Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 490

The DOI matches its Crossref record; 177 of the 177 references it cites were found in a registry or an index.

Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives

Open access
Dubovik, Oleg, Li, Zhengqiang, Mishchenko, Michael I., Tanré, Didier, Karol, Yana, Bojkov, Bojan, Cairns, Brian, Diner, David J., Espinosa, W. Reed, Goloub, Philippe, Gu, Xingfa, Hasekamp, Otto, Hong, Jin, Hou, Weizhen, Knobelspiesse, Kirk D., Landgraf, Jochen, Li, Li, Litvinov, Pavel, Liu, Yi, Lopatin, Anton, Marbach, Thierry, Maring, Hal, Martins, Vanderlei, Meijer, Yasjka, Milinevsky, Gennadi, Mukai, Sonoyo, Parol, Frederic, Qiao, Yanli, Remer, Lorraine, Rietjens, Jeroen, Sano, Itaru, Stammes, Piet, Stamnes, Snorre, Sun, Xiaobing, Tabary, Pierre, Travis, Larry D., Waquet, Fabien, Xu, Feng, Yan, Changxiang, Yin, Dekui·2019·Journal of Quantitative Spectroscopy and Radiative Transfer

Abstract

Polarimetry is one of the most promising types of remote sensing for improved characterization of atmospheric aerosol. Indeed, aerosol particles constitute a highly variable atmospheric component characterized by a large number of parameters describing particle sizes, morphologies (including shape and internal structure), absorption and scattering properties, amounts, horizontal and vertical distribution, etc. Reliable monitoring of all these parameters is very challenging, and therefore the aerosol effects on climate and environment are considered to be among the most uncertain factors in climate and environmental research. In this regard, observations that provide both the angular distribution of the scattered atmospheric radiation as well as its polarization state at multiple wavelengths covering the UV–SWIR spectral range carry substantial implicit information on the atmospheric composition. Therefore, high expectations in improving aerosol characterization are associated with detailed passive photopolarimetric observations. The critical need to use space-borne polarimetry for global accurate monitoring of detailed aerosol properties was first articulated in the late 1980s and early 1990s. By now, several orbital instruments have already provided polarization observations from space, and a number of advanced missions are scheduled for launch in the coming years by international and national space agencies. The first and most extensive record of polarimetric imagery was provided by POLDER-I, POLDER-II, and POLDER/PARASOL multi-angle multi-spectral polarization sensors. Polarimetric observations with the POLDER-like design intended for collecting extensive multi-angular multi-spectral measurements will be provided by several instruments, such as the MAI/TG-2, CAPI/TanSat, and DPC/GF-5 sensors recently launched by the Chinese Space Agency. Instruments such as the 3MI/MetOp-SG, MAIA, SpexOne and HARP2 on PACE, POSP, SMAC, PCF, DPC–Lidar, ScanPol and MSIP/Aerosol-UA, MAP/Copernicus CO2 Monitoring, etc. are planned to be launched by different space agencies in the coming decade. The concepts of these future instruments, their technical designs, and the accompanying algorithm development have been tested intensively and analyzed using diverse airborne prototypes. Certain polarimetric capabilities have also been implemented in such satellite sensors as GOME-2/MetOp and SGLI/GCOM-C. A number of aerosol retrieval products have been developed based on the available measurements and successfully used for different scientific applications. However, the completeness and accuracy of aerosol data operationally derived from polarimetry do not yet appear to have reached the accuracy levels implied by theoretical sensitivity studies that analyzed the potential information content of satellite polarimetry. As a result, the dataset provided by MODIS is still most frequently used by the scientific community, yet this sensor has neither polarimetric nor multi-angular capabilities. Admittedly polarimetric multi-angular observations are highly complex and have extra sensitivities to aerosol particle morphology, vertical variability of aerosol properties, polarization of surface reflectance, etc. As such, they necessitate state-of-the-art forward modeling based on first-principles physics which remains rare, and conventional retrieval approaches based on look-up tables turn out to be unsuitable to fully exploit the information implicit in the measurements. Several new-generation retrieval approaches have recently been proposed to address these challenges. These methods use improved forward modeling of atmospheric (polarized) radiances and implement a search in the continuous space of solutions using rigorous statistically optimized inversions. Such techniques provide more accurate retrievals of the main aerosol parameters such as aerosol optical thickness and yield additional parameters such as aerosol absorption. However, the operational implementation of advanced retrieval approaches generally requires a significant extra effort, and the forward-modeling part of such retrievals still needs to be substantially improved. Ground-based passive polarimetric measurements have also been evolving over the past decade. Although polarimetry helps improve aerosol characterization, especially of the fine aerosol mode, the operators of major observational networks such as AERONET remain reluctant to include polarimetric measurements as part of routine retrievals owing to their high complexity and notable increase in effort required to acquire and interpret polarization data. In addition to remote-sensing observations, polarimetric characteristics of aerosol scattering have been measured in situ as well as in the laboratory using polar nephelometers. Such measurements constitute direct observations of single scattering with no contributions from multiple scattering effects and therefore provide unique data for the validation of aerosol opti

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jqsrt.2018.11.024

Cites

177 of 177 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 177 references

Cited by

See all 490

Metadata

Authors
Dubovik, O., Li, Z., Mishchenko, M. I., Tanré, D., Karol, Y., Bojkov, B., Cairns, B., Diner, D. J., Espinosa, W. R., Goloub, P., Gu, X., Hasekamp, O., Hong, J., Hou, W., Knobelspiesse, K. D., Landgraf, J., Li, L., Litvinov, P., Liu, Y., Lopatin, A., Marbach, T., Maring, H., Martins, V., Meijer, Y., Milinevsky, G., Mukai, S., Parol, F., Qiao, Y., Remer, L., Rietjens, J., Sano, I., Stammes, P., Stamnes, S., Sun, X., Tabary, P., Travis, L. D., Waquet, F., Xu, F., Yan, C., & Yin, D.
Venue
Journal of Quantitative Spectroscopy and Radiative Transfer
Publisher
Elsevier BV
Published
2019-02
Volume
224
Pages
474-511
ISSN
0022-4073 (ISSN-L)
OpenAlex
W2902986750
Cited by
490
References
177
FWCI
20.29
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives. https://app.cochat.ai/papers/10.1016/j.jqsrt.2018.11.024
The paper
Dubovik, O., Li, Z., Mishchenko, M. I., Tanré, D., Karol, Y., Bojkov, B., Cairns, B., Diner, D. J., Espinosa, W. R., Goloub, P., Gu, X., Hasekamp, O., Hong, J., Hou, W., Knobelspiesse, K. D., Landgraf, J., Li, L., Litvinov, P., Liu, Y., . . . Yin, D. (2019). Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives. Journal of Quantitative Spectroscopy and Radiative Transfer. https://doi.org/10.1016/j.jqsrt.2018.11.024

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.