Special Issue "Active and Passive Remote Sensing of Aerosols and Clouds"

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

Deadline for manuscript submissions: closed (28 February 2021).

Special Issue Editors

Prof. Dr. Wei Gong
E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: LiDAR hardware; advanced sensors; haze events; climate change
Special Issues and Collections in MDPI journals
Prof. Dr. Feiyue Mao
E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: Lidar data retrieval; 3D remote sensing; air pollution; cloud and aerosol radiation
Prof. Dr. Siwei Li
E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: observation of aerosols and clouds; oxygen A-band remote sensing; radiative transfer model; aerosol-cloud interaction
Prof. Dr. Wei Wang
E-Mail Website
Guest Editor
School of Geoscience and Info-Physics, Central South University, Changsha 410083, China
Interests: aerosol and cloud retrieval; passive remote sensing; PM estimation; atmospheric environment

Special Issue Information

Dear Colleagues,

It is a great pleasure to organize a special issue of “Active and Passive Remote Sensing of Aerosols and Clouds” in the journal of Remote Sensing.

With the increase of anthropogenic emission, radiative forcing from aerosol-cloud interactions have become one of the most uncertain factors in the estimation of the Earth’s changing energy budget and then climate change. In addition, aerosol emission, transportation and chemical composition affect air quality which is important to peoples health. However, the detailed understanding of aerosols and clouds is still insufficient by the limited observations and retrieval algorithms. So it is urgent to promote remote sensing methods to increase the comprehensive observations of three dimensional aerosols and clouds characteristics, further reveal the variation of aerosols and clouds as well as the physical mechanism of the interaction between them. Therefore, the main goal of this special issue is to survey the advanced active and passive remote sensing methods, for determining or understanding the detailed information of aerosols and clouds, their impacts on radiation, precipitation, climate, environment and peoples health from regional to global scales. Advanced active and passive sensors (such as lidar, radar, and Hyper-spectral optical sensor) and related retrieval algorithm for ground-based, airborne, and space-based observation are all encouraged. Also, the impacts of aerosol and cloud on air pollution, climate change and human health based on remote sensing are welcome. Furthermore, data fusion and assimilation approaches for acquiring new data with higher accuracy and temporal and spatial resolution are encouraged.

Prof. Dr. Wei Gong
Prof. Dr. Feiyue Mao
Prof. Dr. Siwei Li
Prof. Dr. Wei Wang
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 2400 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

  • Aerosols and cloud
  • Lidar and radar
  • Passive sensor
  • Retrieval algorithm
  • Haze and particulate matter
  • Aerosol-cloud interactions
  • Radiation effects
  • Data fusion and assimilation

