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"Atmospheric Environmental Remote Sensing Society (AERSS)": Celebrating the Establishment

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 10694

Special Issue Editors

State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: aerosol remote sensing; polarimetric atmospheric detection
Special Issues, Collections and Topics in MDPI journals
German Aerospace Center (DLR), Remote Sensing Technology Institute, Oberpfaffenhofen, 82234 Weßling, Germany
Interests: remote sensing; atmospheric composition; aerosols; cloud; atmospheric trace gases; computational intelligence; machine learning
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
Interests: urban heat island effect; urban environmental quality; landslides; vegetation and ecosystems; spectral mixture analysis; aerosol retrieval; air quality monitoring; water vapor retrieval
Special Issues, Collections and Topics in MDPI journals
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Interests: lidar; atmospheric aerosols; cloud; aerosol-cloud interaction
Special Issues, Collections and Topics in MDPI journals
Department of Physics, University of Peshawar, Peshawar 25120, Pakistan
Interests: air quality assessment; atmospheric composition; DOAS; satellite and ground based remote sensing; aerosol monitoring; low-cost sensors
Special Issues, Collections and Topics in MDPI journals
Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Interests: remote sensing; artificial intelligence; big data; air pollution; aerosol; particulate matter; trace gas; cloud
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

On the 25 July 2022, the Atmospheric Environmental Remote Sensing Society (AERSS) was established. The society is an international scientific organization dedicated to building an interdisciplinary and cross-country platform for linking remote sensing to modeling and in situ observations, and promoting the application of atmospheric environmental remote sensing in climate, air quality, and other sustainable development goals (SDGs), especially in the Global South.

The Special Issue aims to celebrate the establishment of the Atmospheric Environmental Remote Sensing Society (AERSS) by highlighting the role of remote sensing in the atmospheric, environmental, and related fields, and welcomes all manuscripts with unique scientific insights including original research, dataset descriptions, and comprehensive literature reviews.

Prof. Dr. Zhengqiang Li
Dr. Diego Loyola
Prof. Dr. Mansing Wong
Prof. Dr. Kai Qin
Prof. Dr. Zhongwei Huang
Dr. Khan Alam
Dr. Jing Wei
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

  • clouds
  • aerosols
  • pollutant gas
  • greenhouse gas
  • lidar
  • light scattering and radiative transfer
  • remote sensing and modeling
  • air quality, climate, and health
  • sources, characterization, and emissions estimation of air pollutants
  • radiation and ecology
  • meteorological observation

Published Papers (8 papers)

