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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 July 2023) | Viewed by 14676

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Dr. Ke Du

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Guest Editor

Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: optical remote sensing of fugitive emissions; development of methods, instruments, and artificial intelligence approach for air monitoring

Dr. Ravi Varma

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Guest Editor

Optind Solutions Private Limited, Unit No. 11, Technology Business Incubator, National Institute of Technology Calicut, Calicut 673601, India
Interests: spectroscopy; development of Instrumentation for atmospheric, and environmental monitoring using principles of optics; radiative transfer and aerosol forcing on climate (global and regional)

Dr. Ying Zhang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: inversion of atmospheric aerosol characteristics; air quality remote sensing; radiative transfer modeling; remote sensing for particle nucleation; air pollution assimilation and forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emissions, transport, and reactions of atmospheric pollutants determine the air pollution process that negatively affects air quality, ecosystems, and climate. To understand the process and make an effective remediation, it is essential to detect, identify, and quantify those pollutants. Optical remote sensing (ORS) technologies provide a powerful tool to measure atmospheric pollutants, as the measurements can be instantaneous, in situ, continuous, and have high sensitivity and specificity.

The major challenges of ORS applications involve: a) quantifying fugitive emissions that are highly heterogeneous, highly variable, and with undefined boundaries; b) detecting trace gases that exist at low concentrations; and c) determining the spatial distribution of pollutants in a vertical column through the atmospheric layer and/or over a large surface area.

The aim of the Special Issue is to present the latest advancements in optical remote sensing technologies and their applications in measuring atmospheric pollutants. Suitable subjects include, but are not limited to: a) new or improved ORS instrumentation; b) new methods for ORS technology applications; c) new inversion algorithms or models for emission measurements or species detection; and d) characterization or explanation of air pollution processes from optical remote sensing data.

We are looking for manuscripts of research papers, review papers, and technical notes focusing on technique development, methodology, modeling/algorithm development, and ORS data analysis for gas pollutants, greenhouse gases, atmospheric aerosols, and optical-related indices (such as visibility, optical depth, etc.).

Dr. Ke Du
Dr. Ravi Varma
Dr. Ying Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

optical remote sensing
fugitive mission
trace pollutant detection
air pollutants
leak detection
environmental optics
spectroscopy
LiDAR
light scattering and absorption
open-path

Published Papers (12 papers)

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18 pages, 2573 KiB

Open AccessArticle

Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey

by Simone Lolli

Remote Sens. 2023, 15(17), 4318; https://doi.org/10.3390/rs15174318 - 1 Sep 2023

Cited by 2 | Viewed by 1623

Abstract

This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with its ability to probe the atmosphere at very high spatial and temporal resolution and measure backscattered signals, has become an invaluable tool for studying these atmospheric components. However, the complexity and diversity of lidar technology requires advanced data processing and analysis methods, where machine learning has emerged as a powerful approach. This survey focuses on the application of various machine learning techniques, including supervised and unsupervised learning algorithms and deep learning models, to extract meaningful information from lidar observations. These techniques enable the detection, classification, and characterization of aerosols and clouds by leveraging the rich features contained in lidar signals. In this article, an overview of the different machine learning architectures and algorithms employed in the field is provided, highlighting their strengths, limitations, and potential applications. Additionally, this survey examines the impact of machine learning techniques on improving the accuracy, efficiency, and robustness of aerosol and cloud real-time detection from lidar observations. By synthesizing the existing literature and providing critical insights, this survey serves as a valuable resource for researchers, practitioners, and students interested in the application of machine learning techniques to lidar technology. It not only summarizes current state-of-the-art methods but also identifies emerging trends, open challenges, and future research directions, with the aim of fostering advancements in this rapidly evolving field. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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20 pages, 11583 KiB

Open AccessArticle

Spectral Calibration for SO₂ Cameras with Light Dilution Effect Correction

by Kuijun Wu, Jianjun Guo, Zihao Zhang, Huiliang Zhang, Juan Li, Faquan Li and Weiwei He

Remote Sens. 2023, 15(14), 3652; https://doi.org/10.3390/rs15143652 - 21 Jul 2023

