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Remote Sensing
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Remote Sensing for Sustainable Water Resources Management
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Remote Sensing for Sustainable Water Resources Management

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 3764

Special Issue Editors

Dr. Naga Manohar Velpuri

E-Mail Website
Guest Editor
ASRC Federal Data Solutions, Contractor to U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA
Interests: multi-scale watershed hydrologic processes; variability in surface water storage using multi-source satellite data; climate and human impacts on water resources availability, and water availability and use analysis across scales
Special Issues, Collections and Topics in MDPI journals
Dr. Md Shahriar Pervez

E-Mail
Guest Editor
ASRC Federal Data Solutions, Contractor to U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA
Interests: remote sensing; land surface hydrology; water availability; climate change; food security; early warning systems
Special Issues, Collections and Topics in MDPI journals
Dr. Lisa Maria Rebelo

E-Mail Website
Guest Editor
International Water Management Institute, 127 Sunil Mawatha,Pelawatte, Battaramulla, Colombo, Sri Lanka
Interests: remote sensing for basin scale hydrology, water availability and allocation; management of water resources at multiple scales; basin scale water accounting; wetland inventory, monitoring and assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Noel Aloysius

E-Mail Website
Guest Editor
Department of Chemical and Biomedical Engineering, School of Natural Resources University of Missouri, Columbia, MO 65211, USA
Interests: terrestrial hydrology; remote sensing; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing data combined with land surface hydrology offers a reliable source of observations for a range of water cycle components. Satellite data provide consistent, timely, and repeated observations of hydrological variables at multiple scales, especially where in situ data are either scarce or not available. Tremendous progress has been made on the use of remote sensing and hydrological modeling over the last several decades. Satellites such as GPM, SMAP, Terra, Aqua, Landsat, Sentinel, and GRACE-FO, as well as other satellites and airborne platforms are now capable of observing hydrologic variables—such as precipitation, evapotranspiration, streamflow, snow and ice, soil moisture, terrestrial water storage variations—for water balance studies. While recent advances in satellite observing capabilities have improved the accuracy of hydrological observations, there are several challenges to be addressed in the use of the data for the operational and sustainable management of water resources.

This Special Issue welcomes original and innovative manuscripts that focus on the ongoing and future priorities for hydrologic research that address the challenges of using remote sensing data for sustainable water resource management. The scope of this issue will include but not be limited to manuscripts that cover topics such as (1) methodological advances in the use of remote sensing for observing hydrological variables, (2) data assimilation, (3) applications of remote sensing data for a range of hydrological studies at multiple spatiotemporal scales, (4) accuracy evaluation and uncertainty analysis of satellite data, and (5) use of remote sensing data to accelerate the performance of best management practices in hydrology. Review articles are also welcome.

Dr. Naga Manohar Velpuri
Dr. Md Shahriar Pervez
Dr. Lisa Maria Rebelo
Dr. Noel Aloysius
Guest Editor

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

  • Remote Sensing Hydrology
  • Global Water Cycle
  • Climate Impact on Hydrology
  • Water Quantity and Quality
  • Water Availability and Accounting
  • Sustainable Water Management

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Published Papers (1 paper)

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Research

22 pages, 4302 KiB  
Article
Upstream GPS Vertical Displacement and its Standardization for Mekong River Basin Surface Runoff Reconstruction and Estimation
by Hok Sum Fok, Linghao Zhou, Yongxin Liu, Zhongtian Ma and Yutong Chen
Remote Sens. 2020, 12(1), 18; https://doi.org/10.3390/rs12010018 - 18 Dec 2019
Cited by 4 | Viewed by 3066
Abstract
Surface runoff (R), which is another expression for river water discharge of a river basin, is a critical measurement for regional water cycles. Over the past two decades, river water discharge has been widely investigated, which is based on remotely sensed [...] Read more.
Surface runoff (R), which is another expression for river water discharge of a river basin, is a critical measurement for regional water cycles. Over the past two decades, river water discharge has been widely investigated, which is based on remotely sensed hydraulic and hydrological variables as well as indices. This study aims to demonstrate the potential of upstream global positioning system (GPS) vertical displacement (VD) and its standardization to statistically derive R time series, which has not been reported in recent literature. The correlation between the in situ R at estuaries and averaged GPS-VD and its standardization in the river basin upstream on a monthly temporal scale of the Mekong River Basin (MRB) is examined. It was found that the reconstructed R time series from the latter agrees with and yields a similar performance to that from the terrestrial water storage based on gravimetric satellite (i.e., Gravity Recovery and Climate Experiment (GRACE)) and traditional remote sensing data. The reconstructed R time series from the standardized GPS-VD was found to have a 2–7% accuracy increase against those without standardization. On the other hand, it is comparable to data that are obtained by the Palmer drought severity index (PDSI). Similar accuracies are exhibited by the estimated R when externally validated through another station location with in situ time series. The comparison of the estimated R at the entrance of river delta against that at the estuaries indicates a 1–3% relative error induced by the residual ocean tidal effect at the estuary. The reconstructed R from the standardized GPS-VD yields the lowest total relative error of less than 9% when accounting for the main upstream area of the MRB. The remaining errors may be the result of the combined effect of the proposed methodology, remaining environmental signals in the data time series, and potential time lag (less than a month) between the upstream MRB and estuary. Full article
(This article belongs to the Special Issue Remote Sensing for Sustainable Water Resources Management)
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