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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 34581

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Dr. Oleg Dubovik

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Director de Recherche, CNRS, University of Lille-1, 59655 Villeneuve D'ascq, CEDEX, France
Interests: atmospheric remote sensing; inversion algorithm; optical diagnostic; light scattering; atmospheric radiative transfer; aerosol retrieval; inverse modeling; numerical inversion; statistical estimation theory
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Dr. Feng Xu

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Guest Editor

School of Meteorology, The University of Oklahoma, Norman, OK, USA
Interests: aerosol and cloud remote sensing; atmospheric radiation; light scattering by small particles
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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

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Keywords

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

Published Papers (10 papers)

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17 pages, 681 KiB

Open AccessArticle

Spatial Retrievals of Atmospheric Carbon Dioxide from Satellite Observations

by Jonathan Hobbs, Matthias Katzfuss, Daniel Zilber, Jenný Brynjarsdóttir, Anirban Mondal and Veronica Berrocal

Remote Sens. 2021, 13(4), 571; https://doi.org/10.3390/rs13040571 - 05 Feb 2021

Cited by 2 | Viewed by 2499

Abstract

Modern remote-sensing retrievals often invoke a Bayesian approach to infer atmospheric properties from observed radiances. In this approach, plausible mean states and variability for the quantities of interest are encoded in a prior distribution. Recent developments have devised prior assumptions for the correlation among atmospheric constituents and across observing locations. This work formulates a spatial statistical framework for simultaneous multi-footprint retrievals of carbon dioxide (CO₂) with application to the Orbiting Carbon Observatory-2/3 (OCO-2/3). Formally, the retrieval state vector is extended to include atmospheric and surface conditions at many footprints in a small region, and a prior distribution that assumes spatial correlation across these locations is assumed. This spatial prior allows the length-scale, or range, of spatial correlation to vary between different elements of the state vector. Various single- and multi-footprint retrievals are compared in a simulation study. A spatial prior that also includes relatively large prior variances for CO₂ results in posterior inferences that most accurately represent the true state and that reduce the correlation in retrieval error across locations. Full article

(This article belongs to the Special Issue Advances of Remote Sensing Inversion)

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15 pages, 2256 KiB

Open AccessArticle

A Fast Retrieval of Cloud Parameters Using a Triplet of Wavelengths of Oxygen Dimer Band around 477 nm

by Haklim Choi, Xiong Liu, Gonzalo Gonzalez Abad, Jongjin Seo, Kwang-Mog Lee and Jhoon Kim

Remote Sens. 2021, 13(1), 152; https://doi.org/10.3390/rs13010152 - 05 Jan 2021

Cited by 3 | Viewed by 2233

Abstract

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O₂–O₂ absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O₂–O₂ at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O₂ A-band. There was good agreement between the results, despite small discrepancies for low clouds. Full article

(This article belongs to the Special Issue Advances of Remote Sensing Inversion)

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29 pages, 5170 KiB

Open AccessArticle

Model Selection in Atmospheric Remote Sensing with an Application to Aerosol Retrieval from DSCOVR/EPIC, Part 1: Theory

by Sruthy Sasi, Vijay Natraj, Víctor Molina García, Dmitry S. Efremenko, Diego Loyola and Adrian Doicu

Remote Sens. 2020, 12(22), 3724; https://doi.org/10.3390/rs12223724 - 12 Nov 2020

Cited by 8 | Viewed by 1683

Abstract

The retrieval of aerosol and cloud properties such as their optical thickness and/or layer/top height requires the selection of a model that describes their microphysical properties. We demonstrate that, if there is not enough information for an appropriate microphysical model selection, the solution’s accuracy can be improved if the model uncertainty is taken into account and appropriately quantified. For this purpose, we design a retrieval algorithm accounting for the uncertainty in model selection. The algorithm is based on (i) the computation of each model solution using the iteratively regularized Gauss–Newton method, (ii) the linearization of the forward model around the solution, and (iii) the maximum marginal likelihood estimation and the generalized cross-validation to estimate the optimal model. The algorithm is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements corresponding to the Earth Polychromatic Imaging Camera (EPIC) instrument onboard the Deep Space Climate Observatory (DSCOVR) satellite. Our numerical simulations show that the heuristic approach based on the thesolution minimizing the residual, which is frequently used in literature, is completely unrealistic when both the aerosol model and surface albedo are unknown. Full article

(This article belongs to the Special Issue Advances of Remote Sensing Inversion)

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17 pages, 412 KiB

Open AccessArticle

Model Selection in Atmospheric Remote Sensing with Application to Aerosol Retrieval from DSCOVR/EPIC. Part 2: Numerical Analysis

by Sruthy Sasi, Vijay Natraj, Víctor Molina García, Dmitry S. Efremenko, Diego Loyola and Adrian Doicu