Published Papers (21 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle
Polar Stratospheric Clouds Detection at Belgrano II Antarctic Station with Visible Ground-Based Spectroscopic Measurements
Remote Sens. 2021, 13(8), 1412; https://doi.org/10.3390/rs13081412 - 07 Apr 2021
Viewed by 223
Abstract
By studying the evolution of the color index (CI) during twilight at high latitudes, polar stratospheric clouds (PSCs) can be detected and characterized. In this work, this method has been applied to the measurements obtained by a visible ground-based spectrometer and PSCs have [...] Read more.
By studying the evolution of the color index (CI) during twilight at high latitudes, polar stratospheric clouds (PSCs) can be detected and characterized. In this work, this method has been applied to the measurements obtained by a visible ground-based spectrometer and PSCs have been studied over the Belgrano II Antarctic station for years 2018 and 2019. The methodology applied has been validated by full spherical radiative transfer simulations, which confirm that PSCs can be detected and their altitude estimated with this instrumentation. Moreover, our investigation shows that this method is useful even in presence of optically thin tropospheric clouds or aerosols. PSCs observed in this work have been classified by altitude. Our results are in good agreement with the stratospheric temperature evolution obtained by the global meteorological model ECMWF (European Centre for Medium Range Weather Forecasts) and with satellite PSCs observations from CALIPSO (Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations). To investigate the presence and long-term evolution of PSCs, the methodology used in this work could also be applied to foreseen and/or historical observations obtained with ground-based spectrometers such e. g. those dedicated to Differential Optical Absorption Spectroscopy (DOAS) for trace gas observation in Arctic and Antarctic sites. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Convective Boundary Layer Clouds as Observed with Ground-Based Lidar at a Mid-Latitude Plain Site
Remote Sens. 2021, 13(7), 1281; https://doi.org/10.3390/rs13071281 - 27 Mar 2021
Viewed by 317
Abstract
A total of 3047 individual shallow cumuli were identified from 9 years of polarization lidar measurements (2011–2019) at Wuhan, China (30.5°N, 114.4°E). These fair-weather shallow cumuli occurred at the top edge of the convective boundary layer between April and October with the maximum [...] Read more.
A total of 3047 individual shallow cumuli were identified from 9 years of polarization lidar measurements (2011–2019) at Wuhan, China (30.5°N, 114.4°E). These fair-weather shallow cumuli occurred at the top edge of the convective boundary layer between April and October with the maximum occurrence in July over the 30°N plain site. They persisted mostly (>92%) for a short period of ~1–10 min and had a geometrical thickness of ~50–600 m (a mean of 209 ± 138 m). The majority (>94%) of the cloud bases of these cumuli were found to appear ~50–560 m (a mean of 308 ± 254 m) above the lifting condensation level (LCL). In this height range from the LCL to the cloud base, the lidar volume depolarization ratio (δδV) slightly decreased with increasing height, showing gradually increasing condensation in this sub-cloud region due to penetrative thermals. Most of the observed shallow cumuli (79%) formed under the conditions of high near-surface air temperature (>30 °C) and water vapor mixing ratio (>15 g kg−1). Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Profiling Dust Mass Concentration in Northwest China Using a Joint Lidar and Sun-Photometer Setting
Remote Sens. 2021, 13(6), 1099; https://doi.org/10.3390/rs13061099 - 13 Mar 2021
Viewed by 568
Abstract
The satellite-based estimation of the dust mass concentration (DMC) is essential for accurately evaluating the global biogeochemical cycle of the dust aerosols. As for the uncertainties in estimating DMC caused by mixing dust and pollutants and assuming a fixed value for the mass [...] Read more.
The satellite-based estimation of the dust mass concentration (DMC) is essential for accurately evaluating the global biogeochemical cycle of the dust aerosols. As for the uncertainties in estimating DMC caused by mixing dust and pollutants and assuming a fixed value for the mass extinction efficiency (MEE), a classic lidar-photometer method is employed to identify and separate the dust from pollutants, obtain the dust MEE, and evaluate the effect of the above uncertainties, during five dust field experiments in Northwest China. Our results show that this method is effective for continental aerosol mixtures consisting of dust and pollutants. It is also seen that the dust loading mainly occurred in the free troposphere (<6 km), with the average mass loading of 905 ± 635 µg m−2 trapped in the planetary boundary layer. The dust MEE ranges from 0.30 to 0.60 m2 g−1 and has a significantly negative relationship with the size of dust particles. With the assumption of the dust MEE of 0.37 (0.60) m2 g−1, the DMC is shown to be overestimated (underestimated) by 20–40% (15–30%). In other words, our results suggest that the change of MEE with the size of dust particles should be considered in the estimation of DMC. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations
Remote Sens. 2021, 13(6), 1096; https://doi.org/10.3390/rs13061096 - 13 Mar 2021
Viewed by 407
Abstract
This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by [...] Read more.
This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Figure 1

