Dear Colleagues,

Inland and coastal water bodies are crucial for various services for human societies. Under the context of a changing climate and intensified human interventions, the quality and quantity of water bodies has evidently been changing. Satellite remote sensing is an efficient and crucial tool for monitoring and sustainable management of those water resources. However, it is still very challenging for algorithm development and various applications due to the sensor’s electromagnetic interaction with the atmosphere and complex substances in waters. In recent years, research on remote sensing of inland water color has greatly increased. However, the water mass has to some extent been less focused on. Meawhile, the rapid development of mathematic techniques (e.g., machine learning) and cloud computation platforms (e.g., Google Earth Engine) provides new opportunities to improve the capacity of satellite remote sensing for water monitoring. There is a clear need to share approaches and new ideas that can be used to strenthen the approach of investigating water quality or water storage.

To meet this urgent need, a Special Issue on “Monitoring Waters from Space” has been accepted by the leading international journal Sensors, to address the technical challenges for satellite monitoring or estimating of water bodies.

We sincerely solicit your contributions in this field to our Sensors Special Issue. Research or review articles with respect to the following topics are welcome:

• Waterbody delineation from remote sensing imagery;
• Water volume estimation by remote sensing;
• Satellite monitoring of inland water resources;
• Remote sensing retrival of water corlor/quality parameters;
• Machine learning applications on remote sensing of water environment;
• Google Earth Engine-based remote sensing of water environment;
• Satellite mapping of aquatic macrophytes in inland waters;
• Long-term satellite monitoring of watershed LUCC linked to water quality or storage.

Dr. Ronghua Ma
Dr. Chunqiao Song
Guest Editors

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• water volume
• water quality
• remote sensing
• algorithm development
• environmental monitoring
• machine learning