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A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms

A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Laboratoire d’Optique Atmosphérique, CNRS/Université Lille-1, 59655 Villeneuve d’Ascq, France
Viterbi Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 746;
Received: 8 January 2019 / Revised: 2 March 2019 / Accepted: 19 March 2019 / Published: 27 March 2019
(This article belongs to the Special Issue Advances of Remote Sensing Inversion)
Aerosol retrieval algorithms used in conjunction with remote sensing are subject to ill-posedness. To mitigate non-uniqueness, extra constraints (in addition to observations) are valuable for stabilizing the inversion process. This paper focuses on the imposition of an empirical correlation constraint on the retrieved aerosol parameters. This constraint reflects the empirical dependency between different aerosol parameters, thereby reducing the number of degrees of freedom and enabling accelerated computation of the radiation fields associated with neighboring pixels. A cross-pixel constraint that capitalizes on the smooth spatial variations of aerosol properties was built into the original multi-pixel inversion approach. Here, the spatial smoothness condition is imposed on principal components (PCs) of the aerosol model, and on the corresponding PC weights, where the PCs are used to characterize departures from the mean. Mutual orthogonality and unit length of the PC vectors, as well as zero sum of the PC weights also impose stabilizing constraints on the retrieval. Capitalizing on the dependencies among aerosol parameters and the mutual orthogonality of PCs, a perturbation-based radiative transfer computation scheme is developed. It uses a few dominant PCs to capture the difference in the radiation fields across an imaged area. The approach is tested using 27 observations acquired by the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) during multiple NASA field campaigns and validated using collocated AERONET observations. In particular, aerosol optical depth, single scattering albedo, aerosol size, and refractive index are compared with AERONET aerosol reference data. Retrieval uncertainty is formulated by accounting for both instrumental errors and the effects of multiple types of constraints. View Full-Text
Keywords: correlated aerosol inversion; radiative transfer; multiangle radiometry; polarimetry correlated aerosol inversion; radiative transfer; multiangle radiometry; polarimetry
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MDPI and ACS Style

Xu, F.; Diner, D.J.; Dubovik, O.; Schechner, Y. A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing. Remote Sens. 2019, 11, 746.

AMA Style

Xu F, Diner DJ, Dubovik O, Schechner Y. A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing. Remote Sensing. 2019; 11(7):746.

Chicago/Turabian Style

Xu, Feng, David J. Diner, Oleg Dubovik, and Yoav Schechner. 2019. "A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing" Remote Sensing 11, no. 7: 746.

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