Remote Sensing Data Refinement and Utilization for Advanced Atmospheric Observations
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 10 August 2026
Special Issue Editors
Interests: aerosol parameters retrieval and validation; calibration; polarization; surface reflectance retrieval; multi-angle remote sensing; atmosphere remote sensing
Interests: polarimetry; polarization remote sensing instrument; atmosphere correction
Interests: satellite; in-flight calibration; polairzation
Special Issue Information
Dear Colleagues,
With the rapid advancement of remote sensing technology, satellite data has become an indispensable tool for global and regional atmospheric monitoring. To ensure the accuracy and reliability of satellite-derived products, continuous research on data validation, calibration, and innovative applications is crucial. This Special Issue is the third in this series, aiming to gather the latest progress in the field, promote algorithm improvements, uncertainty assessment, and application expansion, thereby providing a more solid data foundation for climate research, environmental monitoring, and weather forecasting.
This Special Issue aims to provide a platform for researchers to share the latest achievements in the application, validation, calibration, and related methodological developments using satellite data in the field of atmospheric observation. The content must align with the aims and scope of the journal (Remote Sensing), focusing on high-quality research.
Suggested themes and article types for submissions:
- Novel retrieval algorithms for atmospheric components (e.g., aerosols, trace gases, clouds) from satellite remote sensing
- Ground-based validation and inter-comparison studies of satellite atmospheric products
- On-orbit performance monitoring and calibration techniques for satellite sensors
- Atmospheric correction
- Polarization atmosphere
- Applications of multi-source satellite data fusion and assimilation in atmospheric sciences
- Research on climate change and atmospheric environmental processes based on satellite data
- Applications of machine learning and artificial intelligence in processing satellite atmospheric data
- Data evaluation and application from emerging satellite missions (e.g., hyperspectral, LiDAR)
Dr. Bangyu Ge
Dr. Zhenwei Qiu
Dr. Sifeng Zhu
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-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
- satellite remote sensing
- atmospheric observation
- data validation
- sensor calibration
- atmospheric components
- retrieval algorithms
- data assimilation
- climate change
- environmental monitoring
- machine learning
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