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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 12908

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Dr. Tatiana Zhuravleva

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Guest Editor

Zuev Institute of Atmospheric Optics of the Siberian Branch of the RAS, Tomsk, Russia
Interests: radiative transfer; remote sensing of aerosol and clouds; 3D cloud effects; radiative balance of the atmosphere
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Dr. Alexander Kokhanovsky

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Guest Editor

Max Planck Institute for Chemistry, 55128 Mainz, Germany
Interests: cloud remote sensing; aerosol remote sensing; trace gas remote sensing; snow remote sensing; radiative transfer
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Special Issue Information

Dear Colleagues,

Atmospheric optics envelopes a wide range of subjects; this extends from the study of the interaction between optical radiation and atmospheric particulate matter and gases, to the methods and devices used for environmental investigation, and from the physical and chemical processes that govern the optical states of the atmosphere, to the mechanisms that affect the radiative balance of the atmosphere and the Earth’s climate. One of the fundamental components of atmospheric optics is remote sensing, both from the perspective of the development of the instrumental base and from the perspective of the development of creating theoretical foundations that describe the propagation of optical waves, atmospheric correction, light scattering processes in the atmosphere, the evolution of optical parameters in the atmosphere under natural and anthropogenic impacts, etc.

The simulation of optical radiation transfer provides the most logical linkage between observation and the physical processes that condition the Earth’s atmosphere. Advances in radiative transfer modeling and atmospheric optics enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches; this is in order to analyze and interpret measurements obtained via passive and active (LiDAR and radar) remote sensing from ground-based, air- and space-borne sensors.

Scientists currently working on forward and inverse radiative transfer research are encouraged to submit original articles and reviews on recent advances in atmospheric environmental remote sensing.

This Special Issue will be focused on (but not limited to) the following: (1) the theoretical aspects of radiative transfer that could advance remote sensing techniques; (2) models and codes for radiative transfer in the atmosphere and on the Earth's surface that improve our understanding of the information content of multiangle, spectral and polarimetric data and its applications; (3) analyses of 3D effects in radiative transfer and the associated uncertainties in regards to the interpretation of remotely sensed data; (4) amospheric correction; (5) the retrieval of the properties of aerosols; (6) the retrieval of liquid, mixed and ice clouds; (7) the retrieval of trace gases, (8) the retrieval of underlying surface properties, including albedo and bidirectional reflection distribution function.

Dr. Tatiana Zhuravleva
Dr. Alexander Kokhanovsky
Guest Editors

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Keywords

radiative transfer models and their applications for monitoring atmospheric phenomena
computational methods
active and passive remote sensing
polarimetric remote sensing
clouds, aerosol, and atmospheric gases
advanced retrieval algorithms
light scattering and absorption
remote sensing of underlying surface
combined aerosol-cloud retrieval algorithms

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Published Papers (10 papers)

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24 pages, 7451 KiB

Open AccessArticle

Trends of Key Greenhouse Gases as Measured in 2009–2022 at the FTIR Station of St. Petersburg State University

by Maria Makarova, Anatoly Poberovskii, Alexander Polyakov, Khamud H. Imkhasin, Dmitry Ionov, Boris Makarov, Vladimir Kostsov, Stefani Foka and Evgeny Abakumov

Remote Sens. 2024, 16(11), 1996; https://doi.org/10.3390/rs16111996 - 31 May 2024

Viewed by 824

Abstract

Key long-lived greenhouse gases (CO₂, CH₄, and N₂O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO₂, CH₄, and N₂O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO₂, CH₄, and N₂O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr⁻¹ for CO₂; (0.46 ± 0.02) % yr⁻¹ for CH₄; (0.28 ± 0.01) % yr⁻¹ for N₂O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH₄ and N₂O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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16 pages, 3257 KiB

Open AccessArticle

Lidar Optical and Microphysical Characterization of Tropospheric and Stratospheric Fire Smoke Layers Due to Canadian Wildfires Passing over Naples (Italy)

