An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign

Weizhen Hou; Jun Wang; Xiaoguang Xu; Jeffrey S Reid; Scott J Janz; James W Leitch

doi:10.1016/j.jqsrt.2020.107161

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An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign

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An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign

Weizhen Hou, Jun Wang, Xiaoguang Xu, Jeffrey S Reid, Scott J Janz and James W Leitch

Journal of quantitative spectroscopy & radiative transfer, Vol.253, p.107161

09/2020

DOI: 10.1016/j.jqsrt.2020.107161

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Abstract

•An algorithm is prototyped for TEMPO to retrieve aerosols & surfaces simultaneously.•The prototyping is implemented by applying GEO-TASO data in KORUS-AQ to mimic TEMPO.•Spectral AOD retrievals from GEO-TASO are validated with AERONET data.•Principal components of surface reflectance are retrieved iteratively together with AOD.•Next-step applications of this algorithm for TEMPO instruments are discussed. This paper describes the third part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface spectral reflectance from GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO) instrument. Since the algorithm is designed for future hyperspectral and geostationary satellite sensors, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), it is applied to GEO-TASO data measured over the same area by different flights as part of the Korea-United Stated Air Quality Study (KORUS-AQ) field campaign in 2016. While GEO-TASO has a spectral sampling interval of ~0.28 nm in the visible, its data is thinned through a band selection approach with consideration of atmospheric transmittance and different surface types, which yields 20 common spectral bands to be used by the algorithm. The algorithm starts with 4 common principal components (PCs) for surface spectral reflectance extracted from various spectral libraries; constraints of surface reflectance and aerosol model parameters are obtained respectively from k-means clustering analysis of the Rayleigh-corrected GEO-TASO spectra and AERONET data. The algorithm then proceeds iteratively with an optimal estimation approach to update PCs and retrieve aerosol optical depth (AOD) from GEO-TASO measured spectra until state vector converges. The comparison of AODs between GEO-TASO retrievals (y) and 7 AERONET (x) sites reveals that the iterative updates of surface PCs (and so surface reflectance) improve the inversions of fine-mode AOD, fine-mode fraction of AOD, Ångström exponent, and AOD at all (440, 550, 550, 675 nm) wavelengths. At 440 nm, the linear fitting equation, the Pearson correlation coefficient (R2), and mean absolute error are improved respectively from y = 0.72x + 0.11, 0.53, and 0.05 (without update of PCs) to y = 1.055x + 0.01, 0.76, and 0.033. Future work is to prepare the algorithm for TEMPO that carries an enhanced version of GEO-TASO instrument.

Hyperspectral geostationary remote sensing

Optimal-estimation inversion

TEMPO

GEO-TASO

KORUS-AQ

Details

Title: Subtitle: An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign
Creators: Weizhen Hou - Department of Chemical and Biochemical Engineering, Center for Global & Regional Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, IA 52242, United States
Jun Wang - Department of Chemical and Biochemical Engineering, Center for Global & Regional Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, IA 52242, United States
Xiaoguang Xu - Department of Chemical and Biochemical Engineering, Center for Global & Regional Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, IA 52242, United States
Jeffrey S Reid - Marine Meteorology Division, Naval Research Laboratory, 7 Grace Hopper Ave, Stop 2, Monterey, CA 93943, United States
Scott J Janz - Atmospheric Chemistry and Dynamics, NASA Goddard Space Flight Center, Code 614, 8800 Greenbelt Rd., Greenbelt, MD 20771, United States
James W Leitch - Ball Aerospace & Technologies, 10 Longs Peak Drive, Broomfield, CO 80021, United States
Resource Type: Journal article
Publication Details: Journal of quantitative spectroscopy & radiative transfer, Vol.253, p.107161
DOI: 10.1016/j.jqsrt.2020.107161
ISSN: 0022-4073
eISSN: 1879-1352
Publisher: Elsevier Ltd
Grant note: DOI: 10.13039/100000006, name: Office of Naval Research; DOI: 10.13039/100000104, name: National Aeronautics and Space Administration
Language: English
Date published: 09/2020
Academic Unit: Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
Record Identifier: 9984066100902771

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