Open AccessArticle
Uncertainty Assessment of the Vertically-Resolved Cloud Amount for Joint CloudSat–CALIPSO Radar–Lidar Observations
Remote Sens. 2021, 13(4), 807; https://doi.org/10.3390/rs13040807 - 23 Feb 2021
Viewed by 422
Abstract
The joint CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) climatology remains the only dataset that provides a global, vertically-resolved cloud amount statistic. However, data are affected by uncertainty that is the result of a combination of infrequent sampling, and a very narrow, [...] Read more.
The joint CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) climatology remains the only dataset that provides a global, vertically-resolved cloud amount statistic. However, data are affected by uncertainty that is the result of a combination of infrequent sampling, and a very narrow, pencil-like swath. This study provides the first global assessment of these uncertainties, which are quantified using bootstrapped confidence intervals. Rather than focusing on a purely theoretical discussion, we investigate empirical data that span a five-year period between 2006 and 2011. We examine the 2B-Geometric Profiling (GEOPROF)-LIDAR cloud product, at typical spatial resolutions found in global grids (1.0°, 2.5°, 5.0°, and 10.0°), four confidence levels (0.85, 0.90, 0.95, and 0.99), and three time scales (annual, seasonal, and monthly). Our results demonstrate that it is impossible to estimate, for every location, a five-year mean cloud amount based on CloudSat–CALIPSO data, assuming an accuracy of 1% or 5%, a high confidence level (>0.95), and a fine spatial resolution (1°–2.5°). In fact, the 1% requirement was only met by ~6.5% of atmospheric volumes at 1° and 2.5°, while the more tolerant criterion (5%) was met by 22.5% volumes at 1°, or 48.9% at 2.5° resolution. In order for at least 99% of volumes to meet an accuracy criterion, the criterion itself would have to be lowered to ~20% for 1° data, or to ~8% for 2.5° data. Our study also showed that the average confidence interval: decreased four times when the spatial resolution increased from 1° to 10°; doubled when the confidence level increased from 0.85 to 0.99; and tripled when the number of data-months increased from one (monthly mean) to twelve (annual mean). The cloud regime arguably had the most impact on the width of the confidence interval (mean cloud amount and its standard deviation). Our findings suggest that existing uncertainties in the CloudSat–CALIPSO five-year climatology are primarily the result of climate-specific factors, rather than the sampling scheme. Results that are presented in the form of statistics or maps, as in this study, can help the scientific community to improve accuracy assessments (which are frequently omitted), when analyzing existing and future CloudSat–CALIPSO cloud climatologies. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Aerosol—Cloud Interaction with Summer Precipitation over Major Cities in Eritrea
Remote Sens. 2021, 13(4), 677; https://doi.org/10.3390/rs13040677 - 14 Feb 2021
Viewed by 537
Abstract
This paper presents the spatiotemporal variability of aerosols, clouds, and precipitation within the major cities in Eritrea and it investigates the relationship between aerosols, clouds, and precipitation concerning the presence of aerosols over the study region. In Eritrea, inadequate water supplies will have [...] Read more.
This paper presents the spatiotemporal variability of aerosols, clouds, and precipitation within the major cities in Eritrea and it investigates the relationship between aerosols, clouds, and precipitation concerning the presence of aerosols over the study region. In Eritrea, inadequate water supplies will have both direct and indirect adverse impacts on sustainable development in areas such as health, agriculture, energy, communication, and transport. Besides, there exists a gap in the knowledge on suitable and potential areas for cloud seeding. Further, the inadequate understanding of aerosol-cloud-precipitation (ACP) interactions limits the success of weather modification aimed at improving freshwater sources, storage, and recycling. Spatiotemporal variability of aerosols, clouds, and precipitation involve spatial and time series analysis based on trend and anomaly analysis. To find the relationship between aerosols and clouds, a correlation coefficient is used. The spatiotemporal analysis showed larger variations of aerosols within the last two decades, especially in Assab, indicating that aerosol optical depth (AOD) has increased over the surrounding Red Sea region. Rainfall was significantly low but AOD was significantly high during the 2011 monsoon season. Precipitation was high during 2007 over most parts of Eritrea. The correlation coefficient between AOD and rainfall was negative over Asmara and Nakfa. Cloud effective radius (CER) and cloud optical thickness (COT) exhibited a negative correlation with AOD over Nakfa within the June–July–August (JJA) season. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model that is used to find the path and origin of the air mass of the study region showed that the majority of aerosols made their way to the study region via the westerly and the southwesterly winds. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation
Remote Sens. 2021, 13(3), 456; https://doi.org/10.3390/rs13030456 - 28 Jan 2021
Viewed by 558
Abstract
Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network [...] Read more.
Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Figure 1