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17 pages, 2855 KiB  
Article
Analysis of the Vertical Distribution and Driving Factors of Aerosol and Ozone Precursors in Huaniao Island, China, Based on Ground-Based MAX-DOAS
by Jinping Ou, Qihou Hu, Chengzhi Xing, Yizhi Zhu, Jiaxuan Feng, Xiangguang Ji, Mingzhu Zhang, Xinqi Wang, Liyuan Li, Ting Liu, Bowen Chang, Qihua Li, Hao Yin and Cheng Liu
Remote Sens. 2023, 15(21), 5103; https://doi.org/10.3390/rs15215103 - 25 Oct 2023
Viewed by 794
Abstract
Urban air pollution has become a regional environmental problem. In order to explore whether island areas were affected by the urban development of surrounding areas, in this paper, we systematically study the vertical distribution characteristics of atmospheric components, meteorological drivers, potential pollution sources, [...] Read more.
Urban air pollution has become a regional environmental problem. In order to explore whether island areas were affected by the urban development of surrounding areas, in this paper, we systematically study the vertical distribution characteristics of atmospheric components, meteorological drivers, potential pollution sources, and the population health risks of fine particulate matter in island cities in China. The vertical profiles of three atmospheric pollutants (aerosols, NO2, and HCHO) in the lower troposphere of Huaniao Island in the East China Sea (ECS) were obtained using ground-based multi-axial differential optical absorption spectroscopy (MAX-DOAS). The results show that the aerosol extinction coefficients, NO2, and HCHO were primarily distributed at altitudes below 1 km, and the atmospheric pollutants in Zhoushan were obviously affected by high-altitude transfer. The main meteorological driving factors of aerosols, NO2, and HCHO were different at different altitudes. The key factor contributing to the high column concentrations of NO2 and HCHO in the upper air (greater than 400 m) was the transport of pollutants brought about by changes in wind speed. By exploring the main potential sources of atmospheric pollutants, it was found that the main sources of aerosols, NO2, and HCHO are coastal cities in the Yangtze River Delta, including southeast Zhejiang Province, southeast Fujian Province, Shanghai, ECS, and the Yellow Sea. Compared with aerosols and HCHO, local primary emissions are an important source of NO2, which are mainly related to industrial activities in Zhoushan Port. In addition, using the expose-response function model, the number of attributable cases of PM2.5 air pollution in Zhoushan City in 2019 accounted for 6.58% of the total population. This study enriches our understanding of the vertical distribution characteristics of atmospheric composition and health risk assessment on Chinese islands. Full article
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23 pages, 19022 KiB  
Article
Estimation of Daily Seamless PM2.5 Concentrations with Climate Feature in Hubei Province, China
by Wenjia Ni, Yu Ding, Siwei Li, Mengfan Teng and Jie Yang
Remote Sens. 2023, 15(15), 3822; https://doi.org/10.3390/rs15153822 - 31 Jul 2023
Viewed by 772
Abstract
The urgent necessity for precise and uninterrupted PM2.5 datasets of high spatial–temporal resolution is underscored by the significant influence of PM2.5 on weather, climate, and human health. This study leverages the AOD reconstruction method to compensate for missing values in the [...] Read more.
The urgent necessity for precise and uninterrupted PM2.5 datasets of high spatial–temporal resolution is underscored by the significant influence of PM2.5 on weather, climate, and human health. This study leverages the AOD reconstruction method to compensate for missing values in the MAIAC AOD throughout Hubei Province. The reconstructed AOD dataset, exhibiting an R2/RMSE of 0.76/0.18, compared to AERONET AOD, was subsequently used for PM2.5 estimation. Our research breaks from traditional methodologies that solely depend on latitude and longitude information. Instead, it emphasizes the use of climate feature as an input for estimating PM2.5 concentrations. This strategic approach prevents potential spatial discontinuities triggered by geolocation information (latitude and longitude), thus ensuring the precision of the PM2.5 estimation (sample/spatial CV R2 = 0.91/0.88). Moreover, we proposed a method for identifying the absolute feature importance of machine-learning models. Contrasted with the relative feature-importance property typical of machine-learning models (a minor difference in the order of top three between geolocation-based and climate-feature-based models, and the slight difference in the top three: 0.08%/0.17%), our method provides a more comprehensive explanation of the absolute significance of features to the model (maintaining the same order and a larger difference in the top three: 0.99%/0.72%). Crucially, our findings demonstrated that AOD reconstruction can mitigate the overestimation of annual mean PM2.5 concentrations (ranging from 0.52 to 9.28 µg/m3). In addition, the seamless PM2.5 dataset contributes to reducing the bias in exposure risk assessment (ranging from −0.11 to 9.81 µg/m3). Full article