by Riccardo Damiano, Salvatore Amoruso, Alessia Sannino and Antonella Boselli

Remote Sens. 2024, 16(3), 538; https://doi.org/10.3390/rs16030538 - 31 Jan 2024

Cited by 1 | Viewed by 1237

Abstract

In the summer of 2017, huge wildfires in the British Columbia region (Canada) led to the injection of a remarkably high concentration of biomass burning aerosol in the atmosphere. These aerosol masses reached the city of Naples, Italy, at the end of August 2017, where they were characterized by means of a multiwavelength lidar and a sun–sky–lunar photometer. Here we report on the optical and microphysical properties of this aerosol in an intriguing condition, occurring on 4 September 2017, which is characterized by an interesting multi-layered vertical distribution of the aerosol. The Lidar profiles highlighted the presence of four aerosol layers, with two located in the lower troposphere and the other two at stratospheric altitudes. A rather thorough characterization of the biomass burning aerosol was carried out. The aerosol depolarization ratio showed an increasing dependence on the altitude with averaged values of 2–4% for the tropospheric layers, which are indicative of almost spherical smoke particles, and larger values in the stratospheric layers, suggestive of aspheric particles. Lidar-derived size distributions were retrieved for the first three aerosol layers, highlighting a higher particle concentration in the fine-mode fraction for the layers observed at higher altitudes. A dominance of fine particles in the atmosphere (fine-mode fraction > 0.8) with low absorption properties (absorption AOD < 0.0025 and SSA > 0.97) was also observed over the whole atmospheric column by sun photometer data. The space-resolved results provided by the lidar data are consistent with the columnar features retrieved by the AERONET sun photometer, thus evidencing the reliability and capability of lidar characterization of atmospheric aerosol in a very interesting condition of multiple aerosol layers originating from Canadian fires overpassing the observation station. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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18 pages, 7932 KiB

Open AccessArticle

Atmospheric HDO Abundance Measurements in the Tibetan Plateau Based on Laser Heterodyne Radiometer

by Xingji Lu, Yinbo Huang, Pengfei Wu, Jun Huang, Tao Luo, Qiang Liu and Zhensong Cao

Remote Sens. 2024, 16(3), 459; https://doi.org/10.3390/rs16030459 - 24 Jan 2024

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Abstract

The Tibet Plateau is known as the “third pole” of the world, and its environmental change profoundly impacts East Asia and even the global climate. HDO is the stable isotope of water vapor, which acts as an ideal tracer for studying the water cycle, and which is commonly used for atmospheric circulation and climatic studies. To monitor the water vapor isotopic abundance in the Tibetan Plateau, a portable laser heterodyne radiometer was operated in Golmud in August 2019. The radiometer utilizes a narrow-linewidth 3.66 μm distributed feedback interband cascade laser as the local oscillator, the heterodyne module is been optimized and the radiometer performs with high resolution and stability in obtaining spectral data. Furthermore, the absorption spectra of atmospheric HDO and H₂O are obtained, and the retrieval method for water vapor isotopic abundance is discussed. The optimal estimation method is adopted to retrieve the density of HDO and H₂O. The average column density of H₂O was 1.22 g/cm², and the HDO/H₂O ratio in Golmud was 178 ± 15 × 10⁻⁶ during the observation. For a better understanding of the retrieval, the retrieval errors are analyzed and compared. The results indicate that the smoothing error is significantly higher than the measurement error in this work. The backward trajectory analysis of atmospheric transport is used to investigate the relationship between water vapor density and atmospheric motion. The results indicate that the variation of H₂O column density and HDO/H₂O ratio have a relationship with atmospheric movements. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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16 pages, 3202 KiB

Open AccessArticle

Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm

by Zichao Liang, Yong-Keun Lee, Christopher Grassotti, Lin Lin and Quanhua Liu

Remote Sens. 2024, 16(1), 77; https://doi.org/10.3390/rs16010077 - 24 Dec 2023

Viewed by 1554

Abstract

A U-Net algorithm was used to retrieve surface pressure and wind speed over the ocean within tropical cyclones (TCs) and their neighboring areas using NOAA-20 Advanced Technology Microwave Sounder (ATMS) reprocessed Sensor Data Record (SDR) brightness temperatures (TBs) and geolocation information. For TC locations, International Best Track Archive for Climate Stewardship (IBTrACS) data have been used over the North Atlantic Ocean and West Pacific Ocean between 2018 and 2021. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) surface pressure and wind speed were employed as reference labels. Preliminary results demonstrated that the visualizations for wind speed and pressure matched the prediction and ERA5 location. The residual biases and standard deviations between the predicted and reference labels were about 0.15 m/s and 1.95 m/s, respectively, for wind speed and 0.48 hPa and 2.67 hPa, respectively, for surface pressure, after applying cloud screening for each ATMS pixel. This indicates that the U-Net model is effective for surface wind speed and surface pressure estimates over general ocean conditions. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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17 pages, 3199 KiB

Open AccessArticle

Detection of Atmospheric Hydrofluorocarbon-22 with Ground-Based Remote High-Resolution Fourier Transform Spectroscopy over Hefei and an Estimation of Emissions in the Yangtze River Delta

by Xiangyu Zeng, Wei Wang, Changgong Shan, Yu Xie, Qianqian Zhu, Peng Wu, Bin Liang and Cheng Liu

Remote Sens. 2023, 15(23), 5590; https://doi.org/10.3390/rs15235590 - 30 Nov 2023

