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Remote Sensing Calibration and Validation in Sounding Atmosphere and Ionosphere

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 7425

Special Issue Editors


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Guest Editor
State Key Laboratory of Space Weather, University of Chinese Academy of Sciences, Beijing, China
Interests: remote sensing; data processing; airglow

E-Mail Website
Guest Editor
State Key Laboratory of Space Weather, University of Chinese Academy of Sciences, Beijing, China
Interests: GNSS remote sensing GNSS occultation; GNSS-R technique and application

E-Mail Website
Guest Editor
State Key Laboratory of Space Weather, University of Chinese Academy of Sciences, Beijing, China
Interests: atmospheric remote sensing

Special Issue Information

Dear Colleagues,

A variety of remote sensing techniques have been adopted to detect the atmosphere and ionosphere for the purpose of studying the characteristics of dynamics and photochemistry in the region, such as radars, lidars, GNSS radio occultations, and airglow observation techniques, etc. Each remote sensing technique has its own unique advantages as well as shortcomings in deriving the atmospheric and ionospheric parameters. Therefore, the calibration and validation of the instruments based on these remote sensing techniques play essential roles in the research on the atmosphere and ionosphere.

This Special Issue aims to collect papers with new results of diverse aspects of remote sensing techniques in the sounding of the atmosphere and ionosphere, including the development of active and passive optical and radio instruments, GNSS radio occultation (GNSS-RO) and reflectometry (GNSS-R) techniques, calibration and validation, comparison and application of datasets, and new retrieval methods, etc.

Potential topics for this Special Issue include, but are not limited to:

  • GNSS RO atmospheric datasets calibration, validation, comparison and application
  • GNSS-R datasets calibration, validation, comparison and application
  • Development of new and advanced active and passive optical and radio instruments
  • Calibration, validation, comparison and application of datasets from active and passive optical and radio instruments
  • Study of retrieval methods

Prof. Dr. Yajun Zhu
Prof. Dr. Weihua Bai
Prof. Dr. Jiyao Xu
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

  • GNSS-RO
  • GNSS-R
  • radar
  • lidar
  • airglow
  • data processing
  • calibration
  • validation

Published Papers (6 papers)

