Advances in Artificial Intelligence and Multi-Source Remote Sensing for Surface Hydrology
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: 31 August 2026 | Viewed by 2159
Special Issue Editors
Interests: altimetry; cascade reservoir; remote sensing; surface hydrology
Special Issue Information
Dear Colleagues,
Surface water resources—rivers, lakes, and reservoirs—are vital for ecosystems, socio-economic development, and human well-being. Accurate, timely monitoring of water level, area, and storage is key to water management, disaster mitigation, and climate adaptation. Advances in multi-source remote sensing (e.g., Sentinel-1/2, Sentinel-3A/B, SWOT, GRACE), combined with modeling, have greatly enhanced large-scale observation of surface water, especially in data-scarce remote or transboundary basins. Emerging tools—satellite altimetry, hydrological/hydrodynamic modeling, and AI-driven data fusion—further improve runoff simulation and event detection, aiding complex settings like cascade reservoirs and extreme events such as floods and droughts. Amid accelerating climate change, integrating multi-source Earth observation for monitoring, modeling, and early warning is a critical and impactful research frontier.
This Special Issue brings together advanced research and innovative applications in surface water monitoring and modeling using multi-source remote sensing. We welcome studies that integrate diverse satellite missions—such as radar, optical, altimetry, gravimetry, and the recently launched SWOT—with hydrological, hydrodynamic, and AI-based models to address water-related challenges under climate change. For example, SWOT’s high-resolution, wide-coverage observations provide unprecedented opportunities to investigate surface water dynamics and emerging scientific questions, while artificial intelligence techniques applied to multi-source data fusion and pattern recognition are significantly enhancing the accuracy and efficiency of hydrological process modeling and extreme event monitoring. Emphasizing both methodological advances and practical applications for water level, area, and storage estimation, as well as monitoring droughts and floods, this Special Issue provides a key platform for researchers at the interface of remote sensing, hydrology, and climate science.
- Multi-source remote sensing of surface water (Sentinel-1/2, Sentinel-3A/B, SWOT, GRACE, ICESat-2, Landsat, etc.)
- Integration of satellite altimetry, gravimetry, optical and radar data for water level, area, and storage monitoring
- Data fusion and modeling approaches to improve runoff and hydrological process simulations in data-scarce basins
- Monitoring and modeling cascade reservoirs and transboundary river basins
- AI and machine learning applications in multi-source Earth observation data integration for hydrology
- Climate change impacts on surface water dynamics and related hazards
- Remote sensing-based drought, flood, and hydrological extremes monitoring and early warning
- Validation and calibration of remote sensing products with in-situ measurements and models
- Other related topics are also welcome
Dr. Xingxing Zhang
Dr. Liguang Jiang
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.
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Keywords
- multi-source remote sensing
- surface water monitoring
- satellite altimetry
- SWOT mission
- GRACE gravimetry
- hydrological modeling
- AI-based methods
- climate change impacts
- flood and drought monitoring
- transboundary river basins
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