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Aerosol and Cloud Properties Retrieval by Satellite Sensors

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

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 27719

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Dr. Yolanda Sola

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Department of Applied Physics, University of Barcelona, 08016 Barcelona, Spain
Interests: radiative transfer; radiative models; aerosol optical properties; clouds and solar radiation; remote sensing; ozone column; photobiology
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Dear Colleagues,

The characterization of aerosol and cloud properties is fundamental for weather forecast and climate and environmental sciences. Both aerosols and clouds represent an important source of uncertainty in climate change studies. Moreover, clouds have large impact on the determination of surface properties in agricultural studies and solar energy. Remote sensing from satellite allows to obtain information on global and regional scales that perfectly complement ground-based retrievals, with more limited and irregular spatial distribution. Great efforts have been performed in recent years to improve satellite sensors and retrievals algorithms to quantify and characterize clouds and aerosols, but it remains a challenging research field.

This Special Issue calls for contributions that develop new retrieval techniques for cloud and aerosol detection and the estimation of optical and physical properties, such as optical depth. Comparisons between satellite sensors, as well as ground-based observations are also invited, with special emphasis on the validation of satellite products. Review papers compiling the current state-of-the-art and the availability of datasets with cloud and aerosols properties are highly welcomed.

Dr. Yolanda Sola
Guest Editor

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Keywords

AOD retrieval
Cloud detection methodologies
Cloud vertical profiles
Validation of satellite data
Comparison of satellite and ground-based observations
Aerosol and cloud optical properties

Related Special Issue

Aerosol and Cloud Properties Retrieval Using Satellite Sensors II: Focusing on Radiative Effects in Remote Sensing

Published Papers (9 papers)

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19 pages, 2070 KiB

Open AccessArticle

Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions

by Somaya Falah, Alaa Mhawish, Ali H. Omar, Meytar Sorek-Hamer, Alexei I. Lyapustin, Tirthankar Banerjee, Fadi Kizel and David M. Broday

Remote Sens. 2022, 14(15), 3667; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153667 - 30 Jul 2022

Cited by 8 | Viewed by 1735

Abstract

This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007–2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36–57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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31 pages, 49697 KiB

Open AccessArticle

Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation

by Michael Grzegorski, Gabriele Poli, Alessandra Cacciari, Soheila Jafariserajehlou, Andriy Holdak, Ruediger Lang, Margarita Vazquez-Navarro, Rosemary Munro and Bertrand Fougnie

Remote Sens. 2022, 14(1), 85; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010085 - 24 Dec 2021

Cited by 2 | Viewed by 2855

Abstract

The Polar Multi-Sensor Aerosol product (PMAp) is based on the synergistic use of three instruments from the Metop platform, GOME-2, AVHRR, and IASI. The retrieval algorithm includes three major steps: a pre-identification of the aerosol class, a selection of the aerosol model, and a calculation of the Aerosol Optical Depth (AOD). This paper provides a detailed description of the PMAp retrieval, which combines information provided by the three instruments. The retrieved AOD is qualitatively evaluated, and a good temporal as well as spatial performance is observed, including for the transition between ocean and land. More quantitatively, the performance is evaluated by comparison to AERONET in situ measurements. Very good consistency is also observed when compared to other space-based data such as MODIS or VIIRS. The paper demonstrates the ability of this first generation of synergistic products to derive reliable AOD, opening the door for the development of synergistic products from the instruments to be embarked on the coming Metop Second Generation platform. PMAp has been operationally distributed in near-real-time since 2014 over ocean, and 2016 over land. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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23 pages, 9262 KiB

Open AccessArticle

Cloud-Top Height Comparison from Multi-Satellite Sensors and Ground-Based Cloud Radar over SACOL Site

by Xuan Yang, Jinming Ge, Xiaoyu Hu, Meihua Wang and Zihang Han

Remote Sens. 2021, 13(14), 2715; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142715 - 09 Jul 2021

