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Remote Sensing
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Retrieval and Validation of Trace Gases Using Remote Sensing Measurements
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Retrieval and Validation of Trace Gases Using Remote Sensing Measurements

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 9484

Special Issue Editor

Dr. Lok Lamsal

E-Mail Website
Guest Editor
NASA Goddard Space Flight Center, Universities Space Research Association, Washington, DC, USA
Interests: retrieval and validation of trace gases from satellite measurements; interpretation of satellite observations and model simulation for surface air quality and emissions; radiative transfer

Special Issue Information

Dear Colleagues,

Remote sensing trace gas observations from satellites are increasingly used in a wide range of applications. Development of new instruments and advances in remote sensing techniques and retrieval methods have enabled improvements in spatial, temporal, and vertical characterization of trace gases. Different observation platforms, measurement techniques, and retrieval methods yield different sampling of the atmosphere in both space (horizontal and vertical) and time as well as different sensitivity to atmospheric and observational parameters. To maximize science, applications, and societal benefits from these capabilities, it is crucial to carry out well-established evaluation strategies, including a thorough analysis when comparing measurement-derived geophysical parameters with correlative observations acquired by independent instrumentations from various platforms such as ground-based stations, ships, aircraft, balloons, and satellites. Proper validation not only ensures maturity in the quality of data but also serves as a diagnostic tool for improving retrieval algorithms. This Special Issue focuses on compiling various scientific works related to retrieval and validation of trace gases from orbital, suborbital, and ground-based remote sensing instruments.

Dr. Lok Lamsal
Guest Editor

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

  • Remote sensing
  • Retrieval methods
  • Trace gases
  • Validation
  • Observation network
  • Field campaign
  • Satellite intercomparison

Published Papers (3 papers)

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Research

28 pages, 7807 KiB  
Article
Impact of Molecular Spectroscopy on Carbon Monoxide Abundances from TROPOMI
by Philipp Hochstaffl, Franz Schreier, Manfred Birk, Georg Wagner, Dietrich G. Feist, Justus Notholt, Ralf Sussmann and Yao Té
Remote Sens. 2020, 12(21), 3486; https://doi.org/10.3390/rs12213486 - 23 Oct 2020
Cited by 4 | Viewed by 2800
Abstract
The impact of SEOM–IAS (Scientific Exploitation of Operational Missions–Improved Atmospheric Spectroscopy) spectroscopic information on CO columns from TROPOMI (Tropospheric Monitoring Instrument) shortwave infrared (SWIR) observations was examined. HITRAN 2016 (High Resolution Transmission) and GEISA 2015 (Gestion et Etude des Informations Spectroscopiques Atmosphériques 2015) [...] Read more.
The impact of SEOM–IAS (Scientific Exploitation of Operational Missions–Improved Atmospheric Spectroscopy) spectroscopic information on CO columns from TROPOMI (Tropospheric Monitoring Instrument) shortwave infrared (SWIR) observations was examined. HITRAN 2016 (High Resolution Transmission) and GEISA 2015 (Gestion et Etude des Informations Spectroscopiques Atmosphériques 2015) were used as a reference upon which the spectral fitting residuals, retrieval errors and inferred quantities were assessed. It was found that SEOM–IAS significantly improves the quality of the CO retrieval by reducing the residuals to TROPOMI observations. The magnitude of the impact is dependent on the climatological region and spectroscopic reference used. The difference in the CO columns was found to be rather small, although discrepancies reveal, for selected scenes, in particular, for observations with elevated molecular concentrations. A brief comparison to Total Column Carbon Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) also demonstrated that both spectroscopies cause similar columns; however, the smaller retrieval errors in the SEOM with Speed-Dependent Rautian and line-Mixing (SDRM) inferred CO turned out to be beneficial in the comparison of post-processed mole fractions with ground-based references. Full article
(This article belongs to the Special Issue Retrieval and Validation of Trace Gases Using Remote Sensing Measurements)
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17 pages, 3807 KiB  
Article
Retrieval and Validation of XCO2 from TanSat Target Mode Observations in Beijing
by Zhengyi Bao, Xingying Zhang, Tianxiang Yue, Lili Zhang, Zong Wang, Yimeng Jiao, Wenguang Bai and Xiaoyang Meng
Remote Sens. 2020, 12(18), 3063; https://doi.org/10.3390/rs12183063 - 18 Sep 2020
Cited by 8 | Viewed by 2949
Abstract
Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan’s Greenhouse Gases Observing SATellite (GOSAT), the USA’s Orbiting Carbon [...] Read more.
Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan’s Greenhouse Gases Observing SATellite (GOSAT), the USA’s Orbiting Carbon Observatory-2 (OCO-2), and China’s Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China’s State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS. Full article
(This article belongs to the Special Issue Retrieval and Validation of Trace Gases Using Remote Sensing Measurements)
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25 pages, 19776 KiB  
Article
Impact of Molecular Spectroscopy on Carbon Monoxide Abundances from SCIAMACHY
by Philipp Hochstaffl and Franz Schreier
Remote Sens. 2020, 12(7), 1084; https://doi.org/10.3390/rs12071084 - 27 Mar 2020
Cited by 5 | Viewed by 3189
Abstract
High-quality observations have indicated the need for improved molecular spectroscopy for accurate atmospheric characterization. Line data provided by the new SEOM-IAS (Scientific Exploitation of Operational Missions—Improved Atmospheric Spectroscopy) database in the shortwave infrared (SWIR) region were used to retrieve CO total vertical columns [...] Read more.
High-quality observations have indicated the need for improved molecular spectroscopy for accurate atmospheric characterization. Line data provided by the new SEOM-IAS (Scientific Exploitation of Operational Missions—Improved Atmospheric Spectroscopy) database in the shortwave infrared (SWIR) region were used to retrieve CO total vertical columns from a selected set of nadir SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY) observations. In order to assess the quality of the retrieval results, differences in the spectral fitting residuals with respect to the HITRAN 2016 (High-resolution TRANsmission molecular absorption) and GEISA 2015 (Gestion et Etude des Informations Spectroscopiques Atmosphériques) line lists were quantified and column-averaged dry-air CO mole fractions were compared to NDACC (Network for the Detection of Atmospheric Composition Change) and TCCON (Total Carbon Column Observing Network) ground-based measurements. In general, it was found that using SEOM-IAS line data with corresponding line models improve the spectral quality of the retrieval (smaller residuals) and increase the fitted CO columns, thereby reducing the bias to both ground-based networks. Full article
(This article belongs to the Special Issue Retrieval and Validation of Trace Gases Using Remote Sensing Measurements)
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