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15 pages, 3018 KiB  
Article
Seasonal Variation in the Mesospheric Ca Layer and Ca+ Layer Simultaneously Observed over Beijing (40.41°N, 116.01°E)
by Yuchang Xun, Peng Zhao, Zelong Wang, Lifang Du, Jing Jiao, Zhishan Chen, Haoran Zheng, Shaohua Gong and Guotao Yang
Remote Sens. 2024, 16(3), 596; https://doi.org/10.3390/rs16030596 - 05 Feb 2024
Viewed by 583
Abstract
In March 2020, an all-solid-state dual-wavelength narrow-band lidar system was deployed. A total of 226 nights spanning from March 2020 to July 2022 were employed in order to investigate the seasonal variations of calcium atoms and ions in the mesosphere over Beijing (40.41°N, [...] Read more.
In March 2020, an all-solid-state dual-wavelength narrow-band lidar system was deployed. A total of 226 nights spanning from March 2020 to July 2022 were employed in order to investigate the seasonal variations of calcium atoms and ions in the mesosphere over Beijing (40.41°N, 116.01°E). The Ca+ layer shows general annual variation, while a semiannual variation is observed on the Ca layer. The calcium atomic column densities ranged from 2.0 × 106 to 1.1 × 108 cm−2, and the calcium ion column densities ranged from 1.6 × 106 to 4.2 × 108 cm−2. The mean centroid heights of Ca+ and Ca are 98.6 km and 93.0 km, respectively, and the centroid heights of Ca+ and Ca are mostly influenced by annual variations. The seasonal variation in the Ca+ and Ca layers in Beijing exhibits similarities to that of Kühlungsborn (54°N). While the peak density of Ca+ in Beijing are similar to those observed in Kühlungsborn, the peak density of the Ca layer in Beijing is about half of that reported in the Ca layer at 54°N. We provide an explanation for the disparities in the column abundance and centroid altitude of the Ca layer between Yanqing and Kühlungsborn, discussing variations in neutralization among different metal ions. Full article
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18 pages, 3848 KiB  
Article
Cloud-to-Ground and Intra-Cloud Nowcasting Lightning Using a Semantic Segmentation Deep Learning Network
by Ling Fan and Changhai Zhou
Remote Sens. 2023, 15(20), 4981; https://doi.org/10.3390/rs15204981 - 16 Oct 2023
Cited by 1 | Viewed by 888
Abstract
Weather forecasting requires a comprehensive analysis of various types of meteorology data, and with the wide application of deep learning in various fields, deep learning has proved to have powerful feature extraction capabilities. In this paper, from the viewpoint of an image semantic [...] Read more.
Weather forecasting requires a comprehensive analysis of various types of meteorology data, and with the wide application of deep learning in various fields, deep learning has proved to have powerful feature extraction capabilities. In this paper, from the viewpoint of an image semantic segmentation problem, a deep learning framework based on semantic segmentation is proposed to nowcast Cloud-to-Ground and Intra-Cloud lightning simultaneously within an hour. First, a dataset with spatiotemporal features is constructed using radar echo reflectivity data and lightning observation data. More specifically, each sample in the dataset consists of the past half hour of observations. Then, a Light3DUnet is presented based on 3D U-Net. The three-dimensional structured network can extract spatiotemporal features, and the encoder–decoder structure and the skip connection can handle small targets and recover more details. Due to the sparsity of lightning observations, a weighted cross-loss function was used to evaluate network performance. Finally, Light3DUnet was trained using the dataset to predict Cloud-to-Ground and Intra-Cloud lightning in the next hour. We evaluated the prediction performance of the network using a real-world dataset from middle China. The results show that Light3DUnet has a good ability to nowcast IC and CG lightning. Meanwhile, due to the spatial position coupling of IC and CG on a two-dimensional plane, predictions from summing the probabilistic prediction matrices will be augmented to obtain accurate prediction results for total flashes. Full article
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18 pages, 9293 KiB  
Article
Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data
by Qi Liu, Shuangcheng Zhang, Weiqiang Li, Yang Nan, Jilun Peng, Zhongmin Ma and Xin Zhou
Remote Sens. 2023, 15(14), 3669; https://doi.org/10.3390/rs15143669 - 23 Jul 2023
Viewed by 1165
Abstract
Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and [...] Read more.
Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and all-weather operation. GNSS signal reflected at L-band also has significant advantages for SM estimation. Usually, SM is estimated based on the sensitivity of GNSS-R reflectivity to SM, but the noise in observations can significantly impact SM estimation results. A new SM retrieval method based on robust regression is proposed to address this issue in this work, and the effects of roughness and vegetation on the effective reflectivity of the Cyclone Global Navigation Satellite System (CyGNSS) are reconsidered. Ancillary data are provided by the SM Active Passive (SMAP) mission. The retrieved results from the training sets and test sets agree well with the referenced SMAP SM data. The correlation coefficient R is 0.93, the root mean square error (RMSE) is 0.058 cm3cm−3, the unbiased RMSE (ubRMSE) is 0.042 cm3cm−3, and the mean absolute error (MAE) is 0.040 cm3cm−3 in the training sets. For the test, the correlation coefficient is 0.91, the RMSE is 0.067 cm3cm−3, the ubRMSE is 0.051 cm3cm−3, and the MAE is 0.044 cm3cm−3. The proposed method has been evaluated using in situ measurements from the SMAP/in situ core validation site; in situ measurements and retrieval results exhibit good consistency with the ubRMSE value below 0.35 cm3cm−3. Moreover, the SM retrieval results using robust regression methods show better performance than CyGNSS official SM products that use linear regression. In addition, the land cover types significantly affect the accuracy of SM retrieval, and the incoherent scattering in densely vegetated areas (tropical forests) usually leads to more errors. Full article
