Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 4
Special Issue Editors
Interests: hydrology; water resources; remote sensing; geography; sustainability
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image processing; pansharpening; multimodal data fusion
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing of water environment; trophic status assessment
Special Issues, Collections and Topics in MDPI journals
Interests: computer communications (networks); programming languages; databases; remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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 have come under increasing pressure due to 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) at local to global scales using remote sensing has become a critical challenge for hydrological, ecological, and environmental researchers, managers, and policymakers.
Remote sensing and artificial intelligence (AI) have emerged as powerful tools for addressing the above-mentioned challenges. Remote sensing technologies, encompassing optical, thermal, radar, and lidar sensors aboard satellites and other platforms, enable the acquisition of frequent, synoptic, and multidimensional data across large geographic areas over long periods at 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 (e.g., water area, water level, water storage, water quality, and wetland area) from inland water bodies worldwide. 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
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- remote sensing
- inland water
- artificial intelligence
- water resource
- hydrology
- machine learning
- global change and regional response
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