Spatio-Temporal Land Surface Temperature Retrieval Based on Ground-Based, Satellite Observations and Artificial Intelligence
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 91
Special Issue Editors
2. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: retrieval and validation of land surface temperature/emissivity; retrieval and validation of net surface radiation; radiative transfer modeling; quantitative estimation of land surface variables from middle infrared data; hyperspectral thermal infrared data analysis
Special Issues, Collections and Topics in MDPI journals
Interests: retrieval and validation of land surface temperature; radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Land Surface Temperature (LST) is a key physical variable in understanding land–atmosphere interactions, surface energy balance, and climate dynamics. Accurate retrieval of spatio-temporal LST plays a vital role in monitoring global climate change, water cycle processes, ecosystem health, and human–environment interactions. Despite advances in satellite, reanalysis, and ground-based observations, challenges remain in producing long time-series, high-resolution, space-time continuous, and accurate LST data, particularly in heterogeneous or mountainous regions. Recent developments in artificial intelligence (AI) provide promising avenues for addressing these challenges through enhanced modeling, data fusion, and gap-filling techniques. The integration of ground-based measurement, satellite observation, and artificial intelligence to achieve accurate LST retrieval in time and space is an important development direction for the current surface temperature extension and application.
This Special Issue aims to bring together cutting-edge research focused on innovative methods for spatio-temporal LST retrieval by integrating ground-based measurements, satellite remote sensing, and AI technologies. It aligns closely with the journal’s scope by advancing remote sensing applications, algorithm development, and Earth system monitoring. Contributions that enhance the accuracy, resolution, and applicability of LST products in environmental and climate-related fields are especially welcome.
Articles may address, but are not limited to, the following topics:
- LST retrieval algorithms based on multi-source data
- The coupling of artificial intelligence and physical models
- AI and machine learning approaches for LST reconstruction
- LST retrieval in complex terrains or under extreme conditions
- Validation and uncertainty analysis of LST products
- Applications of LST in hydrology, ecology, and climate studies
Article types include original research, reviews, methodological papers, and case studies.
Dr. Bo-Hui Tang
Dr. Xiangyang Liu
Dr. Tian Hu
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
- land surface temperature (LST)
- retrieval algorithm
- complex terrain
- urban area
- ground-based observation
- artificial intelligence
- validation
- reconstruction of data under clouds
- temporal scale expansion
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