Remote Sens. 2020, 12(21), 3656; https://doi.org/10.3390/rs12213656 - 07 Nov 2020

Cited by 5 | Viewed by 2245

Abstract

An algorithm for retrieving aerosol parameters by taking into account the uncertainty in aerosol model selection is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements from the EPIC sensor onboard the Deep Space Climate Observatory. The synthetic measurements are generated using aerosol models derived from AERONET measurements at different sites, while other commonly used aerosol models, such as OPAC, GOCART, OMI, and MODIS databases are used in the retrieval. The numerical analysis is focused on the estimation of retrieval errors when the true aerosol model is unknown. We found that the best aerosol model is the one with a value of the asymmetry parameter and an angular variation of the phase function around the viewing direction that is close to the values corresponding to the reference aerosol model. Full article

(This article belongs to the Special Issue Advances of Remote Sensing Inversion)

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20 pages, 7474 KiB

Open AccessArticle

Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors

by Chong Li, Jing Li, Oleg Dubovik, Zhao-Cheng Zeng and Yuk L. Yung

Remote Sens. 2020, 12(9), 1524; https://doi.org/10.3390/rs12091524 - 11 May 2020

Cited by 21 | Viewed by 4287

Abstract

When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties. Full article

(This article belongs to the Special Issue Advances of Remote Sensing Inversion)

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22 pages, 3716 KiB

Open AccessArticle

Comparison of Regional Simulation of Biospheric CO₂ Flux from the Updated Version of CarbonTracker Asia with FLUXCOM and Other Inversions over Asia

by Samuel Takele Kenea, Lev D. Labzovskii, Tae-Young Goo, Shanlan Li, Young-Suk Oh and Young-Hwa Byun

Remote Sens. 2020, 12(1), 145; https://doi.org/10.3390/rs12010145 - 01 Jan 2020

Cited by 7 | Viewed by 3284

Abstract

There are still large uncertainties in the estimates of net ecosystem exchange of CO₂ (NEE) with atmosphere in Asia, particularly in the boreal and eastern part of temperate Asia. To understand these uncertainties, we assessed the CarbonTracker Asia (CTA2017) estimates of the spatial and temporal distributions of NEE through a comparison with FLUXCOM and the global inversion models from the Copernicus Atmospheric Monitoring Service (CAMS), Monitoring Atmospheric Composition and Climate (MACC), and Jena CarboScope in Asia, as well as examining the impact of the nesting approach on the optimized NEE flux during the 2001–2013 period. The long-term mean carbon uptake is reduced in Asia, which is −0.32 ± 0.22 PgC yr⁻¹, whereas −0.58 ± 0.26 PgC yr⁻¹ is shown from CT2017 (CarbonTracker global). The domain aggregated mean carbon uptake from CTA2017 is found to be lower by 23.8%, 44.8%, and 60.5% than CAMS, MACC, and Jena CarboScope, respectively. For example, both CTA2017 and CT2017 models captured the interannual variability (IAV) of the NEE flux with a different magnitude and this leads to divergent annual aggregated results. Differences in the estimated interannual variability of NEE in response to El Niño–Southern Oscillation (ENSO) may result from differences in the transport model resolutions. These inverse models’ results have a substantial difference compared to FLUXCOM, which was found to be −5.54 PgC yr⁻¹. On the one hand, we showed that the large NEE discrepancies between both inversion models and FLUXCOM stem mostly from the tropical forests. On the other hand, CTA2017 exhibits a slightly better correlation with FLUXCOM over grass/shrub, fields/woods/savanna, and mixed forest than CT2017. The land cover inconsistency between CTA2017 and FLUXCOM is therefore one driver of the discrepancy in the NEE estimates. The diurnal averaged NEE flux between CTA2017 and FLUXCOM exhibits better agreement during the carbon uptake period than the carbon release period. Both CTA2017 and CT2017 revealed that the overall spatial patterns of the carbon sink and source are similar, but the magnitude varied with seasons and ecosystem types, which is mainly attributed to differences in the transport model resolutions. Our findings indicate that substantial inconsistencies in the inversions and FLUXCOM mainly emerge during the carbon uptake period and over tropical forests. The main problems are underrepresentation of FLUXCOM NEE estimates by limited eddy covariance flux measurements, the role of CO₂ emissions from land use change not accounted for by FLUXCOM, sparseness of surface observations of CO₂ concentrations used by the assimilation systems, and land cover inconsistency. This suggested that further scrutiny on the FLUXCOM and inverse estimates is most likely required. Such efforts will reduce inconsistencies across various NEE estimates over Asia, thus mitigating ecosystem-driven errors that propagate the global carbon budget. Moreover, this work also recommends further investigation on how the changes/updates made in CarbonTracker affect the interannual variability of the aggregate and spatial pattern of NEE flux in response to the ENSO effect over the region of interest. Full article