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10 pages, 3614 KiB  
Technical Note
Mitigation of Calibration Ringing in the Context of the MTG-S IRS Instrument
by Pierre Dussarrat, Guillaume Deschamps, Bertrand Theodore, Dorothee Coppens, Carsten Standfuss and Bernard Tournier
Remote Sens. 2023, 15(11), 2873; https://doi.org/10.3390/rs15112873 - 31 May 2023
Cited by 2 | Viewed by 1008
Abstract
EUMETSAT is currently developing the on-ground processing chain of the infrared sounders (IRS) on-board the Meteosat third-generation sounding satellites (MTG-S). In this context, the authors investigated the impact of a particular type of radiometric error, called hereafter calibration ringing. It arises in Fourier [...] Read more.
EUMETSAT is currently developing the on-ground processing chain of the infrared sounders (IRS) on-board the Meteosat third-generation sounding satellites (MTG-S). In this context, the authors investigated the impact of a particular type of radiometric error, called hereafter calibration ringing. It arises in Fourier transform spectrometers when the instrument’s radiometric transfer function (RTF) varies within the domain of the instrument’s spectral response function (SRF). The expected radiometric errors were simulated in the context of the MTG-S IRS instrument in the long-wave infrared (LWIR) band. Making use of a principal components (PCs) decomposition, a software correction, called RTF uniformisation, was designed and its performance was assessed in the context of MTG-S IRS. Full article
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9 pages, 3468 KiB  
Technical Note
An Efficient Calibration System of Optical Interferometer for Measuring Middle and Upper Atmospheric Wind
by Guangyi Zhu, Yajun Zhu, Martin Kaufmann, Tiancai Wang, Weijun Liu and Jiyao Xu
Remote Sens. 2023, 15(7), 1898; https://doi.org/10.3390/rs15071898 - 31 Mar 2023
Viewed by 1172
Abstract
Detection of the Doppler shift of airglow radiation in the middle and upper atmosphere is one of the most important methods for remote sensing of the atmospheric wind field. Laboratory and routine field calibration of an optical interferometer for wind measurement is very [...] Read more.
Detection of the Doppler shift of airglow radiation in the middle and upper atmosphere is one of the most important methods for remote sensing of the atmospheric wind field. Laboratory and routine field calibration of an optical interferometer for wind measurement is very important. We report a novel calibration system that simulates a frequency shift of airglow emission lines introduced by wind in the middle and upper atmosphere for calibrating passive optical interferometers. The generator avoids the shortcomings of traditional motor-driven Doppler-shift generators in terms of stability and security while improving accuracy and simplifying assemblies. A simulated wind speed can be determined simultaneously using the light-beat method. The wind error simulated by the generator mainly comes from the light source, which is about 0.63 m/s. An experimental demonstration was conducted using a calibrated Fabry–Perot interferometer and showed that the root mean square of the measurement uncertainty is 0.91 m/s. The novel calibration system was applied to calibrate an asymmetric spatial heterodyne spectrometer (ASHS)-type interferometer successfully. The results demonstrate the feasibility of the system. Full article
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16 pages, 15503 KiB  
Technical Note
Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
by Cong Yin, Feixiong Huang, Junming Xia, Weihua Bai, Yueqiang Sun, Guanglin Yang, Xiaochun Zhai, Na Xu, Xiuqing Hu, Peng Zhang, Jinsong Wang, Qifei Du, Xianyi Wang and Yuerong Cai
Remote Sens. 2023, 15(4), 1097; https://doi.org/10.3390/rs15041097 - 17 Feb 2023
Cited by 3 | Viewed by 1767
Abstract
The reflected GNSS signals at the L-band is significantly advantageous in soil moisture monitoring as they are sensitive to the dielectric properties determined by the volumetric water content of topsoil, and they can penetrate vegetation, except in very dense forests. The Global Navigation [...] Read more.
The reflected GNSS signals at the L-band is significantly advantageous in soil moisture monitoring as they are sensitive to the dielectric properties determined by the volumetric water content of topsoil, and they can penetrate vegetation, except in very dense forests. The Global Navigation satellite system Occultation Sounder (GNOS-II) with a reflectometry technique onboard the Fengyun-3E (FY-3E) satellite, launched on 5 July 2021, is the first mission that can receive reflected Global Navigation Satellite System (GNSS) signals from GPS, BeiDou and Galileo systems. This paper presents the soil moisture retrieval results from the FY-3E GNOS-II mission using 16 months of data. In this study, the reflectivity observations from different GNSS systems were firstly intercalibrated with some differences analyzed. Observations were also corrected by considering vegetation attenuation for 16 different land cover classifications. Finally, an empirical model was constructed for volumetric soil moisture (VSM) estimation, where the reflectivity of GNOS-II was linearly related to the SMAP reference soil moisture for each 36 km ease grid. The overall root-mean-square error of the retrieved soil moisture is 0.049 compared with the SMAP product, and 0.054 compared with the in situ data. The results of the three GNSS systems show similar levels of accuracy. This paper, for the first time, demonstrates the feasibility of global soil moisture retrieval using multiple GNSS signals. Full article
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