Open AccessArticle
Climatology of Cloud Phase, Cloud Radiative Effects and Precipitation Properties over the Tibetan Plateau
Remote Sens. 2021, 13(3), 363; https://doi.org/10.3390/rs13030363 - 21 Jan 2021
Viewed by 375
Abstract
Current passive sensors fail to accurately identify cloud phase, thus largely limiting the quantification of radiative contributions and precipitation of different cloud phases over the Tibet Plateau (TP), especially for the mixed-phase and supercooled water clouds. By combining the 4 years of (January [...] Read more.
Current passive sensors fail to accurately identify cloud phase, thus largely limiting the quantification of radiative contributions and precipitation of different cloud phases over the Tibet Plateau (TP), especially for the mixed-phase and supercooled water clouds. By combining the 4 years of (January 2007–December 2010) cloud phase (2B-CLDCLASS-LIDAR), radiative fluxes (2B-FLXHR-LIDAR), and precipitation (2C-PRECIP-COLUMN) products from CloudSat, this study systematically quantifies the radiative contribution of cloud phases and precipitation over the TP. Statistical results indicate that the ice cloud frequently occurs during the cold season, while mixed-phase cloud fraction is more frequent during the warm season. In addition, liquid clouds exhibit a weak seasonal variation, and the relative cloud fraction is very low, but supercooled water cloud has a larger cloud distribution (the value reaches about 0.24) than those of warm water clouds in the eastern part of the TP during the warm season. Within the atmosphere, the ice cloud has the largest radiative contribution during the cold season, the mixed-phase cloud is the second most important cloud phase for the cloud radiative contribution during the warm season, and supercooled water clouds’ contribution is particularly important during the cold season. In particular, the precipitation frequency over the TP is mainly dominated by the ice and mixed-phase clouds and is larger over the southeastern part of the TP during the warm season. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessFeature PaperArticle
A Global Climatology of Dust Aerosols Based on Satellite Data: Spatial, Seasonal and Inter-Annual Patterns over the Period 2005–2019
Remote Sens. 2021, 13(3), 359; https://doi.org/10.3390/rs13030359 - 21 Jan 2021
Cited by 1 | Viewed by 712
Abstract
A satellite-based algorithm is developed and used to determine the presence of dust aerosols on a global scale. The algorithm uses as input aerosol optical properties from the MOderate Resolution Imaging Spectroradiometer (MODIS)-Aqua Collection 6.1 and Ozone Monitoring Instrument (OMI)-Aura version v003 (OMAER-UV) [...] Read more.
A satellite-based algorithm is developed and used to determine the presence of dust aerosols on a global scale. The algorithm uses as input aerosol optical properties from the MOderate Resolution Imaging Spectroradiometer (MODIS)-Aqua Collection 6.1 and Ozone Monitoring Instrument (OMI)-Aura version v003 (OMAER-UV) datasets and identifies the existence of dust aerosols in the atmosphere by applying specific thresholds, which ensure the coarse size and the absorptivity of dust aerosols, on the input optical properties. The utilized aerosol optical properties are the multiwavelength aerosol optical depth (AOD), the Aerosol Absorption Index (AI) and the Ångström Exponent (a). The algorithm operates on a daily basis and at 1° × 1° latitude-longitude spatial resolution for the period 2005–2019 and computes the absolute and relative frequency of the occurrence of dust. The monthly and annual mean frequencies are calculated on a pixel level for each year of the study period, enabling the study of the seasonal as well as the inter-annual variation of dust aerosols’ occurrence all over the globe. Temporal averaging is also applied to the annual values in order to estimate the 15-year climatological mean values. Apart from temporal, a spatial averaging is also applied for the entire globe as well as for specific regions of interest, namely great global deserts and areas of desert dust export. According to the algorithm results, the highest frequencies of dust occurrence (up to 160 days/year) are primarily observed over the western part of North Africa (Sahara), and over the broader area of Bodélé, and secondarily over the Asian Taklamakan desert (140 days/year). For most of the study regions, the maximum frequencies appear in boreal spring and/or summer and the minimum ones in winter or autumn. A clear seasonality of global dust is revealed, with the lowest frequencies in November–December and the highest ones in June. Finally, an increasing trend of global dust frequency of occurrence from 2005 to 2019, equal to 56.2%, is also found. Such an increasing trend is observed over all study regions except for North Middle East, where a slight decreasing trend (−2.4%) is found. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China
Remote Sens. 2020, 12(20), 3425; https://doi.org/10.3390/rs12203425 - 19 Oct 2020
Cited by 1 | Viewed by 501
Abstract
The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at [...] Read more.