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21 pages, 6667 KiB  
Article
Comparative Analysis of Intelligent Optimization Algorithms for Atmospheric Duct Inversion Using Automatic Identification System Signals
by Li-Feng Huang, Cheng-Guo Liu, Zhi-Peng Wu, Li-Jun Zhang, Hong-Guang Wang, Qing-Lin Zhu, Jie Han and Ming-Chen Sun
Remote Sens. 2023, 15(14), 3577; https://doi.org/10.3390/rs15143577 - 17 Jul 2023
Cited by 1 | Viewed by 862
Abstract
Using intelligent optimization algorithms to retrieve atmospheric duct parameters by monitoring automatic identification system (AIS) signals at sea is a new passive remote sensing technology for atmospheric ducts. To thoroughly compare and analyze the inversion results of different intelligent optimization algorithms and optimize [...] Read more.
Using intelligent optimization algorithms to retrieve atmospheric duct parameters by monitoring automatic identification system (AIS) signals at sea is a new passive remote sensing technology for atmospheric ducts. To thoroughly compare and analyze the inversion results of different intelligent optimization algorithms and optimize the parameters of the algorithms, this study considered a simulated atmospheric duct environment for atmospheric duct inversion using the genetic, simulated annealing, and particle swarm optimization (PSO) algorithms. The results indicated that the PSO algorithm exhibited the best inversion performance. The inversion results of the simulated annealing particle swarm optimization (SAPSO) and PSO algorithms under different inversion parameters were further statistically analyzed, and the atmospheric duct parameters were obtained from measured AIS signals based on the SAPSO algorithm. The inversion results verified the effectiveness of the proposed algorithm, and they continuously improved with additional calculations in the inversion algorithm. However, the changing trend gradually slowed. Therefore, in practical applications, the inversion time consumption should be balanced with the inversion effect to optimize the inversion parameters. Full article
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20 pages, 40396 KiB  
Article
Convolutional Neural Network-Driven Improvements in Global Cloud Detection for Landsat 8 and Transfer Learning on Sentinel-2 Imagery
by Shulin Pang, Lin Sun, Yanan Tian, Yutiao Ma and Jing Wei
Remote Sens. 2023, 15(6), 1706; https://doi.org/10.3390/rs15061706 - 22 Mar 2023
Cited by 4 | Viewed by 1726
Abstract
A stable and reliable cloud detection algorithm is an important step of optical satellite data preprocessing. Existing threshold methods are mostly based on classifying spectral features of isolated individual pixels and do not contain or incorporate the spatial information. This often leads to [...] Read more.
A stable and reliable cloud detection algorithm is an important step of optical satellite data preprocessing. Existing threshold methods are mostly based on classifying spectral features of isolated individual pixels and do not contain or incorporate the spatial information. This often leads to misclassifications of bright surfaces, such as human-made structures or snow/ice. Multi-temporal methods can alleviate this problem, but cloud-free images of the scene are difficult to obtain. To deal with this issue, we extended four deep-learning Convolutional Neural Network (CNN) models to improve the global cloud detection accuracy for Landsat imagery. The inputs are simplified as all discrete spectral channels from visible to short wave infrared wavelengths through radiometric calibration, and the United States Geological Survey (USGS) global Landsat 8 Biome cloud-cover assessment dataset is randomly divided for model training and validation independently. Experiments demonstrate that the cloud mask of the extended U-net model (i.e., UNmask) yields the best performance among all the models in estimating the cloud amounts (cloud amount difference, CAD = −0.35%) and capturing the cloud distributions (overall accuracy = 94.9%) for Landsat 8 imagery compared with the real validation masks; in particular, it runs fast and only takes about 41 ± 5.5 s for each scene. Our model can also actually detect broken and thin clouds over both dark and bright surfaces (e.g., urban and barren). Last, the UNmask model trained for Landsat 8 imagery is successfully applied in cloud detections for the Sentinel-2 imagery (overall accuracy = 90.1%) via transfer learning. These prove the great potential of our model in future applications such as remote sensing satellite data preprocessing. Full article
