Research Topic

Remote Sensing of the Water Cycle and its Added Value for Hydrological Modeling

About this Research Topic

Quantifying the temporal and spatial variability of hydrological processes is vital for effective water management, and remote sensing techniques provide a new avenue for such applications. Significant progress has been made in observing/retrieving hydrological variables using remote sensing techniques - including precipitation, evapotranspiration, terrestrial water storage, soil moisture, and snow cover. Recent studies demonstrate that these products are of paramount value for constraining our modeling uncertainties, and advances in remote sensing techniques are directly linked to improved modeling skills, particularly over data-sparse regions.

There are several remaining challenges in optimally implementing remote sensing data for hydrological analysis, for example, the quantification of remote sensing errors, meeting the accuracy requirement of hydrological application, and robustly inferring variable of interest that is not directly retrievable by remote sensing. The above-mentioned challenges can be potentially addressed via new remote sensing techniques/products and/or advanced statistical tools, which may benefit regional and global scale hydrological modeling and water management.

Therefore, this Research Topic aims to initiate a forum to present and discuss the recent and near-future advances and challenges of remote sensing techniques, with a focus on their applications on hydrological and/or land surface analyses. Specifically, this Research Topic will include, but is not limited to:
• Review of hydrological remote sensing techniques.
• Ground-based and/or statistical (e.g. triple collocation and instrumental variable techniques)
validation of remote sensing data.
• Machine-learning techniques for producing new/improved remote sensing products.
• Multi-source data fusion algorithms to develop improved global/regional hydrological products.
• Hydrological and/or land data assimilation of remote sensing data.


Keywords: remote sensing, machine learning, data assimilation, hydrological modeling, land-surface modeling, calibraiton, validation


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Quantifying the temporal and spatial variability of hydrological processes is vital for effective water management, and remote sensing techniques provide a new avenue for such applications. Significant progress has been made in observing/retrieving hydrological variables using remote sensing techniques - including precipitation, evapotranspiration, terrestrial water storage, soil moisture, and snow cover. Recent studies demonstrate that these products are of paramount value for constraining our modeling uncertainties, and advances in remote sensing techniques are directly linked to improved modeling skills, particularly over data-sparse regions.

There are several remaining challenges in optimally implementing remote sensing data for hydrological analysis, for example, the quantification of remote sensing errors, meeting the accuracy requirement of hydrological application, and robustly inferring variable of interest that is not directly retrievable by remote sensing. The above-mentioned challenges can be potentially addressed via new remote sensing techniques/products and/or advanced statistical tools, which may benefit regional and global scale hydrological modeling and water management.

Therefore, this Research Topic aims to initiate a forum to present and discuss the recent and near-future advances and challenges of remote sensing techniques, with a focus on their applications on hydrological and/or land surface analyses. Specifically, this Research Topic will include, but is not limited to:
• Review of hydrological remote sensing techniques.
• Ground-based and/or statistical (e.g. triple collocation and instrumental variable techniques)
validation of remote sensing data.
• Machine-learning techniques for producing new/improved remote sensing products.
• Multi-source data fusion algorithms to develop improved global/regional hydrological products.
• Hydrological and/or land data assimilation of remote sensing data.


Keywords: remote sensing, machine learning, data assimilation, hydrological modeling, land-surface modeling, calibraiton, validation


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

19 April 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

19 April 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..