AI-Enhanced Remote Sensing and Land Surface Modeling for Terrestrial Hydrology and Climate Systems

Dear Colleagues,

The terrestrial water and climate system is undergoing rapid change due to both natural variability and human activities. Accurately determining, monitoring, and modeling these changes is crucial for understanding the dynamics of the water cycle, predicting hydrological extremes, and managing ecosystem and water resources sustainably. The recent convergence of remote sensing, land surface and climate modeling, and AI for science offers unprecedented opportunities to advance research in this area.

In particular, the development of Remote Sensing Foundation Models and AI-based data fusion techniques allows for large-scale, high-resolution, and temporally consistent observations of key hydrological and climatic variables. These include, but are not limited to, evapotranspiration, soil moisture, precipitation, snow cover, discharge, water level, glacial and permafrost changes, and drought dynamics. Coupling these data with land surface, hydrological, and climate models enables improved simulation, prediction, and process understanding of complex terrestrial physical processes.

This Special Issue aims to foster interdisciplinary contributions that integrate remote sensing, AI techniques, and process-based modeling to better characterize, simulate, and attribute land–atmosphere interactions under global environmental change. Topics of interest include, but are not limited to, the following:

• Development and application of Remote Sensing Foundation Models in hydrological and climate studies.
• AI-based fusion of multi-source remote sensing and observational data for comprehensive monitoring of key hydrological and climatic variables.
• Coupling AI-enhanced remote sensing with land surface, hydrological, and climate models to improve simulation and prediction of terrestrial physical processes.
• Detection and attribution of hydroclimatic extremes (e.g., droughts, floods) using AI-integrated observation model frameworks.
• Long-term changes in terrestrial hydrological and climatic variables (e.g., water storage, snow cover, ET, precipitation) revealed by remote sensing and machine learning.
• Impacts of human–environment interactions (e.g., irrigation, water regulation, land use change) on regional climate, water cycle, and ecological environment assessed through AI-enhanced remote sensing and modeling.

Dr. Ya Huang
Dr. Yuyan Zhou
Dr. Qing Yang
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.