Viewed by 803

Abstract

The detection ability of SO₂ cameras has been improved effectively, while the calibration is still the main factor that limits their measurement accuracy. This paper presents a nonlinear calibration theory by considering the effect of light dilution due to the path radiance as well as the dependence of plume aerosol on scattering wavelength. This new spectral calibration method is used to retrieve the SO₂ column density and emission rate of the Etna volcano. Results show that, compared with the DOAS calibration approach, the inversion error can be reduced by 13% if the new spectral calibration is adopted. The superiority of the proposed method will become more obvious for long-distance detection of optically thick plumes. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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10 pages, 2871 KiB

Open AccessCommunication

Energetic Electron Precipitation via Satellite and Balloon Observations: Their Role in Atmospheric Ionization

by Irina Mironova, Galina Bazilevskaya, Vladimir Makhmutov, Andrey Mironov and Nikita Bobrov

Remote Sens. 2023, 15(13), 3291; https://doi.org/10.3390/rs15133291 - 27 Jun 2023

Cited by 1 | Viewed by 934

Abstract

Information about the energetic electron precipitation (EEP) from the radiation belt into the atmosphere is important for assessing the ozone variability and dynamics of the middle atmosphere during magnetospheric and geomagnetic disturbances. The accurate values of energetic electron fluxes depending on their energy range are one of the most important problems for calculating atmospheric ionization rates, which, in turn, are taken into account for estimating ozone depletion in chemistry–climate models. Despite the importance of these processes for the high latitudes of middle atmosphere, precipitation of energetic electrons is still insufficiently studied. In order to better understand EEP and related processes in the atmosphere, it is important to have many realistic observations of EEP in order to correctly characterize their spectra. Invading the atmosphere, precipitating energetic electrons, in the range from tens of keV to relativistic energies of more than 1 MeV, generate bremsstrahlung, which penetrates into the stratosphere and is recorded by detectors on balloons. However, these observations can be made only when the balloon is at stratospheric heights. Near-Earth satellites, such as the polar-orbiting operational environmental satellites (POES), are constantly registering precipitating electrons in the loss cone, but are moving too fast in space. Based on a comparison of the results of EEP measurements on balloons and onboard POES satellites in 2003, we propose a criterion that makes it possible to constantly monitor EEP ionization at stratospheric heights using observations on POES satellites. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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21 pages, 100658 KiB

Open AccessArticle

Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments

by Mohammad Koushafar, Gunho Sohn and Mark Gordon

Remote Sens. 2023, 15(12), 3083; https://doi.org/10.3390/rs15123083 - 13 Jun 2023

Viewed by 1335

Abstract

Determining the height of plume clouds is crucial for various applications, including global climate models. Smokestack plume rise refers to the altitude at which the plume cloud travels downwind until its momentum dissipates and the temperatures of the plume cloud and its surroundings become equal. While most air-quality models employ different parameterizations to forecast plume rise, they have not been effective in accurately estimating it. This paper introduces a novel framework that utilizes Deep Convolutional Neural Networks (DCNNs) to monitor smokestack plume clouds and make real-time, long-term measurements of plume rise. The framework comprises three stages. In the first stage, the plume cloud is identified using an enhanced Mask R-CNN, known as the Deep Plume Rise Network (DPRNet). Next, image processing analysis and least squares theory are applied to determine the plume cloud’s boundaries and fit an asymptotic model to its centerlines. The z-coordinate of the critical point of this model represents the plume rise. Finally, a geometric transformation phase converts image measurements into real-world values. This study’s findings indicate that the DPRNet outperforms conventional smoke border detection and recognition networks. In quantitative terms, the proposed approach yielded a 22% enhancement in the F1 score, compared to its closest competitor, DeepLabv3. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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17 pages, 3833 KiB