(This article belongs to the Special Issue Advances of Remote Sensing Inversion)

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19 pages, 4352 KiB

Open AccessArticle

Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements

by Cheng Fan, Guangliang Fu, Antonio Di Noia, Martijn Smit, Jeroen H.H. Rietjens, Richard A. Ferrare, Sharon Burton, Zhengqiang Li and Otto P. Hasekamp

Remote Sens. 2019, 11(23), 2877; https://doi.org/10.3390/rs11232877 - 03 Dec 2019

Cited by 22 | Viewed by 4227

Abstract

For aerosol retrieval from multi-angle polarimetric (MAP) measurements over the ocean it is important to accurately account for the contribution of the ocean-body to the top-of-atmosphere signal, especially for wavelengths <500 nm. Performing online radiative transfer calculations in the coupled atmosphere ocean system is too time consuming for operational retrieval algorithms. Therefore, mostly lookup-tables of the ocean body reflection matrix are used to represent the lower boundary in an atmospheric radiative transfer model. For hyperspectral measurements such as those from Spectro-Polarimeter for Planetary Exploration (SPEXone) on the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, also the use of look-up tables is unfeasible because they will become too big. In this paper, we propose a new method for aerosol retrieval over ocean from MAP measurements using a neural network (NN) to model the ocean body reflection matrix. We apply the NN approach to synthetic SPEXone measurements and also to real data collected by SPEX airborne during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We conclude that the NN approach is well capable for aerosol retrievals over ocean, introducing no significant error on the retrieved aerosol properties Full article

(This article belongs to the Special Issue Advances of Remote Sensing Inversion)

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51 pages, 6087 KiB

Open AccessFeature PaperArticle

A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing

by Feng Xu, David J. Diner, Oleg Dubovik and Yoav Schechner

Remote Sens. 2019, 11(7), 746; https://doi.org/10.3390/rs11070746 - 27 Mar 2019

Cited by 26 | Viewed by 4513

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 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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32 pages, 1938 KiB

Open AccessArticle

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

by Gregory L. Schuster, W. Reed Espinosa, Luke D. Ziemba, Andreas J. Beyersdorf, Adriana Rocha-Lima, Bruce E. Anderson, Jose V. Martins, Oleg Dubovik, Fabrice Ducos, David Fuertes, Tatyana Lapyonok, Michael Shook, Yevgeny Derimian and Richard H. Moore

Remote Sens. 2019, 11(5), 498; https://doi.org/10.3390/rs11050498 - 01 Mar 2019

Cited by 20 | Viewed by 4371

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 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

^{\circ}

to 77

^{\circ}

. 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_{\circ} = 50^{\circ}

); 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_{\circ}

) 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)

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13 pages, 6017 KiB

Open AccessLetter

Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter

by Shikuan Jin, Yingying Ma, Ming Zhang, Wei Gong, Oleg Dubovik, Boming Liu, Yifan Shi and Changlan Yang

Remote Sens. 2019, 11(19), 2218; https://doi.org/10.3390/rs11192218 - 23 Sep 2019

Cited by 20 | Viewed by 2867

Abstract

Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products are used worldwide for their reliable accuracy. However, the aerosol optical depth (AOD) usually retrieved by the operational dark target (DT) algorithm of MODIS has been missing for most of the urban regions in Central China. This was due to a high surface reflectance and heavy aerosol loading, especially in winter, when a high cloud cover fraction and the frequent occurrence of haze events reduce the number of effective satellite observations. The retrieval of the AOD from limited satellite data is much needed and important for further aerosol investigations. In this paper, we propose an improved AOD retrieval method for 500 m MODIS data, which is based on an extended surface reflectance estimation scheme and dynamic aerosol models derived from ground-based sun-photometric observations. This improved method was applied to retrieve AOD during heavy aerosol loading and effectively complements the scarcity of AOD in correspondence with urban surface of a higher spatial resolution. The validation results showed that the retrieved AOD was consistent with MODIS DT AOD (R = ~0.87; RMSE = ~0.11) and ground measurements (R = ~0.89; RMSE = ~0.15) from both the Terra and the Aqua satellite. The method can be easily applied to different urban environments affected by air pollution and contributes to the research on aerosol. Full article

(This article belongs to the Special Issue Advances of Remote Sensing Inversion)

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