An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign. (September 2020)

• Record Type: Journal Article
• Authors: Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.; Janz, Scott J.; Leitch, James W.
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 253(2020)
• Publication Date: 2020-09
• Subjects: Hyperspectral geostationary remote sensing -- Optimal-estimation inversion -- KORUS-AQ -- GEO-TASO -- TEMPO; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2020.107161
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: 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. Abstract: 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 ( R 2 ), and mean absolute error are improved respectively from y = 0.72 x + 0.11, 0.53, and 0.05 (without update of PCs) to y = 1.055 x + 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.