Cited by 7 | Viewed by 2554

Abstract

Cloud-top heights (CTH), as one of the representative variables reflecting cloud macro-physical properties, affect the Earth–atmosphere system through radiation budget, water cycle, and atmospheric circulation. This study compares the CTH from passive- and active-spaceborne sensors with ground-based Ka-band zenith radar (KAZR) observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site for the period 2013–2019. A series of fundamental statistics on cloud probability in different limited time and areas at the SACOL site reveals that there is an optimal agreement for both cloud frequency and fraction derived from space and surface observations in a 0.5° × 0.5° box area and a 40-min time window. Based on the result, several facets of cloud fraction (CF), cloud overlapping, seasonal variation, and cloud geometrical depth (CGD) are investigated to evaluate the CTH retrieval accuracy of different observing sensors. Analysis shows that the CTH differences between multi-satellite sensors and KAZR decrease with increasing CF and CGD, significantly for passive satellite sensors in non-overlapping clouds. Regarding passive satellite sensors, e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, the Multi-angle Imaging SpectroRadiometer (MISR) on Terra, and the Advanced Himawari Imager on Himawari-8 (HW8), a greater CTH frequency difference exists between the upper and lower altitude range, and they retrieve lower CTH than KAZR on average. The CTH accuracy of HW8 and MISR are susceptible to inhomogeneous clouds, which can be reduced by controlling the increase of CF. Besides, the CTH from active satellite sensors, e.g., Cloud Profiling Radar (CPR) on CloudSat, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), agree well with KAZR and are less affected by seasonal variation and inhomogeneous clouds. Only CALIPSO CTH is higher than KAZR CTH, mainly caused by the low-thin clouds, typically in overlapping clouds. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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14 pages, 3998 KiB

Open AccessArticle

Effects of Cloud Microphysics on the Vertical Structures of Cloud Radiative Effects over the Tibetan Plateau and the Arctic

by Yafei Yan, Yimin Liu, Xiaolin Liu and Xiaocong Wang

Remote Sens. 2021, 13(14), 2651; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142651 - 06 Jul 2021

Cited by 4 | Viewed by 3208

Abstract

The Tibetan Plateau (TP) and the Arctic are both cold, fragile, and sensitive to global warming. However, they have very different cloud radiative effects (CRE) and influences on the climate system. In this study, the effects of cloud microphysics on the vertical structures of CRE over the two regions are analyzed and compared by using CloudSat/CALIPSO satellite data and the Rapid Radiative Transfer Model. Results show there is a greater amount of cloud water particles with larger sizes over the TP than over the Arctic, and the supercooled water is found to be more prone to exist over the former than the latter, making shortwave and longwave CRE, as well as the net CRE, much stronger over the TP. Further investigations indicate that the vertical structures of CRE at high altitudes are primarily dominated by cloud ice water, while those at low altitudes are dominated by cloud liquid and mixed-phase water. The liquid and mixed-phase water results in a strong shallow heating (cooling) layer above the cooling (heating) layer in the shortwave (longwave) CRE profiles, respectively. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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31 pages, 73104 KiB

Open AccessArticle

Effect of Aerosols, Tropospheric NO₂ and Clouds on Surface Solar Radiation over the Eastern Mediterranean (Greece)

by Georgia Alexandri, Aristeidis K. Georgoulias and Dimitris Balis

Remote Sens. 2021, 13(13), 2587; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132587 - 01 Jul 2021

Cited by 7 | Viewed by 2678

Abstract

In this work, the effect that two basic air quality indexes, aerosols and tropospheric NO₂, exert on surface solar radiation (SSR) is studied, along with the effect of liquid and ice clouds over 16 locations in Greece, in the heart of the Eastern Mediterranean. State-of-the-art satellite-based observations and climatological data for the 15-year period 2005–2019, and a radiative transfer system based on a modified version of the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model are used. Our SSR simulations are in good agreement with ground observations and two satellite products. It is shown that liquid clouds dominate, with an annual radiative effect (RE) of −36 W/m², with ice clouds (−19 W/m²) and aerosols (−13 W/m²) following. The radiative effect of tropospheric NO₂ is smaller by two orders of magnitude (−0.074 W/m²). Under clear skies, RE_aer is about 3–4 times larger than for liquid and ice cloud-covered skies, while RE_NO2 doubles. The radiative effect of all the parameters exhibits a distinct seasonal cycle. An increase in SSR is observed for the period 2005–2019 (positive trends ranging from 0.01 to 0.52 W/m²/year), which is mostly related to a decrease in the aerosol optical depth and the liquid cloud fraction. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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21 pages, 6867 KiB

Open AccessArticle

A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images

by Xidong Chen, Liangyun Liu, Yuan Gao, Xiao Zhang and Shuai Xie

Remote Sens. 2020, 12(15), 2365; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152365 - 23 Jul 2020

