Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 351
Special Issue Editors
Interests: artificial intelligence; deep learning; retrieval paradigm; soil moisture retrieval; land surface temperature retrieval; water vapor content retrieval; near surface temperature retrieval
Special Issues, Collections and Topics in MDPI journals
Interests: satellite data processing; land surface product algorithm; remote sensing classification with machine learning; agrometeorology; agrometeorological disater monitoring with remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: land surface models; remote sensing; snow accumulation; snow water equivalent; assimilation of observations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, artificial intelligence has become the core driving force behind a new wave of industrial transformation; this will further unleash the enormous energy of technological innovation. The combination of artificial intelligence and specific industries will lead to the emergence of novel technologies and products, profoundly changing human thinking and production patterns, and achieving an overall leap in social and industrial productivity. Considering the potential and significance of deep learning in the fields of geology and agriculture, in order to promote the application of artificial intelligence, it is necessary to accelerate the deep integration of this technology with remote sensing, provide key technical support for meteorological forecasting, agricultural monitoring, and agricultural disaster prediction, and thus facilitate global disaster monitoring and food security. Cross-disciplinary research is still in its early stages, and most deep learning applications in the geosciences remain largely opaque, often lacking physical significance, interpretability, and universality.
Therefore, the first volume of this Special Issue of Remote Sensing was published to explore the application of artificial intelligence methods for retrieving key remote sensing parameters in geology and agriculture. Topics for the second edition may cover a range of subjects, from the retrieval of surface temperature or soil moisture to atmospheric water vapor content and rainfall. Building on the success of the first volume, we are excited to invite original research and review articles that discuss the progress, challenges, and opportunities in the remote sensing of agrometeorological phenomena for publication in the second volume.
Hence, submissions describing remote sensing parameters retrieved from multi-source data (such as multispectral, hyperspectral, thermal infrared, and microwave) at multiple scales are welcome. Articles may address, but are not limited to, the following topics:
- surface temperature;
- near-surface air temperature;
- surface emissivity;
- soil moisture;
- vegetation moisture content;
- water vapor content;
- precipitation;
- LAI;
- drought and flood.
Prof. Dr. Kebiao Mao
Dr. Sayed M. Bateni
Dr. Jongmin Park
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
- surface temperature
- near-surface air temperature
- surface emissivity
- soil moisture
- vegetation moisture content
- water vapor content
- precipitation
- LAI
- drought and flood
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