Viewed by 1201

Abstract

Under the control of the Montreal Protocol and its amendments, hydrofluorocarbons (HCFCs) are used as temporary substitutes for ozone-depleting substances, such as chlorofluorocarbons, and are regulated for consumption and production. China plans to phase out HCFCs by 2030, and HCFC-22 (CHClF₂) is currently the most abundant HCFC in the atmosphere. This study measures the vertical profiles and total columns of atmospheric HCFC-22 from January 2017 to December 2022, based on the mid-infrared solar spectra recorded by the ground-based high-resolution Fourier transform infrared (FTIR) spectrometer at the Hefei remote sensing station. The HCFC-22 total columns over Hefei increased from 2017–2018 and gradually decreased in 2018–2022, with an annual variation rate of 5.98% and −1.02% ± 0.02%, respectively. Compared with the ACE-FTS satellite independent dataset, the FTIR data indicate good consistency with the ACE-FTS data at a 5–25 km altitude, with an average relative difference of −4.38 ± 0.83% between the vertical profiles. HCFC-22 emissions in the Yangtze River Delta from 2017 to 2022 are estimated, derived from measured total columns combined with the Lagrangian transport model and the Bayesian inversion technique. In the Yangtze River Delta, HCFC-22 emissions were high in 2017, with a value of 33.3 ± 16.8 kt, and decreased from 2018 to 2022, with a minimum of 27.3 ± 13.6 kt in 2022 during the observations. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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24 pages, 11127 KiB

Open AccessArticle

Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B

by Ruxuanyi Xiang, Tao Xie, Shuying Bai, Xuehong Zhang, Jian Li, Minghua Wang and Chao Wang

Remote Sens. 2023, 15(23), 5572; https://doi.org/10.3390/rs15235572 - 30 Nov 2023

Viewed by 1293

Abstract

The monitoring of mesoscale convective systems (MCS) is typically based on satellite infrared data. Currently, there is limited research on the identification of MCS using true color composite cloud imagery. In this study, an MCS dataset was created based on the true color composite cloud imagery from the Fengyun-4B geostationary meteorological satellite. An MCS true color composite cloud imagery identification model was developed based on the Swin-Unet network. The MCS dataset was categorized into continental MCS and oceanic MCS, and the model’s performance in identifying these two different types of MCS was examined. Experimental results indicated that the model achieved a recall rate of 83.3% in identifying continental MCS and 86.1% in identifying oceanic MCS, with a better performance in monitoring oceanic MCS. These results suggest that using true color composite cloud imagery for MCS monitoring is feasible, and the Swin-Unet network outperforms traditional convolutional neural networks. Meanwhile, we find that the frequency and distribution range of oceanic MCS is larger than that of continental MCS, and the area is larger and some parts of it are stronger. This study provides a novel approach for satellite remote-sensing-based MCS monitoring. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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20 pages, 4086 KiB

Open AccessArticle

Polar Cloud Detection of FengYun-3D Medium Resolution Spectral Imager II Imagery Based on the Radiative Transfer Model

by Shaojin Dong, Cailan Gong, Yong Hu, Fuqiang Zheng and Zhijie He

Remote Sens. 2023, 15(21), 5221; https://doi.org/10.3390/rs15215221 - 3 Nov 2023

Viewed by 1032

Abstract

The extensive existence of high-brightness ice and snow underlying surfaces in polar regions presents notable complexities for cloud detection in remote sensing imagery. To elevate the accuracy of cloud detection in polar regions, a novel polar cloud detection algorithm is proposed in this paper. Employing the MOD09 surface reflectance product, we compiled a database of monthly composite surface reflectance in the shortwave infrared bands specific to polar regions. Through the forward simulation of the correlation between the apparent reflectance and surface reflectance across diverse conditions using the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model, we established a dynamic cloud detection model for the shortwave infrared channels. In contrast to a machine learning algorithm and the widely used MOD35 cloud product, the algorithm introduced in this study demonstrates enhanced congruence with the authentic cloud distribution within cloud products. It precisely distinguishes between the cloudy and clear-sky pixels, achieving rates surpassing 90% for both, while maintaining an error rate and a missing rate each under 10%. The algorithm yields positive results for cloud detection in polar regions, effectively distinguishing between ice, snow, and clouds. It provides robust support for comprehensive and long-term cloud detection efforts in polar regions. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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14 pages, 2801 KiB

Open AccessArticle

Angular Patterns of Nonlinear Emission in Dye Water Droplets Stimulated by a Femtosecond Laser Pulse for LiDAR Applications

by Yury E. Geints

Remote Sens. 2023, 15(16), 4004; https://doi.org/10.3390/rs15164004 - 12 Aug 2023