Cited by 10 | Viewed by 2794

Abstract

Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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18 pages, 1714 KiB

Open AccessArticle

A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images

by Quan Xiong, Yuan Wang, Diyou Liu, Sijing Ye, Zhenbo Du, Wei Liu, Jianxi Huang, Wei Su, Dehai Zhu, Xiaochuang Yao and Xiaodong Zhang

Remote Sens. 2020, 12(3), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030450 - 01 Feb 2020

Cited by 23 | Viewed by 3602

Abstract

Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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25 pages, 10782 KiB

Open AccessArticle

Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo

by Xavier Ceamanos, Suman Moparthy, Dominique Carrer and Felix C. Seidel

Remote Sens. 2019, 11(24), 2958; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242958 - 10 Dec 2019

Cited by 11 | Viewed by 4590

Abstract

Geostationary satellites are increasingly used for the detection and tracking of atmospheric aerosols and, in particular, of the aerosol optical depth (AOD). The main advantage of these spaceborne platforms in comparison with polar orbiting satellites is their capability to observe the same region of the Earth several times per day with varying geometry. This provides a wealth of information that makes aerosol remote sensing possible when combined with the multi-spectral capabilities of the on-board imagers. Nonetheless, the suitability of geostationary observations for AOD retrieval may vary significantly depending on their spatial, spectral, and temporal characteristics. In this work, the potential of geostationary satellites was assessed based on the concept of critical surface albedo (CSA). CSA is linked to the sensitivity of each spaceborne observation to the aerosol signal, as it is defined as the value of surface albedo for which a varying AOD does not alter the satellite measurement. In this study, the sensitivity to aerosols was determined by estimating the difference between the surface albedo of the observed surface and the corresponding CSA (referred to as dCSA). The values of dCSA were calculated for one year of observations from the Meteosat Second Generation (MSG) spacecraft, based on radiative transfer simulations and information on the satellite acquisition geometry and the properties of the observed surface and aerosols. Different spectral channels from MSG and the future Meteosat Third Generation-Imager were used to study their distinct capabilities for aerosol remote sensing. Results highlight the significant but varying potential of geostationary observations across the observed Earth disk and for different time scales (i.e., diurnal, seasonal, and yearly). For example, the capability of sensing multiples times during the day is revealed to be a notable strength. Indeed, the value of dCSA often fluctuates significantly for a given day, which makes some instants of time more suitable for aerosol retrieval than others. This study determines these instants of time as well as the seasons and the sensing wavelengths that increase the chances for aerosol remote sensing thanks to the variations of dCSA. The outcomes of this work can be used for the development and refinement of AOD retrieval algorithms through the use of the concept of CSA. Furthermore, results can be extrapolated to other present-day geostationary satellites such as Himawari-8/9 and GOES-16/17. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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11 pages, 20191 KiB

Open AccessLetter

Comparison of ISS–CATS and CALIPSO–CALIOP Characterization of High Clouds in the Tropics

by Pasquale Sellitto, Silvia Bucci and Bernard Legras

Remote Sens. 2020, 12(23), 3946; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233946 - 02 Dec 2020

Cited by 3 | Viewed by 1972

Abstract

Clouds in the tropics have an important role in the energy budget, atmospheric circulation, humidity, and composition of the tropical-to-global upper-troposphere–lower-stratosphere. Due to its non-sun-synchronous orbit, the Cloud–Aerosol Transport System (CATS) onboard the International Space Station (ISS) provided novel information on clouds from space in terms of overpass time in the period of 2015–2017. In this paper, we provide a seasonally resolved comparison of CATS characterization of high clouds (between 13 and 18 km altitude) in the tropics with well-established CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) data, both in terms of clouds’ occurrence and cloud optical properties (optical depth). Despite the fact that cloud statistics for CATS and CALIOP are generated using intrinsically different local overpass times, the characterization of high clouds occurrence and optical properties in the tropics with the two instruments is very similar. Observations from CATS underestimate clouds occurrence (up to 80%, at 18 km) and overestimate the occurrence of very thick clouds (up to 100% for optically very thick clouds, at 18 km) at higher altitudes. Thus, the description of stratospheric overshoots with CATS and CALIOP might be different. While this study hints at the consistency of CATS and CALIOP clouds characterizaton, the small differences highlighted in this work should be taken into account when using CATS for estimating cloud properties and their variability in the tropics. Full article

(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)

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