Multi-Scale Remote Sensing and Machine Learning for Hydrological Modeling
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 1169
Special Issue Editors
Interests: large-scale hydrological modeling; data-driven methods; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: data assimilation; satellite remote sensing; land surface modelling; model calibration; hydrographic mapping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing provides rich spatiotemporal information on changing hydrological processes and their environmental impacts. The dynamics of terrestrial water storage (rivers, lakes, reservoirs) and snow cover, soil moisture, and certain atmospheric variables of land surface–atmosphere exchange (such as those related to rainfall or evapotranspiration) can be evaluated on a temporal and spatial scale using a variety of airborne and spaceborne instruments. For example, MODIS, AMSR-E, Sentinel, SMOS, SMAP, GRACE and other remote sensing technologies are used for the calibration and verification of hydrological models.
Furthermore, machine learning has been successfully used in almost every field of science and technology. With the assistance of remote sensing data, it has also been widely used in hydrological basin cycles and meteorological prediction, and different types of hydrological parameters have been implemented in computational models, mathematical models, and systems. Advanced data-driven algorithms properly combine multisource products (measurements, satellite images, and reanalysis) with hydrological models, which further improves their performance and prediction accuracy.
It is our pleasure to announce the launch of a new Special Issue in Remote Sensing. Research topics include but are not limited to collecting papers that apply to remote sensing and retrieval methods (innovative machine learning and data assimilation techniques) in hydrological environment assessment and hydrological modeling, including innovative technologies for evaluating hydrological models, new insights into inland water processes, integration in hydrological modeling, etc.
Dr. Jun Liu
Dr. Mehdi Khaki
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
- spatial hydrology
- large-scale modeling
- inland water bodies
- land–atmosphere interaction
- data assimilation
- remote sensing
- machine learning
- calibration
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.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue policies can be found here.
