Integrating Remote Sensing and Artificial Intelligence for Hydrological Variables Retrieval
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 16 March 2026 | Viewed by 81
Special Issue Editors
Interests: satellite altimetry for inland waters and ice sheets; river discharge estimation using multisource remote sensing and hydrological modelling
Interests: remote sensing; soil moisture; hydrology; radar and radiometry; water cycle; climate change
Special Issues, Collections and Topics in MDPI journals
Interests: disaster remote sensing; spatiotemporal fusion of multi-source disaster data; intelligent assessment of disaster risk
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Special Issue Information
Dear Colleagues,
The availability and distribution of global water resources are increasingly being threatened by climate change and human activities. In this context, accurate monitoring of hydrological variables (e.g., precipitation, soil moisture, streamflow, evapotranspiration, terrestrial water storage, and snow and ice) is critical for sustainable water resources management. While remote sensing has revolutionized our ability to observe the water cycle at large scales, it still faces significant challenges, including high uncertainty in complex terrains (e.g., mountainous regions), trade-offs between spatial and temporal resolution, and cloud contamination for optical images. Meanwhile, artificial intelligence (AI) has emerged as a powerful tool for hydrological sciences. However, data-driven biases, lack of physical interpretability, and overfitting risks are among the most critical limitations of pure AI approaches. The cutting-edge remote sensing platforms and diverse observation modes, coupled with advanced machine learning algorithms, have the potential to overcome these limitations and open up new avenues in monitoring and modelling hydrological processes.
This Special Issue aims to bridge the knowledge gap through synergistic integration of state-of-the-art remote sensing techniques with AI-driven methods for monitoring the key flux and state variables of the hydrological cycle. We welcome contributions that leverage satellite, airborne, and UAV-based remote sensing data, along with machine learning, deep learning, and data assimilation techniques, to enhance the accuracy and efficiency of hydrological variable estimation and modelling. This collection aims to deepen our understanding of the complex hydrological processes and provide implications for water sustainability under the changing environment.
Topics of interest include, but are not limited to, the following areas:
- Reviews of models, methods, products, and applications of remote sensing and AI in hydrology.
- AI-driven retrieval of hydrological variables (e.g., precipitation, soil moisture, streamflow, evapotranspiration, terrestrial water storage, and snow and ice) from satellite and airborne observations.
- AI-assisted calibration and parameter optimization of hydrological and land surface models using remotely sensed information.
- Data assimilation of multi-sensor (e.g., optical, SAR (off-nadir view), altimetry (nadir view), SWOT (near-nadir view), and LiDAR) satellite observations and AI into hydrological and land surface models for water cycle monitoring.
- Hydrological applications (e.g., flood forecasting and drought monitoring) using remote sensing data and AI.
Dr. Qi Huang
Dr. Jiangyuan Zeng
Prof. Dr. Xiang Zhang
Dr. Stefano Vignudelli
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- remote sensing
- data assimilation
- water cycle
- water resources
- hydrology
- hydrological processes
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