Cited by 2 | Viewed by 1162

Abstract

Femtosecond laser-induced fluorescence (FLIF) and femtosecond laser-induced optical breakdown spectroscopy (FIBS) are important tools for remote diagnostics of atmospheric aerosols using LiDAR (Light Identification Detection and Ranging) technology. They are based on light emission excitation in disperse media via multiphoton nonlinear processes in aerosol particles induced by high-power optical pulses. To date, the main challenge restraining the large-scale application of FLIF and FIBS in atmospheric studies is the lack of a valued theory of the stimulated light emission in liquid microparticles with a sufficiently broad range of sizes. In this paper, we fill this gap and present a theoretical model of dye water droplet emission under high intensity laser exposure that adequately simulates the processes of multiphoton excited fluorescence and optical breakdown plasma emission in microparticles and gives quantitative estimates of the angular and power characteristics of nonlinear emission. The model is based on the numerical solution to the inhomogeneous Helmholtz equations for stimulating (primary) and nonlinear (secondary) waves provided by the random nature of molecule emission in particles. We show that droplet fluorescence stimulated by multiphoton absorption generally becomes more intense with increasing particle size. Moreover, far-field plasma emission from liquid particles demonstrates a larger angular diversity when changing the droplet radius in comparison with multiphoton excited fluorescence, which is mainly due to the excitation of the internal optical field resonances in spherical particles. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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18 pages, 16395 KiB

Open AccessArticle

Study of Atmospheric Aerosol in the Baikal Mountain Basin with Shipborne and Ground-Based Lidars

by Sergei Nasonov, Yurii Balin, Marina Klemasheva, Grigorii Kokhanenko, Mikhail Novoselov and Ioganes Penner

Remote Sens. 2023, 15(15), 3816; https://doi.org/10.3390/rs15153816 - 31 Jul 2023

Cited by 4 | Viewed by 1136

Abstract

The results of long-term lidar studies of the peculiarities of the vertical structure of atmospheric aerosols over Lake Baikal are presented. The paper provides an analysis of data obtained over the period from 2010 to 2022. The studies were carried out under both the background conditions and the extreme natural conditions associated with severe wildfires in Siberia. The parameters of the lidars used in regular summer expeditions to Lake Baikal are briefly described. The data analysis shows that the vertical structure of the aerosol in the lower troposphere up to 2000 m above Baikal in summer is often a stable structure of several aerosol layers tens to hundreds of meters thick. There can be no mixing of layers because the water in the lake is very cold and the aerosol does not rise to higher layers while the air is warming up during the day. The difference is shown between the spatiotemporal structures of aerosol plumes from local wildfires within the lake area and from distant sources. The Angstrom parameter and the aerosol optical depth are calculated for different atmospheric conditions: η_β = 1.57 ± 0.16 and τ = 0.09 for background conditions; η_β = 1.41 ± 0.07 and τ = 0.64 for the cases of the observation of smoke aerosol from distant wildfires; and η_β = 1.05 ± 0.08 and τ = 0.25 for the cases of the observation of smoke aerosol from nearby wildfires. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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14 pages, 2001 KiB

Open AccessTechnical Note

A Regional Aerosol Model for the Oceanic Area around Eastern China Based on Aerosol Robotic Network (AERONET)

by Shunping Chen, Congming Dai, Nana Liu, Wentao Lian, Yuxuan Zhang, Fan Wu, Cong Zhang, Shengcheng Cui and Heli Wei

Remote Sens. 2024, 16(6), 1106; https://doi.org/10.3390/rs16061106 - 21 Mar 2024

Viewed by 997

Abstract

A regional aerosol model can complement globally averaged models and improve the accuracy of atmospheric numerical models in local applications. This study established a seasonal aerosol model based on data from the Aerosol Robotic Network (AERONET) of the sea area around eastern China, and its performance in calculating the aerosol optical depth (AOD) was evaluated. The seasonal columnar volume particle size distributions (VPSDs) illustrated a bimodal structure consisting of fine and coarse modes. The VPSDs of spring, autumn, and winter roughly agreed with each other, with their amplitudes of fine and coarse modes being almost equal; however, the fine mode of the summer VPSD was approximately twice as high as that of the coarse mode. Lognormal mode decomposition analysis revealed that fine and coarse modes comprised two sub-modes. Fitting the seasonal VPSDs to the four-mode lognormal distribution yielded a parameterized aerosol size distribution model. Furthermore, seasonal variations in complex refractive indices (CRIs) indicated unignorable changes in aerosol compositions. Overall, error analysis validated that the proposed model could meet accuracy requirements for optical engineering applications, with median AOD calculation errors of less than 0.01. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)

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