Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
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: closed (30 July 2021) | Viewed by 37908
Special Issue Editors
Interests: remote sensing; hydrology; climate change; evapotranspiration; runoff; predictions; catchment
Special Issues, Collections and Topics in MDPI journals
Interests: microwave remote sensing of soil moisture; hydrological applications of remote sensing; hydrological data assimilation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are living in a world where geophysical datasets, particularly, remote sensing datasets, are created at fast increasing rates. The efficient and innovative use of these datasets for understanding hydrological processes in various climatic and vegetation regimes under anthropogenic influence has become an important challenge, which offers a wide range of research opportunities. This is particularly urgent for the hydrological community at large who has relied on distributed and lumped hydrological models for hydrological simulations/predictions over the last several decades. Increasingly accurate water information requires more insightful understanding and more skilful predictions, at resolutions commensurate with the available data, which is challenging the more traditional hydrological modelling. This Special Issue provides a great opportunity to stimulate investigations on how a better and smarter use of high-to-moderate-resolution remote sensing datasets can improve hydrological simulations and predictions.
We invite researchers to contribute their original research articles that use various remote sensing techniques to improve hydrological simulations and predictions and that will stimulate our collective efforts. We invite papers covering the following, but not exclusive, topics:
(1) Detecting hydrological and other related changes using state-of-the-art remote sensing techniques;
(2) Mapping eco-hydrological and hydrological processes and their driving factors using large samples and high-resolution datasets;
(3) Understanding hydrological processes under a rapidly changing world by using hydrological modelling together with high-to-moderate-resolution (several meters to hundred meters) remote sensing data;
(4) Improving hydrological prediction skills by modifying hydrological model structures to incorporate remote sensing data and by using various model calibrations against remote sensing data;
(5) Developing hydrological modelling frameworks using advanced cloud cluster computation techniques and tools, such as the Google Earth Engine;
(6) Using remote sensing data together with machine learning techniques to improve predictions of various hydrological variables and hydrological signatures;
(7) Using remote sensing techniques for water-related studies, such as on the water–food–energy security nexus.
Prof. Yongqiang Zhang
Dr. Dongryeol Ryu
Dr. Donghai Zheng
Guest Editors
Manuscript Submission Information
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Keywords
- remote sensing
- high resolution
- model
- GEE
- machine learning
- evapotranspiration
- runoff
- soil moisture
- hydrological signatures
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