Recent Advantages in Monitoring Inland Water Using Various Sources of Remote Sensing Imagery from Space

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

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