Open AccessArticle

Atmospheric CW S-Lidars with Si/InGaAs Arrays: Potentialities in Real Environment

by Ravil Agishev, Zhenzhu Wang and Dong Liu

Remote Sens. 2023, 15(9), 2291; https://doi.org/10.3390/rs15092291 - 26 Apr 2023

Viewed by 1191

Abstract

The article proposes a methodology for analyzing the performance of S-lidars (S comes from Scheimpflug) as a new class of environmental remote sensors operating under conditions of wide variability of optical weather and sky background brightness. The novelty of the problem statement, the methods used and the results obtained are determined by their application to laser sensing systems with unconventional design principles and the consequent need to revise the traditional ways of assessing their potential capabilities. The research method is based on a dimensionless-parametric approach, which allows comparing phenomena and systems of different scales and combining complementary characteristics and parameters. Effects of the dimensionless optical weather factor on lidar potential are shown being investigated under various environmental conditions, from the clear atmosphere through haze and mist to fog when probing in Vis/SWIR spectral bands and using Si/InGaAs detector arrays. It is shown exactly how and to what extent the significant differences in their spectral sensitivity and internal noise parameters are susceptible to the wide spectral and energy variability of the sky background brightness observed at very different angles to the Sun. A detailed analysis of the two most important influencing factors within the system, “S-Lidar instrument + Optical weather + External background source”, taking into account their wide variability, allowed us to describe their joint nonlinear influence and, thus, to anticipate the imposed limitations. The proposed dimensionless-parametric concept for predicting the potential capabilities of S-lidars with Si/InGaAs arrays is aimed at expanding applications of this rapidly developing class of remote sensors in a wide variety of environments. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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17 pages, 4587 KiB

Open AccessArticle

Intercomparison of NO₃ under Humid Conditions with Open-Path and Extractive IBBCEAS in an Atmospheric Reaction Chamber

by Meng Wang, Shengrong Lou, Weiwei Hu, Haichao Wang, Xinming Wang, Fengxian Fan, Ravi Varma, Dean S. Venables and Jun Chen

Remote Sens. 2023, 15(3), 739; https://doi.org/10.3390/rs15030739 - 27 Jan 2023

Viewed by 1498

Abstract

We report an open-path incoherent broadband cavity-enhanced absorption spectroscopy (OP-IBBCEAS) technique for in situ simultaneous optical monitoring of NO₂, NO₃, and H₂O in a reaction chamber. The measurement precision values (1σ) are 2.9 ppbv and 2.9 pptv for NO₂ and NO₃ in 2 s, respectively, and the measurement uncertainties are 6% for NO₂ and 14% for NO₃. Intercomparison of measured concentrations of NO₂ and NO₃ by open-path and extractive IBBCEAS was carried out in the SAES-ARC reaction chamber during the reaction of NO₂ with O₃. The measurement accuracy of OP-IBBCEAS is verified by an NO₂ intercomparison and the NO₃ transmission efficiency of the extractive IBBCEAS is determined by comparison against the in situ NO₃ measurement. The relationship between H₂O absorption cross section and its mixing ratio at 295 K and 1 atm was analysed. Due to the spectral resolution of IBBCEAS system, the strong and narrow absorption lines of H₂O are unresolved and exhibit non-Beer–Lambert Law behaviour. Therefore, a correction method is used to obtain the effective absorption cross section for fitting the H₂O structure. An inappropriate H₂O absorption cross section can cause an overestimation of NO₃ concentration of about 28% in a humid atmosphere (H₂O = 1.8%). This spectroscopic correction provides an approach to obtain accurate NO₃ concentrations for open-path optical configurations, for example in chamber experiments or field campaigns. The measurement precision values are improved by a factor of 3 to 4 after applying Kalam filtering, achieving sub-ppbv (0.8 ppbv) and sub-pptv (0.9 pptv) performance in 2 s for NO₂ and NO₃, respectively. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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20 pages, 6374 KiB

Open AccessArticle

Effect of UV Scattering on Detection Limit of SO₂ Cameras

by Kuijun Wu, Zihao Zhang, Jianjun Guo, Xiangrui Hu, Juan Li, Faquan Li and Weiwei He