The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolution of 2 km from the AHI level 1 data. Considering the anisotropic effects of complex surface structures over land, Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) model parameters product (MCD19A3) is used to calculate the surface reflectance for Himawari-8’s view angle and band. In addition, daily BRDF model parameters are calculated in areas with dense vegetation, considering the rapid variation of surface reflectance caused by vegetation growth. Moreover, aerosol models are constructed based on long duration Aerosol Robotic Network (AERONET) single scattering albedo (SSA) values to stand for aerosol types in the retrieval algorithm. The new algorithm is applied to AHI images over Bohai Rim region from 2018 and is evaluated using the newest AERONET version 3 AOD measurements and the latest MODIS collection 6.1 AOD products. The AOD retrievals from the new algorithm show good agreement with the AERONET AOD measurements, with a correlation coefficient of 0.93 and root mean square error (RMSE) of 0.12. In addition, the new algorithm increases AOD retrievals and retrieval accuracy compared to the Japan Aerospace Exploration Agency (JAXA) aerosol products. The algorithm shows stable performance during different seasons and times, which makes it possible for use in climate or diurnal aerosol variation studies. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
High-Resolution Gridded Level 3 Aerosol Optical Depth Data from MODIS
Remote Sens. 2020, 12(17), 2847; https://doi.org/10.3390/rs12172847 - 02 Sep 2020
Cited by 4 | Viewed by 1338
Abstract
The state-of-art satellite observations of atmospheric aerosols over the last two decades from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been extensively utilized in climate change and air quality research and applications. The operational algorithms now produce Level 2 aerosol data at [...] Read more.
The state-of-art satellite observations of atmospheric aerosols over the last two decades from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been extensively utilized in climate change and air quality research and applications. The operational algorithms now produce Level 2 aerosol data at varying spatial resolutions (1, 3, and 10 km) and Level 3 data at 1 degree. The local and global applications have benefited from the coarse resolution gridded data sets (i.e., Level 3, 1 degree), as it is easier to use since data volume is low, and several online and offline tools are readily available to access and analyze the data with minimal computing resources. At the same time, researchers who require data at much finer spatial scales have to go through a challenging process of obtaining, processing, and analyzing larger volumes of data sets that require high-end computing resources and coding skills. Therefore, we created a high spatial resolution (high-resolution gridded (HRG), 0.1 × 0.1 degree) daily and monthly aerosol optical depth (AOD) product by combining two MODIS operational algorithms, namely Deep Blue (DB) and Dark Target (DT). The new HRG AODs meet the accuracy requirements of Level 2 AOD data and provide either the same or more spatial coverage on daily and monthly scales. The data sets are provided in daily and monthly files through open an Ftp server with python scripts to read and map the data. The reduced data volume with an easy to use format and tools to access the data will encourage more users to utilize the data for research and applications. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Information Content of Ice Cloud Properties from Multi-Spectral, -Angle and -Polarization Observations
Remote Sens. 2020, 12(16), 2548; https://doi.org/10.3390/rs12162548 - 07 Aug 2020
Viewed by 1180
Abstract
Ice clouds play an important role in the Earth’s radiation budget, while their microphysical and optical properties remain one of the major uncertainties in remote sensing and atmospheric studies. Many satellite-based multi-spectral, -angle and -polarization instruments have been launched in recent years, and [...] Read more.
Ice clouds play an important role in the Earth’s radiation budget, while their microphysical and optical properties remain one of the major uncertainties in remote sensing and atmospheric studies. Many satellite-based multi-spectral, -angle and -polarization instruments have been launched in recent years, and it is unclear how these observations can be used to improve the understanding of ice cloud properties. This study discusses the impacts of multi-spectral, -angle and -polarization observations on ice cloud property retrievals by performing a theoretical information content (IC) analysis. Ice cloud properties, including the cloud optical thickness (COT), particle effective radius (Re) and particle habit (defined by the aspect ratio (AR) and the degree of surface roughness level (σ)), are considered. An accurate polarized radiative transfer model is used to simulate the top-of-atmosphere intensity and polarized observations at the cloud-detecting wavelengths of interest. The ice cloud property retrieval accuracy should be improved with the additional information from multi-spectral, -angle and -polarization observations, which is verified by the increased degrees of freedom for signal (DFS). Polarization