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19 pages, 7314 KiB  
Article
Simulation and Performance Evaluation of Laser Heterodyne Spectrometer Based on CO2 Absorption Cell
by Tengteng Xia, Jiqiao Liu, Zheng Liu, Fangxin Yue, Fu Yang, Xiaopeng Zhu and Weibiao Chen
Remote Sens. 2023, 15(3), 788; https://doi.org/10.3390/rs15030788 - 30 Jan 2023
Viewed by 1532
Abstract
The laser heterodyne radiometer (LHR) has the advantages of miniaturization, low cost, and high spectral-resolution as a ground-verification instrument for satellite observation of atmospheric trace-gas concentration. To verify the accuracy of LHR measurements, a new performance evaluation method is presented here, based on [...] Read more.
The laser heterodyne radiometer (LHR) has the advantages of miniaturization, low cost, and high spectral-resolution as a ground-verification instrument for satellite observation of atmospheric trace-gas concentration. To verify the accuracy of LHR measurements, a new performance evaluation method is presented here, based on an ASE source and a CO2 absorption cell in the laboratory. Preliminary simulation analysis based on the system parameters of LHR is carried out for the performance analysis and data processing of this new combined test system. According to the simulation results, at wavelength deviation of fewer than 30 MHz, the retrieval error, which increases with bandwidth, can obtain an accuracy of 1 ppm within the bandwidth range of the photodetector (1.2 GHz) when this instrument line shape (ILS) is calibrated. Meanwhile, when the filter bandwidth is less than 200 MHz, the maximum error without ILS correction does not exceed 0.07 ppm. Moreover, with an ideal 60 MHz bandpass filter without ILS correction, LHR’s signal-to-noise ratio (SNR) should be greater than 20 to achieve retrieval results of less than 1 ppm. When the SNR is 100, the retrieval error is 0.206 and 0.265 ppm, corresponding to whether the system uncertainties (temperature and pressure) are considered. Considering all the error terms, the retrieval error (geometrically added) is 0.528 ppm at a spectral resolution of 0.004 cm−1, which meets the measurement accuracy requirement of 1 ppm. In the experiment, the retrieval and analysis of the heterodyne signals are performed for different XCO2 with [400 ppm, 420 ppm] in the absorption cell. Experimental results match well with the simulation, and confirm the accuracy of LHR with an error of less than 1 ppm with an SNR of 100. The LHR will be used to measure atmospheric-CO2 column concentrations in the future, and could be effective validation instruments on the ground for spaceborne CO2-sounding sensors. Full article
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14 pages, 15582 KiB  
Article
Dominant Contribution of South Asia Monsoon to External Moisture for Extreme Precipitation Events in Northern Tibetan Plateau
by Yan Wang, Kun Yang, Wenyu Huang, Tianpei Qiu and Binbin Wang
Remote Sens. 2023, 15(3), 735; https://doi.org/10.3390/rs15030735 - 27 Jan 2023
Cited by 6 | Viewed by 1700
Abstract
Numerous previous studies have pointed out that the South Asia monsoon (SAM) contributes most moisture to the southern Tibetan Plateau, whilst the moisture over the Northern Tibetan Plateau (NTP) is supplied by the westerlies, but the moisture sources for extreme precipitation events remain [...] Read more.
Numerous previous studies have pointed out that the South Asia monsoon (SAM) contributes most moisture to the southern Tibetan Plateau, whilst the moisture over the Northern Tibetan Plateau (NTP) is supplied by the westerlies, but the moisture sources for extreme precipitation events remain unclear. In this study, the tracking of external moisture sources was performed on ten extreme precipitation events over each of six target subregions of the NTP during the summer of 2010–2018. We found that the SAM provided most of the external moisture for extreme precipitation events in the NTP, except for the largest contribution from East Asia to extreme precipitation in the easternmost subregion. The moisture carried by westerly winds is the second foreign source over the western NTP. In addition, more than 40% of the NTP extreme precipitation events occurred under the synergy of weak westerlies and enhanced SAM, and these events have a longer duration than others. Thus, SAM plays a key role in moisture transport for the extreme precipitation events over the NTP, even though its contribution to the climatological moisture is not significant. Full article
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12 pages, 3980 KiB  
Technical Note
Low Blind Zone Atmospheric Lidar Based on Fiber Bundle Receiving
by Zhenping Yin, Qianyuan Chen, Yang Yi, Zhichao Bu, Longlong Wang and Xuan Wang
Remote Sens. 2023, 15(19), 4643; https://doi.org/10.3390/rs15194643 - 22 Sep 2023