Remote Sens. 2023, 15(3), 705; https://doi.org/10.3390/rs15030705 - 25 Jan 2023

Cited by 2 | Viewed by 1531

Abstract

SO₂ ultraviolet (UV) camera technology has been successfully applied to the accurate imaging detection of pollutant gas concentration; however, the actual detection ability of this technology has not been intensively studied, especially the detection accuracy and limit under the influence of the light dilution effect. Here, we theoretically and experimentally investigate the UV scattering on SO₂ concentration inversion. The radiation transfer model of the light dilution effect is reconstructed, and the concept of the optimized detection limit is discussed. An outfield experiment is conducted on a ship exhaust, and the results are compared with the theoretical calculations, which indicates that the detection limit of the SO₂ UV camera is 15 ppm·m at close range and increases to 25 ppm·m when the detection distance is 3.5 km. This study proves that the detection limit of the SO₂ UV camera deteriorates with the decreasing atmospheric visibility, the lengthening detection distance, and the increasing aerosol content within the plume. In addition, the hardware indicators of the camera systems also play a key role in the detection limit, and taking reasonable image processing can significantly release the instruments’ performance and extend the applicability of the SO₂ UV camera. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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16 pages, 3630 KiB

Open AccessTechnical Note

Design of a Data Acquisition, Correction and Retrieval of Na Doppler Lidar for Diurnal Measurement of Temperature and Wind in the Mesosphere and Lower Thermosphere Region

by Yuan Xia, Xuewu Cheng, Zelong Wang, Linmei Liu, Yong Yang, Lifang Du, Jing Jiao, Jihong Wang, Haoran Zheng, Yajuan Li, Faquan Li and Guotao Yang

Remote Sens. 2023, 15(21), 5140; https://doi.org/10.3390/rs15215140 - 27 Oct 2023

Cited by 1 | Viewed by 811

Abstract

Based on the resonance fluorescence scattering mechanism, a narrowband sodium (Na) lidar can measure temperature and wind in the mesosphere and lower thermosphere (MLT) region. By using a narrowband spectral filter, background light noise during the day can be suppressed, allowing for continuous observations. To obtain full-diurnal-cycle temperature and wind measurement results, a complex and precise retrieval process is required, along with necessary corrections to minimize measurement errors. This paper introduces the design of a data acquisition unit for three frequencies in three directions of the Na lidar system in the Chinese Meridian Project (Phase II) and investigates the calibration and retrieval methods for obtaining diurnal temperature and horizontal wind in the MLT region, using a Na Doppler lidar with Faraday anomalous dispersion optical filter (FADOF). Furthermore, these methods are applied to observations conducted by a Na lidar in Beijing, China. The wind and temperature results over full diurnal cycles obtained from the all-solid-state Na Doppler lidar are reported for the first time and compared with temperature measurements from satellite, as well as wind observations from a meteor radar. The comparison demonstrates a reasonable agreement between the results, indicating the rationality of the lidar-retrieved results and the feasibility and effectiveness of the data correction and retrieval method. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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14 pages, 5195 KiB

Open AccessTechnical Note

The All-Solid-State Narrowband Lidar Developed by Optical Parametric Oscillator/Amplifier (OPO/OPA) Technology for Simultaneous Detection of the Ca and Ca⁺ Layers

by Lifang Du, Haoran Zheng, Chunlei Xiao, Xuewu Cheng, Fang Wu, Jing Jiao, Yuchang Xun, Zhishan Chen, Jiqin Wang and Guotao Yang

Remote Sens. 2023, 15(18), 4566; https://doi.org/10.3390/rs15184566 - 16 Sep 2023

Cited by 3 | Viewed by 1171

Abstract

We report an all-solid-state narrowband lidar system for the simultaneous detection of Ca and Ca⁺ layers over Yanqing (40.41°N, 116.01°E). The uniqueness of this lidar lies in its transmitter, which is based on optical parametric oscillation (OPO) and optical parametric amplification (OPA) techniques. The injection seeded OPO and the OPA are pumped by the second harmonic of an injection-seeded Nd:YAG laser, which can generate coherent light at the wavelength of 786 nm or 846 nm lasers, whose second harmonics in turn generate the 393 nm or 423 nm pulses, respectively, for the detection of thermospheric and ionospheric Ca⁺ and Ca layers. Compared to the conventional dye-based system, this lidar transmitter is a narrowband system (bandwidth < 200 MHz), which has produced a factor of two more output power with higher stability and reliability. The lidar system in Yingqing demonstrated Ca⁺ detection sensitivity of 0.1 atoms-cm⁻³ for long-term observation and reached a height of ~300 km. Potential applications and further improvements in this lidar technique are also discussed in this paper. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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13 pages, 5617 KiB