observations at spectral wavelengths (i.e., 0.87 and 2.13 µm) are helpful in the improvement of ice cloud property retrievals, especially for small-sized particles. An optimal scheme to retrieve ice cloud properties is to comprise radiance intensity information at the 0.87, 1.24, 1.64 and 2.13 µm channels and polarization information (the degree of linear polarization, DOLP) at the 0.87 and 2.13 µm channels. As observations from multiple angles added, DFS clearly increases, while it becomes almost saturated when the number of angles reaches three. Besides, the retrieval of Re exhibits larger uncertainties, and the improvement in total DFS by adding multi-spectral, -angle and -polarization observations is mainly attributed to the improvement of Re retrieval. Our findings will benefit the future instrument design and the improvement in cloud property retrieval algorithms based on multi-spectral, -angle, and -polarization imagers. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements
Remote Sens. 2020, 12(14), 2330; https://doi.org/10.3390/rs12142330 - 20 Jul 2020
Cited by 1 | Viewed by 866
Abstract
Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, [...] Read more.
Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China
Remote Sens. 2020, 12(6), 978; https://doi.org/10.3390/rs12060978 - 18 Mar 2020
Cited by 1 | Viewed by 995
Abstract
Himawari-8 (H8), as a new generation geostationary meteorological satellite, has great potential for monitoring the spatial–temporal variation of aerosol properties. However, the large amount of spectral data with differing observation geometries require re-formulation of the surface reflectance correction to utilize this new satellite [...] Read more.
Himawari-8 (H8), as a new generation geostationary meteorological satellite, has great potential for monitoring the spatial–temporal variation of aerosol properties. However, the large amount of spectral data with differing observation geometries require re-formulation of the surface reflectance correction to utilize this new satellite data. This is achieved by using an improved version of the time series (TS) technique proposed by Mei et al., (2012) based on the assumption that the ratio of the surface reflectance in different spectral bands does not change between any two scan times within an hour. In addition, more suitable aerosol models were adopted, based on cluster analysis of local Aerosol Robotic Network (AERONET) data. The improved TS algorithm (ITS) was applied to retrieve the Aerosol Optical Depth (AOD) over eastern China and the results compare favorably with collocated reference AOD data at eleven sun photometer sites (R > 0.8, Root Mean Square Error (RMSE) < 0.2). Comparison with the H8 official AOD product and with MODIS Dark Target (DT)–Deep Blue (DB) combined AOD data shows the good performance of the ITS method for AOD retrieval with different observation angles. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Analysis of the Transport of Aerosols over the North Tropical Atlantic Ocean Using Time Series of POLDER/PARASOL Satellite Data
Remote Sens. 2020, 12(5), 757; https://doi.org/10.3390/rs12050757 - 25 Feb 2020
Viewed by 940
Abstract
The time series of total, fine and coarse POLAC/PARASOL aerosol optical depth (AOD) satellite products (2005–2013) processed by the POLAC algorithm are examined to investigate the transport of aerosols over the North Tropical Atlantic Ocean, a region that is characterized by significant dust [...] Read more.
The time series of total, fine and coarse POLAC/PARASOL aerosol optical depth (AOD) satellite products (2005–2013) processed by the POLAC algorithm are examined to investigate the transport of aerosols over the North Tropical Atlantic Ocean, a region that is characterized by significant dust aerosols events. First, the comparison of satellite observations with ground-based measurements acquired by AERONET ground-based measurements shows a satisfactory consistency for both total AOD and coarse mode AOD (i.e., correlation coefficients of 0.75 and bias ranging from −0.03 to 0.03), thus confirming the robustness and performance of POLAC/PARASOL data to investigate the spatio-temporal variability of the aerosols over the study area. Regarding fine mode aerosol, POLAC/PARASOL data present a lower performance with correlation coefficient ranging from 0.37 to 0.73. Second, the analysis of POLAC/PARASOL aerosol climatology reveals a high contribution of the coarse mode of aerosols ( AOD c between 0.1 and 0.4) at long distance from the African sources, confirming previous studies related to dust transport. The POLAC/PARASOL data were also compared with aerosol data obtained over the North Tropical Atlantic Ocean from MACC and MERRA-2 reanalyses. It is observed that the total AOD is underestimated in both reanalysis with a negative bias reaching −0.2. In summary, our results thus suggest that satellite POLAC/PARASOL observations of fine and coarse modes of aerosols could provide additional constraints useful to improve the quantification of the dust direct radiative forcing on a regional scale but also the biogeochemical processes such as nutrient supply to the surface waters. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Figure 1