Cited by 1 | Viewed by 639
Abstract
Atmospheric constituents feature a large vertical gradient in concentration, especially at the first few hundred meters over the earth’s surface. Atmospheric lidar usually cannot cover this range due to the incomplete overlap effect or the limited dynamic range of detectors. This drawback is [...] Read more.
Atmospheric constituents feature a large vertical gradient in concentration, especially at the first few hundred meters over the earth’s surface. Atmospheric lidar usually cannot cover this range due to the incomplete overlap effect or the limited dynamic range of detectors. This drawback is well known as the blind zone effect, which hinders the application of atmospheric lidars in many aspects. In this work, a method based on an optical fiber bundle was proposed to mitigate the blind zone effect. An optical fiber head with several stages, installed at the focal plane of the telescope, is used to receive backscatter light from different range levels. The design of the optical fiber head is analyzed with the ray-tracing technique. The optical fiber installed at the highest stage of the fiber head can collect far-range light like a small aperture, and all the other optical fibers are bundled into a near-range detection channel to receive backscatter light from the first few hundred meters. This special design can avoid the near-range light loss in conventional lidar systems, usually equipped with a small aperture. Different optical attenuations are then applied to near-range and far-range channels to suppress the overall signal dynamic range. This light-receiving method was applied in a 1030 nm elastic lidar, in which a fiber bundle with a three-stage fiber head was fabricated and installed. A test experiment was performed to verify this approach. A good agreement between simulations and in-system results was found. Based on this design, the blind zone of the lidar system is less than 50 m, and the detectable range can be over 10 km along the lidar’s line of sight with a single telescope receiver. This approach brings a new way of designing atmospheric lidar with a low blind zone and can strengthen our ability to monitor urban pollution and promote land-atmosphere interaction research. Full article
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17 pages, 4301 KiB  
Technical Note
The Fusion of ERA5 and MERRA-2 Atmospheric Temperature Profiles with Enhanced Spatial Resolution and Accuracy
by Yale Qiao, Dabin Ji, Huazhe Shang, Jian Xu, Ri Xu and Chong Shi
Remote Sens. 2023, 15(14), 3592; https://doi.org/10.3390/rs15143592 - 18 Jul 2023
Cited by 1 | Viewed by 1112
Abstract
Accurate high-resolution atmospheric temperature profiles are essential for precisely characterizing the evolution of the atmosphere and developing numerical forecasts. Atmospheric datasets, such as ERA5 (the fifth-generation ECMWF Reanalysis) and MERRA-2 (the Modern-Era Retrospective Analysis for Research and Applications, Version 2), provide global and [...] Read more.
Accurate high-resolution atmospheric temperature profiles are essential for precisely characterizing the evolution of the atmosphere and developing numerical forecasts. Atmospheric datasets, such as ERA5 (the fifth-generation ECMWF Reanalysis) and MERRA-2 (the Modern-Era Retrospective Analysis for Research and Applications, Version 2), provide global and continuous temperature profiles, with fine vertical distribution and horizontal resolution. RAOB (Radiosonde Observation) sounding data have high confidence and representativeness and are usually used for data accuracy verification. Due to the difficulty of updating existing products, and the scarcity of research on mesospheric temperature profiles, this work maximizes the high observation accuracy of RAOB data, combines the benefits of ERA5’s horizontal resolution and MERRA-2’s vertical distribution, and employs the optimal interpolation method to combine the data, in order to produce a fused result with high spatial resolution. After converting all of the data to the same spatial distribution, the optimal interpolation method was used to combine the two datasets from separate places and different pressure layers in order to produce the fused results, which had a vertical distribution of 45 layers and a spatial resolution of 0.25°. The fused data’s RMSE and MAE were 6.0 K and 5.0 K lower than those of the MERRA-2 temperature profile data, respectively, and 0.3 K and 0.4 K lower than those of the ERA5 temperature profile data, respectively. The validation, using data from 2019, showed that the fused data exhibits better correlation and data accuracy than the other two datasets, which demonstrated that the fused algorithm can potentially be used to generate reliable datasets for future meteorological research. Full article
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