Polarimetric calibration of the multi-angle imager for aerosols (MAIA)

Harten, Gerard van; Davis, A. B.; Diner, David J.; Bailey, Taryn; Brageot, E.; Bruegge, Carol J.; Hancock, B.; Hutchinson, Leina; Manatt, K.; Patel, Saagar; Pearson, John C.; Rheingans, B. E.; Sandhu, J. S.; Shelton, John P.; Steffy, Amanda; Warner, Daniel; Werne, Thomas A.

doi:10.1117/12.2600345

Cited by 1 publication

(1 citation statement)

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“…Satellite-borne spectroradiometers in particular have substantially advanced the way we view our home planet, and their information content will increase in the future as the technology evolves from multispectral to hyperspectral capabilities. Multi-angle polarimeters (MAPs), such as the Polarization and Directionality of the Earth's Reflectance (POLDER) (Deschamps et al, 1994), Airborne Multi-angle Spectro-Polarimetric Imager (AirMSPI) (Diner et al, 2013), Spectro-polarimeter for Planetary EXploration (SPEX) (Smit et al, 2019), Research Scanning Polarimeter (RSP) (Cairns et al, 2003), Multi-viewing Multi-Channel Multi-Polarization Imager (3MI) (Fougnie et al, 2018) and Multi-Angle Imager for Aerosols (MAIA) (Van Harten et al, 2021), have even greater information content compared to other existing single-viewing angle spectroradiometers, such as the MODerate Resolution Imaging Spectrometer (MODIS), Visible Infrared Imaging Radiometer Suite (VI-IRS), and Ocean and Land Color Instrument (OLCI), owing to their ability to perform measurements at multiple viewing angles and different polarimetric states (Dubovik et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of three bio-optical models in aerosol and ocean color joint retrievals

Hannadige,

Zhai,

Gao

et al. 2023

Atmos. Meas. Tech.

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Abstract. Multi-angle polarimeters (MAPs) are powerful instruments to perform remote sensing of the environment. Joint retrieval algorithms of aerosols and ocean color have been developed to extract the rich information content of MAPs. These are optimization algorithms that fit the sensor measurements with forward models, which include radiative transfer simulations of the coupled atmosphere and ocean systems (CAOSs). The forward model consists of sub-models to represent the optics of the atmosphere, ocean water surface and ocean body. The representativeness of these models for observed scenes and the number of retrieval parameters are important for retrieval success. In this study, we have evaluated the impact of three different ocean bio-optical models with one, three and seven optimization parameters on the accuracy of joint retrieval algorithms of MAPs. The Multi-Angular Polarimetric Ocean coLor (MAPOL) joint retrieval algorithm was used to process data from the airborne Research Scanning Polarimeter (RSP) instrument acquired in different field campaigns. We performed ensemble retrievals along three RSP legs to evaluate the applicability of bio-optical models in geographically varying water of clear to turbid conditions. The average differences between the MAPOL aerosol optical depth (AOD) and spectral remote sensing reflectance (Rrs(λ)) retrievals and the MODerate resolution Imaging Spectroradiometer (MODIS) products were also reported. We studied the distribution of retrieval cost function values obtained for the three bio-optical models. For the one-parameter model, the spread of retrieval cost function values is narrow regardless of the type of water even if it fails to converge over coastal water. For the three- and seven-parameter models, the retrieval cost function distribution is water type dependent, showing the widest distribution over clear, open water. This suggests that caution should be used when using the spread of the cost function distribution to represent the retrieval uncertainty. We observed that the three- and seven-parameter models have similar MAP retrieval performances in all cases, though they are prone to converge at local minima over open-ocean water. It is necessary to develop a screening algorithm to divide open and coastal water before performing MAP retrievals. Given the computational efficiency and the algorithm stability requirements, we recommend the three-parameter bio-optical model as the coastal-water bio-optical model for future MAPOL studies. This study provides important practical guides on the joint retrieval algorithm development for current and future satellite missions such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission and ESA's Meteorological Operational-Second Generation (MetOp-SG) mission.

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