Advancing Earth Observation Through Artificial Intelligence: From Foundation Models to Intelligent Retrieval Systems
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".
Deadline for manuscript submissions: 15 February 2026 | Viewed by 9
Special Issue Editors
Interests: earth system science; climate change; climate services; regional climate processes
Interests: ML; radar; weather prediction
Special Issue Information
Dear Colleagues,
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming the landscape of Earth Observation (EO), offering unprecedented capabilities to process, interpret, and exploit satellite data. This Special Issue aims to showcase top-notch research that leverages AI/ML to enhance the utility, precision, and accessibility of remote sensing data across atmospheric, oceanic, and land domains.
We invite contributions that address core challenges and opportunities at the intersection of AI and EO, including the development of foundation models, the emulation of complex physical processes, and the creation of AI-native retrieval and detection algorithms.
Key topics include, but are not limited to, the following:
- Foundation Models for Earth Observation: Large-scale, pre-trained models that can be fine-tuned for diverse EO tasks (e.g., cloud masking, land cover classification, anomaly detection).
- AI for data fusion and data assimilation: How AI-powered data fusion and Assimilation boost satellite data utilization.
- Satellite Retrieval Algorithms: ML-enabled retrievals of geophysical variables from satellite observations, including hybrid physical–ML approaches.
- Radiative Transfer Emulators: Use of AI to emulate radiative transfer models with high fidelity and computational efficiency, enabling faster data assimilation and retrieval.
- Data Curation for AI: Strategies for curating high-quality, diverse, and AI-ready EO datasets, including labeling strategies, bias mitigation, and uncertainty quantification.
- Super-Resolution in Remote Sensing: ML techniques to enhance spatial resolution from coarse satellite measurements, with applications to urban, agricultural, and cryospheric monitoring.
- Radar Proxy Learning: Leveraging ML to emulate or augment radar observations (e.g., precipitation or surface scattering) using multisensor data and physical constraints.
- Cloud Detection and Scene Classification: AI-powered approaches for robust cloud detection, discrimination of cloud types, and scene understanding across multiple spectral and temporal resolutions.
Dr. Paolo M. Ruti
Dr. Claudia Acquistapace
Prof. Dr. Andrew C Parnell
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
- earth observations
- foundation models
- radiative transfer
- meteorology
- machine learning
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