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Advances in Remote Sensing of Aerosols and Cloud Properties over Ocean

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 28178

Special Issue Editors

Dr. Feng Xu

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Pengwang Zhai

E-Mail Website
Guest Editor
Department of Physics, University of Maryland, Baltimore County, MD, USA
Interests: light scattering by irregular particles; vector radiative transfer in coupled atmosphere and ocean systems; remote sensing of aerosols and hydrosols
Dr. Olga Kalashnikova

E-Mail Website
Guest Editor
Jet Propulsion Laboratory, Pasadena, CA 91011, USA
Interests: modeling of aerosol optics; light scattering by irregular particles; remote sensing of aerosol optical properties
Special Issues, Collections and Topics in MDPI journals
Dr. Robert Frouin

E-Mail Website
Guest Editor
Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Interests: radiation transfer; inverse problems related to light scattering; air-sea interactions; ocean color; carbon cycle; climate change

Special Issue Information

Dear Colleagues,

It is our great pleasure to organize a special issue of “Advances in Remote Sensing of Aerosols and Cloud Properties over Ocean” in the journal Remote Sensing.

Understanding the impact of aerosols and clouds on ocean processes and characterizing their coupling effects on ocean biology, biogeochemistry, ecology, and climate rely highly on accurate remote sensing of aerosol and cloud optical and microphysical properties from regional to global scales. Traditional remote sensing techniques mostly employ spectral radiances with single or multiple viewing angles. In the last decade there have been significant advancements that enhance existing remote sensing technologies and bring to bear new types of measurements. These efforts include multi-angle polarimetry, high spectral resolution lidar, broad spectral coverage from ultraviolet to shortwave infrared including oxygen-A and -B bands, and so on. Such technological developments have greatly enriched information available regarding aerosols and cloud properties over the ocean. In addition, in order to fully exploit these new types of measurements, advanced remote sensing algorithms and approaches are required to accurately characterize the properties of the ocean surface and resolve the spatial and vertical distributions of aerosols and clouds on a finer scale using both coupled and uncoupled methods.

This Special Issue aims at providing an overview of the recent advances in aerosol and cloud remote sensing over ocean. We invite investigators to contribute research and review articles that explore the following topics,

  • Development of advanced aerosol and cloud remote sensing technologies over ocean;
  • Validation/inter-comparison of instrument observations and models;
  • Light scattering modeling for aerosols and hydrosols of various shape, size, and composition;
  • Radiative transfer modeling in complex media and sensitivity studies of aerosols/hydrosols/cloud particles and ocean properties;
  • Inverse modeling that leads to improved characterization of aerosols/hydrosols/cloud particles and ocean properties;
  • Observational studies of aerosol, cloud, and ocean properties from regional to global scales.

Please feel free to disseminate this announcement to any colleagues who might be interested.

Kind regards,

Dr. Feng Xu
Dr. Pengwang Zhai
Dr. Olga Kalashnikova
Dr. Robert Frouin
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 submissions that pass pre-check are 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 2700 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

  • Remote Sensing
  • ocean
  • cloud
  • aerosols
  • hydrosols
  • atmospheric radiation
  • inversion

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Published Papers (6 papers)

