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Advanced Retrieval and Estimation Methods for Monitoring Air Pollutants via Satellite Remote Sensing

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 800

Editors

Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Interests: satellite remote sensing; atmospheric pollution

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Guest Editor
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Interests: atmospheric remote sensing inversion; deep learning; atmospheric pollution; satellite remote sensing

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Guest Editor
Anhui Institute of Meteorological Sciences, Hefei 230031, China
Interests: satellite remote sensing; atmospheric pollution; anthropogenic emissions; ozone pollution; trace gas inversion

Special Issue Information

Dear Colleagues,

Satellite remote sensing has become an indispensable technology for monitoring atmospheric pollutants from regional to global scales. With the launch of advanced hyperspectral satellites and geostationary satellites, the capability to detect trace gases and particulate matter has significantly improved, enabling finer spatiotemporal resolution and enhancing atmospheric sensing capabilities. On the one hand, these advancements drive the demand for developing high-precision inversion algorithms tailored to sophisticated platforms. On the other, they open up extensive application domains, including but not limited to point source identification and emission quantification, air pollutant transport and spatiotemporal evolution tracking, monitoring of extreme pollution events, climate change impact assessment, and the evaluation of population exposure and associated health risks. This research is vital for enhanced global air quality monitoring, pollution control policies, exposure assessment, and informed environmental management worldwide.

This Special Issue focuses on the development and application of retrieval and estimation methods for monitoring air pollution using diverse satellite platforms. We welcome contributions covering a wide range of topics, including but not limited to novel satellite retrieval algorithms, AI-driven analytical techniques, multi-source data fusion, and applied studies utilizing satellite-derived results.

Suggested themes and article types for submissions:

  • Satellite Remote Sensing;
  • Air Pollutant Transport;
  • Spatiotemporal Evolution of Air Pollution;
  • Multi-Source Data Fusion Based on Satellite Observations;
  • AI and Data-Driven Techniques Based on Satellite Observations;
  • Impact Assessment on Health, Climate, and Policy.

Dr. Wenjing Su
Dr. Nana Luo
Dr. Yujia Chen
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • satellite remote sensing
  • air pollution monitoring
  • retrieval algorithm
  • satellite applications
  • AI

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Published Papers (1 paper)

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Research

19 pages, 10207 KB  
Article
Application of the Fast Atmospheric Line-by-Line Code with Aerosol and Cloud Scattering (FALCAS) to TROPOMI Total Column Water Vapour Retrievals in the SWIR Band
by Handeul Son, Dmitry S. Efremenko and Philipp Hochstaffl
Remote Sens. 2026, 18(8), 1180; https://doi.org/10.3390/rs18081180 - 15 Apr 2026
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Abstract
Fast radiative transfer models are essential for the efficient processing of hyperspectral satellite data in trace gas retrievals, as full multi-stream radiative transfer simulations are computationally demanding. We present FALCAS (Fast Atmospheric Line-by-line Code with Aerosol and Cloud Scattering), a surrogate forward model [...] Read more.
Fast radiative transfer models are essential for the efficient processing of hyperspectral satellite data in trace gas retrievals, as full multi-stream radiative transfer simulations are computationally demanding. We present FALCAS (Fast Atmospheric Line-by-line Code with Aerosol and Cloud Scattering), a surrogate forward model combining line-by-line radiative transfer with the virtual isotropic scattering layer approximation adopted from FOCAL. FALCAS retains much of the accuracy of full multi-stream calculations while enabling rapid simulations. Previously validated against synthetic spectra from a discrete ordinate radiative transfer model, FALCAS is here applied to real measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to retrieve total column water vapour (TCWV) in the shortwave infrared band around 2.3 μm. Retrieval results are compared to the operational TROPOMI Level-2 TCWV from the CH4 product. As this comparison is performed against an operational product from the same instrument, it represents an intercomparison rather than an evaluation against an independent reference dataset. FALCAS retrievals show a Pearson correlation coefficient greater than 0.99 with the operational data, and after empirical bias correction, the mean absolute bias across all regions is 1.45 mol m−2 (0.12% relative) and the mean RMSE is 39.24 mol m−2 (3.85% relative). These results demonstrate that FALCAS shows strong agreement with the operational TROPOMI Level-2 TCWV product, offering substantial computational advantages for large-scale processing. Full article
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