Open AccessArticle
Construction of Nighttime Cloud Layer Height and Classification of Cloud Types
Remote Sens. 2020, 12(4), 668; https://doi.org/10.3390/rs12040668 - 18 Feb 2020
Cited by 1 | Viewed by 792
Abstract
A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are [...] Read more.
A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights are estimated up to 400 km on both sides of the ground track and reconstructed with the dead zone setting for an approximate evaluation of the reliability. By mimicking off-nadir pixels with a dead zone around pixels along the ground track, reconstruction of nadir profiles shows that, at 200 km from the ground track, the cloud top height (CTH) and the cloud base height (CBH) reconstructed by the NSRM method are within 1.49 km and 1.81 km of the original measurements, respectively. The constructed cloud structure is utilized for cloud classification in the nighttime. The same method is applied to the daytime measurements for comparison with collocated MODIS classification based on the International Satellite Cloud Climatology Project (ISCCP) standard. The comparison of eight cloud types over the expanded distance shows good agreement in general. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Combination of AIRS Dual CO2 Absorption Bands to Develop an Ice Clouds Detection Algorithm in Different Atmospheric Layers
Remote Sens. 2020, 12(1), 6; https://doi.org/10.3390/rs12010006 - 18 Dec 2019
Cited by 2 | Viewed by 693
Abstract
The use of infrared (IR) sensors to detect clouds in different layers of the atmosphere is a big challenge, especially for ice clouds. This study aims to improve ice cloud detection using Lin’s algorithm and apply it to Atmospheric Infrared Sounder (AIRS). To [...] Read more.
The use of infrared (IR) sensors to detect clouds in different layers of the atmosphere is a big challenge, especially for ice clouds. This study aims to improve ice cloud detection using Lin’s algorithm and apply it to Atmospheric Infrared Sounder (AIRS). To achieve these objectives, the scattering and emission characteristics of clouds as perceived by AIRS longwave infrared (LWIR, ~15 μm) and shortwave infrared (SWIR, ~4.3 μm) CO2 absorption bands are applied for ice cloud detection. Hence, the weighting function peak (WFP), cut-off pressure, and correlation coefficients between the brightness temperatures (BTs) of LWIR and SWIR channels are used to pair the LWIR and SWIR channels. After that, the linear relationship between the clear-sky BTs of the paired LWIR and SWIR channels is established by the cloud scattering and emission Index (CESI). However, the linear relationship fails in the presence of ice clouds. Comparing these results with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations show that the probability of detection of ice clouds for Pair-8 (WFP~330hPa), Pair-19 (WFP~555hPa), and Pair-24 (WFP~866hPa) are 0.63, 0.71, and 0.73 in the daytime and 0.46, 0.62, and 0.7 in the nighttime at a false alarm rate of 0.1 when ice clouds top pressure above 330 hPa, 555 hPa, and 866 hPa, respectively. Furthermore, the thresholds of the three pairs are 2.4 K, 3 K, and 8.7 K in the daytime and 1.7 K, 1.7 K, and 4.4 K in the nighttime at the highest Heike Skill Score (HSS). The error of HSS values based on thresholds of ice clouds is between 0.01 and 0.02 which is comparable with the ice cloud detection results in both day and night conditions. It is shown that Pair-8 (WFP~330hPa) can detect opaque and thick ice clouds above its WFP altitude over the tropical areas but it is unable to observe ice clouds over the mid-latitude while Pair-19 and Pair-24 can identify ice clouds above their WFP altitude. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Comparison of Aqua/Terra MODIS and Himawari-8 Satellite Data on Cloud Mask and Cloud Type Classification Using Split Window Algorithm
Remote Sens. 2019, 11(24), 2944; https://doi.org/10.3390/rs11242944 - 09 Dec 2019
Cited by 3 | Viewed by 1681
Abstract
Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), [...] Read more.
Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 µm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 µm) and 32 (12.02 µm) of MODIS (SWA31-32) and band 13 (10.4 µm) and 15 (12.4 µm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method
Remote Sens. 2019, 11(24), 2931; https://doi.org/10.3390/rs11242931 - 06 Dec 2019
Cited by 2 | Viewed by 886
Abstract
Two back-propagation artificial neural network retrieval models have been developed for obtaining the dust aerosol optical depth (AOD) and dust-top height (DTH), respectively, from Atmospheric InfraRed Sounder (AIRS) brightness temperature (BT) measurements over Taklimakan Desert area. China Aerosol Remote Sensing Network (CARSNET) measurements [...] Read more.
