Dear Colleagues,

Inland water bodies around the world, such as lakes, reservoirs, rivers, canals, and ponds, play a crucial role in sustaining life, providing human well-being, supporting ecosystems, and ensuring water security for millions of people worldwide. However, in recent years, these valuable resources are under increasing pressure under the background of climate change, population growth, urbanization, and industrial activities. The ability to comprehensively monitor and assess the status and dynamics of inland water bodies (lakes, rivers, and reservoirs) from a local to global scale using remote sensing has become a critical challenge for hydrological, ecological, and environmental researchers, managers, and policy makers.

Remote sensing and artificial intelligence (AI) have emerged as powerful tools in addressing these above mentioned challenges. Remote sensing technologies, encompassing optical, thermal, radar, and lidar sensors aboard satellites and other platforms, offer the capability to acquire frequent, synoptic, and multidimensional data across large geographic areas over a long period with a given revisit frequency. When coupled with widely used AI techniques, such as machine learning and deep learning, these remote sensing datasets can be efficiently processed and analyzed to extract and invert valuable information (such as water area, water level, water storage, water quality, and wetland area) from inland water bodies across the globe. In addition, the obtained water-related information can further support water resource monitoring, assessment, management, and policy making.

• Global-scale inland water body mapping and monitoring by remote sensing;
• Artificial intelligence and machine learning approaches for water body detection and classification;
• Estimation of water quality parameters from remote sensing data;
• Remote sensing and AI in monitoring wetland ecosystems;
• Monitoring changes in lake and river hydrology;
• Synergistic use of multisource remote sensing data for water resource assessment;
• Fusion of multisource remote sensing data for inland water studies;
• Integration of remote sensing and GIS in water resource management.

Dr. Nan Xu
Dr. Xin Li
Dr. Junfeng Xiong
Dr. Linyang Li
Dr. Armin Moghimi
Dr. Arfan Arshad
Guest Editors

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• remote sensing
• inland water
• artificial intelligence
• water resource
• hydrology
• machine learning
• global change and regional response