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Research

28 pages, 5107 KiB  
Article
On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval
by Mikhail Krinitskiy, Marina Aleksandrova, Polina Verezemskaya, Sergey Gulev, Alexey Sinitsyn, Nadezhda Kovaleva and Alexander Gavrikov
Remote Sens. 2021, 13(2), 326; https://doi.org/10.3390/rs13020326 - 19 Jan 2021
Cited by 16 | Viewed by 3785
Abstract
Total Cloud Cover (TCC) retrieval from ground-based optical imagery is a problem that has been tackled by several generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there has been no considerable progress in terms [...] Read more.
Total Cloud Cover (TCC) retrieval from ground-based optical imagery is a problem that has been tackled by several generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there has been no considerable progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose a framework for the assessment of the algorithms’ characteristics. We present several new algorithms that are based on deep learning techniques: A model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million all-sky optical images of the visible sky dome taken in various regions of the world ocean. The research campaigns that contributed to the DASIO collection took place in the Atlantic ocean, the Indian ocean, the Red and Mediterranean seas, and the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and test subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver a superior quality compared to all previously published approaches. As a key result, we demonstrate a considerable drop in the ability to generalize the training data in the case of a strong covariate shift between the training and test subsets of imagery which may occur in the case of region-aware subsampling. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Aerosols and Cloud Properties over Ocean)
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24 pages, 28738 KiB  
Article
Changes and Predictions of Vertical Distributions of Global Light-Absorbing Aerosols Based on CALIPSO Observation
by Zigeng Song, Xianqiang He, Yan Bai, Difeng Wang, Zengzhou Hao, Fang Gong and Qiankun Zhu
Remote Sens. 2020, 12(18), 3014; https://doi.org/10.3390/rs12183014 - 16 Sep 2020
Cited by 6 | Viewed by 3083
Abstract
Knowledge of the vertical distribution of absorbing aerosols is crucial for radiative forcing assessment, and its quasi real-time prediction is one of the keys for the atmospheric correction of satellite remote sensing. In this study, we investigated the seasonal and interannual changes of [...] Read more.
Knowledge of the vertical distribution of absorbing aerosols is crucial for radiative forcing assessment, and its quasi real-time prediction is one of the keys for the atmospheric correction of satellite remote sensing. In this study, we investigated the seasonal and interannual changes of the vertical distribution of global absorbing aerosols based on satellite measurement from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and proposed a neural network (NN) model to predict the vertical distribution of global absorbing aerosols. Gaussian fitting was proposed to derive the maximum fitted particle number concentration (MFNC), altitude corresponding to MFNC (MFA), and standard deviation (MFASD) for vertical distribution of dust and smoke aerosols. Results showed that higher MFA values of dust and smoke aerosols mainly occurred over deserts and tropical savannas, respectively. For dust aerosol, the MFA is mainly observed at 0.5 to 6 km above deserts, and low MFNC values occur in boreal spring and winter while high values in summer and autumn. The MFA of smoke is systematically lower than that of dust, ranging from 0.5 to 3.5 km over tropical rainforest and grassland. Moreover, we found that the MFA of global dust and smoke had decreased by 2.7 m yr−1 (statistical significance p = 0.02) and 1.7 m yr−1 (p = 0.02) over 2007–2016, respectively. The MFNC of global dust has increased by 0.63 cm−3 yr−1 (p = 0.05), whereas that of smoke has decreased by 0.12 cm−3 yr−1 (p = 0.05). In addition, the determination coefficient (R2) of the established prediction models for vertical distributions of absorbing aerosols were larger than 0.76 with root mean square error (RMSE) less than 1.42 cm−3, which should be helpful for the radiative forcing evaluation and atmospheric correction of satellite remote sensing. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Aerosols and Cloud Properties over Ocean)
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21 pages, 6868 KiB  
Article
Comparison of Cloud Properties from Himawari-8 and FengYun-4A Geostationary Satellite Radiometers with MODIS Cloud Retrievals
by Ruize Lai, Shiwen Teng, Bingqi Yi, Husi Letu, Min Min, Shihao Tang and Chao Liu
Remote Sens. 2019, 11(14), 1703; https://doi.org/10.3390/rs11141703 - 18 Jul 2019
Cited by 47 | Viewed by 5449
Abstract
With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the [...] Read more.
With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the Fengyun-4A satellite, respectively, provide observations over similar regions at higher spatial and temporal resolutions for cloud and atmosphere studies. To better understand the reliability of AHI and AGRI retrieval products, we compare their cloud products with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, especially in terms of the cloud optical thickness (COT) and cloud effective radius (CER). Our comparison indicates that cloud mask and cloud phase of these instruments are reasonably consistent, while clear differences are noticed for COT and CER results. The average relative differences (RDs) between AHI and AGRI ice COT and that of MODIS are both over 40%, and the RDs of ice CER are less than 20%. The consistency between AHI and MODIS water cloud results is much better, with the RDs of COT and CER being 29% and 9%, respectively, whereas the RDs of AGRI COT and CER are still larger than 30%. Many factors such as observation geometry, cloud horizontal homogeneity, and retrieval system (e.g., retrieval algorithm, forward model, and assumptions) may contribute to these differences. The RDs of COTs from different instruments for homogeneous clouds are about one-third smaller than the corresponding RDs for inhomogeneous clouds. By applying unified retrieval systems based on the forward radiative transfer models designed for each particular band, we find that 30% to 70% of the differences among the results from different instruments are caused by the retrieval system (e.g., different treatments or assumptions for the retrievals), and the rest may be due to sub-pixel inhomogeneity, parallax errors, and calibration. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Aerosols and Cloud Properties over Ocean)
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26 pages, 8331 KiB  
Article
Scattering and Radiative Properties of Morphologically Complex Carbonaceous Aerosols: A Systematic Modeling Study
by Li Liu and Michael I. Mishchenko
Remote Sens. 2018, 10(10), 1634; https://doi.org/10.3390/rs10101634 - 14 Oct 2018
Cited by 59 | Viewed by 5270
Abstract
This paper provides a thorough modeling-based overview of the scattering and radiative properties of a wide variety of morphologically complex carbonaceous aerosols. Using the numerically-exact superposition T-matrix method, we examine the absorption enhancement, absorption Ångström exponent (AAE), backscattering linear depolarization ratio (LDR), [...] Read more.
This paper provides a thorough modeling-based overview of the scattering and radiative properties of a wide variety of morphologically complex carbonaceous aerosols. Using the numerically-exact superposition T-matrix method, we examine the absorption enhancement, absorption Ångström exponent (AAE), backscattering linear depolarization ratio (LDR), and scattering matrix elements of black-carbon aerosols with 11 different model morphologies ranging from bare soot to completely embedded soot–sulfate and soot–brown carbon mixtures. Our size-averaged results show that fluffy soot particles absorb more light than compact bare-soot clusters. For the same amount of absorbing material, the absorption cross section of internally mixed soot can be more than twice that of bare soot. Absorption increases as soot accumulates more coating material and can become saturated. The absorption enhancement is affected by particle size, morphology, wavelength, and the amount of coating. We refute the conventional belief that all carbonaceous aerosols have AAEs close to 1.0. Although LDRs caused by bare soot and certain carbonaceous particles are rather weak, LDRs generated by other soot-containing aerosols can reproduce strong depolarization measured by Burton et al. for aged smoke. We demonstrate that multi-wavelength LDR measurements can be used to identify the presence of morphologically complex carbonaceous particles, although additional observations can be needed for full characterization. Our results show that optical constants of the host/coating material can significantly influence the scattering and absorption properties of soot-containing aerosols to the extent of changing the sign of linear polarization. We conclude that for an accurate estimate of black-carbon radiative forcing, one must take into account the complex morphologies of carbonaceous aerosols in remote sensing studies as well as in atmospheric radiation computations. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Aerosols and Cloud Properties over Ocean)
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10 pages, 4139 KiB  
Article
FVI—A Floating Vegetation Index Formed with Three Near-IR Channels in the 1.0–1.24 μm Spectral Range for the Detection of Vegetation Floating over Water Surfaces
by Bo-Cai Gao and Rong-Rong Li
Remote Sens. 2018, 10(9), 1421; https://doi.org/10.3390/rs10091421 - 7 Sep 2018
Cited by 7 | Viewed by 5040
Abstract
Through the analysis of hyperspectral imaging data collected over water surfaces covered by floating vegetation, such as Sargassum and algae, we observed that the spectra commonly contain a reflectance peak centered near 1.07 μm. This peak results from the competing effects between the [...] Read more.
Through the analysis of hyperspectral imaging data collected over water surfaces covered by floating vegetation, such as Sargassum and algae, we observed that the spectra commonly contain a reflectance peak centered near 1.07 μm. This peak results from the competing effects between the well-known vegetation reflectance plateau in the 0.81–1.3 μm spectral range and the absorption effects above 0.75 μm by liquid water within the vegetation and in the surrounding water bodies. In this article, we propose a new index, namely the floating vegetation index (FVI), for the hyperspectral remote sensing of vegetation over surface layers of oceans and inland lakes. In the formulation of the FVI, one channel centered near 1.0 μm and another 1.24 μm are used to form a linear baseline. The reflectance value of the third channel centered at the 1.07-μm reflectance peak above the baseline is defined as the FVI. Hyperspectral imaging data acquired with the AVIRIS (Airborne Visible Infrared Imaging Spectrometer) instrument over the Gulf of Mexico and over salt ponds near Moffett Field in southern portions of the San Francisco Bay were used to demonstrate the success in detecting Sargassum and floating algae with this index. It is expected that the use of this index for the global detection of floating vegetation from hyperspectral imaging data to be acquired with future satellite sensors will result in improved detection and therefore enhanced capability in estimating primary production, a measure of how much carbon is fixed per unit area per day by oceans and inland lakes. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Aerosols and Cloud Properties over Ocean)
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Graphical abstract

