A correlated multi-pixel inversion approach for aerosol remote sensing

Xu, Feng; Diner, David J.; Doubovik, Oleg; Schechner, Yoav

Document type :

Article dans une revue scientifique: Article original

DOI :

10.3390/rs11070746

Permalink :

http://hdl.handle.net/20.500.12210/105100

Title :

A correlated multi-pixel inversion approach for aerosol remote sensing

Author(s) :

Xu, Feng [Auteur]
Jet Propulsion Laboratory [JPL]
Diner, David J. [Auteur]
California Institute of Technology [CALTECH]
Doubovik, Oleg [Auteur]

Laboratoire d’Optique Atmosphérique - UMR 8518 [LOA]
Schechner, Yoav [Auteur]
Department of Electrical Engineering - Technion [Haïfa] [EE-Technion]

Journal title :

Remote Sensing

Abbreviated title :

Remote Sens.

Volume number :

Publication date :

2019-04-01

ISSN :

2072-4292

English keyword(s) :

correlated aerosol inversion
radiative transfer
multiangle radiometry
polarimetry

HAL domain(s) :

Physique [physics]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Laboratoire d'Optique Atmosphérique (LOA) - UMR 8518

Submission date :

2024-01-30T11:45:53Z
2024-02-26T15:46:11Z

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Except where otherwise noted, this item's license is described as Attribution 3.0 United States

Version	Link	Modification date
2	20.500.12210/105100*	2024-02-26T15:41:51Z
1	20.500.12210/105100.1	2024-01-30T11:45:53Z

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A correlated multi-pixel inversion approach ...

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