Open AccessTechnical Note

Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer

by Shijie Li, Tong Wu, Kai Zhong, Xianzhong Zhang, Yue Sun, Yijian Zhang, Yu Wang, Xinqi Li, Degang Xu and Jianquan Yao

Remote Sens. 2023, 15(15), 3812; https://doi.org/10.3390/rs15153812 - 31 Jul 2023

Viewed by 731

Abstract

Lidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive signals from different altitude ranges, where gluing the multi-channel echo signals becomes a key issue for accurate data inversion. In this paper, a multi-channel signal gluing algorithm based on the Improved Gray Wolf Optimizer (IGWO) and Neighborhood Rough Set (NRS), named IGWO-RSD, is proposed. The fitness function F is formed by three objective functions: correlation coefficient R, regression stability coefficient S and mean fit deviation D. All three objective functions are obtained from the data itself and do not rely on prior information. The weights of the objective functions R, S and D are pre-trained by NRS, and IGWO is used to optimize the fitness function F. With ground-based aerosol lidar data, all-day signal gluing experiments are performed, where IGWO-RSD demonstrates obvious advantages in stability, accuracy and applicability in lidar signal processing compared with NRSWNSGA-II. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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16 pages, 8601 KiB

Open AccessTechnical Note

Uncertainty Evaluation on Temperature Detection of Middle Atmosphere by Rayleigh Lidar

by Xinqi Li, Kai Zhong, Xianzhong Zhang, Tong Wu, Yijian Zhang, Yu Wang, Shijie Li, Zhaoai Yan, Degang Xu and Jianquan Yao

Remote Sens. 2023, 15(14), 3688; https://doi.org/10.3390/rs15143688 - 24 Jul 2023

Cited by 1 | Viewed by 755

Abstract

Measurement uncertainty is an extremely important parameter for characterizing the quality of measurement results. In order to measure the reliability of atmospheric temperature detection, the uncertainty needs to be evaluated. In this paper, based on the measurement models originating from the Chanin-Hauchecorne (CH) method, the atmospheric temperature uncertainty was evaluated using the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method (MCM) by considering the ancillary temperature uncertainty and the detection noise as the major uncertainty sources. For the first time, the GUM atmospheric temperature uncertainty framework was comprehensively and quantitatively validated by MCM following the instructions of JCGM 101: 2008 GUM Supplement 1. The results show that the GUM method is reliable when discarding the data in the range of 10–15 km below the reference altitude. Compared with MCM, the GUM method is recommended to evaluate the atmospheric temperature uncertainty of Rayleigh lidar detection in terms of operability, reliability, and calculation efficiency. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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16 pages, 3136 KiB

Open AccessTechnical Note

Quantifying Emissions from Fugitive Area Sources Using a Hybrid Method of Multi-Path Optical Remote Sensing and Tomographic Inverse-Dispersion Techniques

by Sheng Li, Yanna Liu and Ke Du

Remote Sens. 2023, 15(4), 1043; https://doi.org/10.3390/rs15041043 - 14 Feb 2023

Cited by 1 | Viewed by 1207

Abstract

Reducing methane (CH₄) emissions from anthropogenic activities is critical to climate change mitigation efforts. However, there is still considerable uncertainty over the amount of fugitive CH₄ emissions due to large-scale area sources and heterogeneous emission distributions. To reduce the uncertainty and improve the spatial and temporal resolutions, a new hybrid method was developed combining optical remote sensing (ORS), computed tomography (CT), and inverse-dispersion modeling techniques on the basis of which a multi-path scanning system was developed. It uses a horizontal radial plume mapping path configuration and adapts a Lagrangian stochastic dispersion mode into CT reconstruction. The emission map is finally calculated by using a minimal curvature tomographic reconstruction algorithm, which introduces smooth constraints at each pixel. Two controlled-release experiments of CH₄ were conducted with different configurations, showing relative errors of only 2% and 3%. Compared with results from the single-path inverse-dispersion method (5–175%), the new method can not only derive the emission distribution but also obtain a more accurate emission rate. The outcome of this research would bring broad application of the ORS-CT and inverse-dispersion techniques to other gases and sources. Full article

(This article belongs to the Special Issue Development or Application of Remote Sensing Techniques in Atmospheric Monitoring)

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