Advancing Water System with Satellite Observations and Deep Learning
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 940
Special Issue Editors
Interests: data assimilation; hydrological modeling; remote sensing image classification; soil moisture estimation; snow cover assimilation; wildfire detection algorithms; satellite-derived observations
Interests: classification algorithms; remote sensing technology; deep learning reomte sensing classification; land use/cover; remote sensing classification
Interests: hydrological remote sensing; hydrological data assimilation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This special issue explores the transformative synergy between satellite observations and deep learning techniques in advancing water systems management. Water, a vital resource essential for sustaining life and supporting ecosystems, faces increasing pressures from climate change, urbanization, and population growth. Satellite observations provide unprecedented spatial and temporal coverage, enabling comprehensive monitoring of hydrological processes, water quality, and water availability.Deep learning algorithms offer powerful tools to extract actionable insights from large and complex satellite datasets. By leveraging these technologies, researchers can enhance the accuracy of water resource assessments, improve flood forecasting models, optimize irrigation practices, and monitor changes in water quality in both natural environments and human-modified water systems.
This special issue invites contributions that showcase innovative applications, methodological advancements, and case studies demonstrating the impact of integrating satellite observations and deep learning in water resource management. Researchers are encouraged to explore innovative solutions to challenges such as sustainable water use, ecosystem conservation, and resilience to water-related hazards. Through interdisciplinary collaboration and technological innovation, this special issue aims to foster a deeper understanding of water systems dynamics and accelerate the adoption of data-driven strategies for achieving water security and sustainability globally. Submissions will contribute to advancing knowledge at the interface of remote sensing, artificial intelligence, and hydrology, paving the way for more effective policies and practices in managing our precious water resources.
Dr. Ying Zhang
Dr. Peng Dou
Prof. Dr. Chunlin Huang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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
- water system
- hydrological modeling
- satellite observations
- deep learning
- water resource
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