Recent Advantages in Monitoring Inland Water Using Various Sources of Remote Sensing Imagery from Space
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 2877
Special Issue Editors
Interests: spatiotemporal image fusion; multi-sensor and multi-data fusion and its application; superresolution land cover mapping
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; water cycle; carbon cycle; wetlands
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Monitoring the spatiotemporal dynamics of surface water is essential for understanding water’s impact on climate change and the global ecosystem. Multi-source remote sensing imagery, including optical and Synthetic Aperture Radar (SAR) sensors, have significantly advanced the monitoring of inland surface water at very high spatial and temporal resolutions. However, due to the sensors’ limitations and the environment's complexity, there are often significant challenges in monitoring inland water. Many advanced techniques, including artificial intelligence, image fusion, deep learning, image super-resolution, and gap filling, have been proposed to monitor inland water and analyse the spatiotemporal patterns of surface water. However, several challenges and open problems still await solutions and novel methodologies. The main goal of this Special Issue is to address advanced topics related to:
- Advanced machine learning and deep learning methods in monitoring inland water;
- The monitoring of water bodies with increased spatiotemporal resolutions based on data fusion;
- Monitoring water bodies based on MODIS, Landsat, Sentinel, PlanetScope, etc.;
- The spatiotemporal mapping of floods;
- Mapping typical small water bodies in different regions;
- Water-body-related DEM and surface water occurrence studies;
- The impact of climate change and human activities on inland water bodies.
Prof. Dr. Xiaodong Li
Dr. Frédéric Frappart
Guest Editors
Manuscript Submission Information
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Keywords
- inland surface water
- time series analysis
- artificial intelligence
- data fusion
- small water bodies and floods
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