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Special Issue "Advances of Remote Sensing Inversion"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: 30 November 2019

Special Issue Editors

Guest Editor
Dr. Oleg Dubovik

Director de Recherche, CNRS, University of Lille-1, 59655 Villeneuve D'ascq Cedex, France
Website | E-Mail
Phone: +33-3-20-33-59-83
Interests: Atmospheric Remote Sensing; Inversion Algorithm; Optical Diagnostic; Light Scattering; Atmospheric Radiative Transfer; Aerosol Retrieval; Inverse Modeling; Numerical Inversion; Statistical Estimation Theory
Guest Editor
Dr. Feng Xu

Jet Propulsion Laboratory, National Aeronautics and Space Administration, Pasadena, CA 91109, USA
Website | E-Mail
Phone: +1 818-393-8632
Interests: Atmospheric radiative transfer; Remote sensing of aerosol and lower boundary properties, Light scattering by aerosol and cloud particles

Special Issue Information

Dear Colleagues,

Remote sensing is a major tool for studying the atmosphere - surface system of the Earth and other planets. During the past five decades radiation measurements from satellites, aircraft and the ground have been successfully employed for characterizing radiative properties of land, ocean, atmospheric gases, aerosols, clouds, etc. One of the challenges of the remote sensing approaches is the development of a reliable procedure for inversion of the observations.  The inversion is particularly crucial and demanding for interpreting highly complex measurements wherein many unknowns must be determined simultaneously. Therefore, the deployment and evolution of remote sensing with various observational capabilities should inevitably be coupled with significant investments in the inverse algorithm developments. This special issue is dedicated to unite publications emphasizing the various aspects of numerical inversion in diverse remote sensing applications. The contributions are expected to address such important attributes of inversion as optimum accounting for errors in the data and inverting multi-source data with different levels of accuracy, utilizing a priori information and ancillary data, synergy retrievals using complimentary measurements or modeling considerations of different nature, inverse modeling and data assimilation, retrieval errors estimations, clarifying the potential of different mathematical inverse and other operations and methodologies, accelerating and optimizing performance of existing formal inverse operations, comprehensive validation of retrieval results, etc. The development of forward models for light propagation and radiation in a complex media are also welcome provided they open opportunities for establishing improved retrieval approaches.

Thus, in this Special Issue, we encourage submissions focusing on various aspects of inversion in diverse Remote Sensing applications, including, but not limited to:

  • Satellite, airborne and ground-based remote sensing of atmosphere;
  • Characterization of aerosol, cloud and atmospheric gases properties;
  • Land and ocean surface characterization and atmospheric correction;
  • Data assimilation and fusion of modeling and observations.

Dr. Oleg Dubovik
Dr. Feng Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Atmosphere Remote Sensing
  • Atmospheric correction
  • Numerical Inversion
  • Retrieval techniques
  • Light scattering
  • Inverse Modeling
  • Assimilation

Published Papers (2 papers)

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Research

Open AccessFeature PaperArticle
A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing
Remote Sens. 2019, 11(7), 746; https://doi.org/10.3390/rs11070746
Received: 8 January 2019 / Revised: 2 March 2019 / Accepted: 19 March 2019 / Published: 27 March 2019
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Abstract
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 [...] Read 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. Full article
(This article belongs to the Special Issue Advances of Remote Sensing Inversion)
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Graphical abstract

Open AccessArticle
A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms
Remote Sens. 2019, 11(5), 498; https://doi.org/10.3390/rs11050498
Received: 31 January 2019 / Revised: 19 February 2019 / Accepted: 23 February 2019 / Published: 1 March 2019
PDF Full-text (1938 KB) | HTML Full-text | XML Full-text
Abstract
We have developed a method for evaluating the fidelity of the Aerosol Robotic Network (AERONET) retrieval algorithms by mimicking atmospheric extinction and radiance measurements in a laboratory experiment. This enables radiometric retrievals that use the same sampling volumes, relative humidities, and particle size [...] Read more.
We have developed a method for evaluating the fidelity of the Aerosol Robotic Network (AERONET) retrieval algorithms by mimicking atmospheric extinction and radiance measurements in a laboratory experiment. This enables radiometric retrievals that use the same sampling volumes, relative humidities, and particle size ranges as observed by other in situ instrumentation in the experiment. We use three Cavity Attenuated Phase Shift (CAPS) monitors for extinction and University of Maryland Baltimore County’s (UMBC) three-wavelength Polarized Imaging Nephelometer (PI-Neph) for angular scattering measurements. We subsample the PI-Neph radiance measurements to angles that correspond to AERONET almucantar scans, with simulated solar zenith angles ranging from 50 to 77 . These measurements are then used as input to the Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm, which retrieves size distributions, complex refractive indices, single-scatter albedos, and bistatic LiDAR ratios for the in situ samples. We obtained retrievals with residuals less than 8% for about 90 samples. Samples were alternately dried or humidified, and size distributions were limited to diameters of less than 1.0 or 2.5 μ m by using a cyclone. The single-scatter albedo at 532 nm for these samples ranged from 0.59 to 1.00 when computed with CAPS extinction and Particle Soot Absorption Photometer (PSAP) absorption measurements. The GRASP retrieval provided single-scatter albedos that are highly correlated with the in situ single-scatter albedos, and the correlation coefficients ranged from 0.916 to 0.976, depending upon the simulated solar zenith angle. The GRASP single-scatter albedos exhibited an average absolute bias of +0.023–0.026 with respect to the extinction and absorption measurements for the entire dataset. We also compared the GRASP size distributions to aerodynamic particle size measurements, using densities and aerodynamic shape factors that produce extinctions consistent with our CAPS measurements. The GRASP effective radii are highly correlated (R = 0.80) and biased under the corrected aerodynamic effective radii by 1.3% (for a simulated solar zenith angle of θ = 50 ); the effective variance indicated a correlation of R = 0.51 and a relative bias of 280%. Finally, our apparatus was not capable of measuring backscatter LiDAR ratios, so we measured bistatic LiDAR ratios at a scattering angle of 173 degrees. The GRASP bistatic LiDAR ratios had correlations of 0.71 to 0.86 (depending upon simulated θ ) with respect to in situ measurements, positive relative biases of 2–10%, and average absolute biases of 1.8–7.9 sr. Full article
(This article belongs to the Special Issue Advances of Remote Sensing Inversion)
Figures

Graphical abstract

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