Two back-propagation artificial neural network retrieval models have been developed for obtaining the dust aerosol optical depth (AOD) and dust-top height (DTH), respectively, from Atmospheric InfraRed Sounder (AIRS) brightness temperature (BT) measurements over Taklimakan Desert area. China Aerosol Remote Sensing Network (CARSNET) measurements at Tazhong station were used for dust AOD validation. Results show that the correlation coefficient of dust AODs between AIRS and CARSNET reaches 0.88 with a deviation of −0.21, which is the same correlation coefficient as the AIRS dust AOD and the Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product. In the AIRS DTH retrieval model, there is an option to include the collocated MODIS deep blue (DB) AOD as additional input for daytime retrieval; the independent dust heights from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used for AIRS DTH validation, and results show that the DTHs derived from the combined AIRS BT measurements and MODIS DB AOD product have better accuracy than those from AIRS BT measurements alone. The correlation coefficient of DTHs between AIRS and independent CALIOP dust heights is 0.79 with a standard deviation of 0.41 km when MODIS DB AOD product is included in the retrieval model. A series of case studies from different seasons were examined to demonstrate the feasibility of retrieving dust parameters from AIRS and potential applications. The method and approaches can be applied to process measurements from advanced infrared (IR) sounder and high-resolution imager onboard the same platform. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Open AccessArticle
Three-Dimensional Cloud Structure Reconstruction from the Directional Polarimetric Camera
Remote Sens. 2019, 11(24), 2894; https://doi.org/10.3390/rs11242894 - 04 Dec 2019
Cited by 1 | Viewed by 1018
Abstract
Clouds affect radiation transmission through the atmosphere, which impacts the Earth’ s energy balance and climate. Currently, the study of clouds is mostly based on a two-dimensional (2-D) plane rather than a three-dimensional (3-D) space. However, 3-D cloud reconstruction is playing an important [...] Read more.
Clouds affect radiation transmission through the atmosphere, which impacts the Earth’ s energy balance and climate. Currently, the study of clouds is mostly based on a two-dimensional (2-D) plane rather than a three-dimensional (3-D) space. However, 3-D cloud reconstruction is playing an important role not only in a radiation transmission calculation but in forecasting climate change as well. Currently, the study of clouds is mostly based on 2-D single angle satellite observation data while the importance of a 3-D structure of clouds in atmospheric radiation transmission is ignored. 3-D structure reconstruction would improve the radiation transmission accuracy of the cloudy atmosphere based on multi-angle observations data. Characterizing the 3-D structure of clouds is crucial for an extensive study of this complex intermediate medium in the atmosphere. In addition, it is also a great carrier for visualization of its parameters. Special attributes and the shape of clouds can be clearly illustrated in a 3-D cloud while these are difficult to describe in a 2-D plane. It provides a more intuitive expression for the study of complex cloud systems. In order to reconstruct a 3-D cloud structure, we develop and explore a ray casting algorithm applied to data from the Directional Polarimetric Camera (DPC), which is onboard the GF-5 satellite. In this paper, we use DPC with characteristics of imaging multiple angles of the same target, and characterize observations of clouds from different angles in 3-D space. This feature allows us to reconstruct 3-D clouds from different angles of observations. In terms of verification, we use cloud profile data provided by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to compare with the results of reconstructed 3-D clouds based on DPC data. This shows that the reconstruction method has good accuracy and effectiveness. This 3-D cloud reconstruction method would lay a scientific reference for future analysis on the role of clouds in the atmosphere and for the construction of 3-D structures of aerosols. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Other

Jump to: Research

Open AccessTechnical Note
Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations
Remote Sens. 2020, 12(24), 4125; https://doi.org/10.3390/rs12244125 - 17 Dec 2020
Cited by 1 | Viewed by 694
Abstract
Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a [...] Read more.
Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a first estimate of the TOA reflectances which are then optimized by comparison with the satellite data. Recently developed deep neural network (DNN) models provide a powerful tool to represent the complicated relationship statistically. This study presents a methodology based on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations. A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training and validation samples using random k-fold cross-validation and using AERONET site-specific leave-one-station-out validation, and is compared with a random forest regression estimator and Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094, R2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R2 = 0.730 for leave-one-station-out validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation samples may come from the same AHI pixel location. The leave-one-station-out validation reflects the accuracy for large-area applications where there are no training samples for the pixel location to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD. In addition, the contribution of the dark-target derived TOA ratio predictors is examined and confirmed, and the sensitivity to the DNN structure is discussed. Full article
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Show Figures

Graphical abstract

Back to TopTop