11 pages, 313 KiB  
Article
Radiative Transfer Modeling of Phytoplankton Fluorescence Quenching Processes
by Peng-Wang Zhai, Emmanuel Boss, Bryan Franz, P. Jeremy Werdell and Yongxiang Hu
Remote Sens. 2018, 10(8), 1309; https://doi.org/10.3390/rs10081309 - 20 Aug 2018
Cited by 13 | Viewed by 4601
Abstract
We report the first radiative transfer model that is able to simulate phytoplankton fluorescence with both photochemical and non-photochemical quenching included. The fluorescence source term in the inelastic radiative transfer equation is proportional to both the quantum yield and scalar irradiance at excitation [...] Read more.
We report the first radiative transfer model that is able to simulate phytoplankton fluorescence with both photochemical and non-photochemical quenching included. The fluorescence source term in the inelastic radiative transfer equation is proportional to both the quantum yield and scalar irradiance at excitation wavelengths. The photochemical and nonphotochemical quenching processes change the quantum yield based on the photosynthetic active radiation. A sensitivity study was performed to demonstrate the dependence of the fluorescence signal on chlorophyll a concentration, aerosol optical depths and solar zenith angles. This work enables us to better model the phytoplankton fluorescence, which can be used in the design of new space-based sensors that can provide sufficient sensitivity to detect the phytoplankton fluorescence signal. It could also lead to more accurate remote sensing algorithms for the study of phytoplankton physiology. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Aerosols and Cloud Properties over Ocean)
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