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Earth Observation Applications: Towards a Better Understanding of Variability in the Water Cycle Behavior and Water Resources

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (10 March 2022) | Viewed by 11898

Special Issue Editors

Dr. Amen Al-Yaari

E-Mail Website
Guest Editor
Faculty of Science and Engineering, Department of Environment, Transfers and Interactions in Soils and Water Bodies, Sorbonne University, Paris, France
Interests: hydrology; soil moisture; climate; microwave remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoyong Xu

E-Mail Website
Guest Editor
Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
Interests: watershed hydrology; remote sensing of water resources; hydrologic data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water is essential for all life on Earth. Water security and sustainable water resources management are some of the most urgent challenges that the world faces today given that the water cycle behavior and spatial and temporal patterns of water resources are being increasingly impacted by climate change. Recent advances in data collection from satellite-based remote sensing (Earth observation) have opened new opportunities to better understand water cycle behavior and how it is changing in response to climate change.

This Special Issue focuses on Earth observation applications for improving our understanding of variability in water cycle behavior and water resources, a necessary step toward evidence-based sustainable water resources management and water security. We welcome submissions that are related (but not limited) to the following topics:

  • Development of retrieval algorithms for various types of satellite hydrologic products (precipitation, soil moisture, snow and ice, terrestrial water storage, evapotranspiration, streamflow, lake or river water levels, etc.);
  • Validation of satellite hydrologic products using ground measurements;
  • Monitoring of hydroclimatic extreme events (e.g., floods and droughts) from Earth observation;
  • Satellite detection of variability in regional or global surface water and groundwater resources as influenced by climate change and/or human activities;
  • Application of satellite hydrologic products in computational models (e.g., data assimilation, model calibration);
  • An integrated use of Earth observation, ground measurements, and computational modeling for advancing the understanding of the physical processes that govern water movement in the surface/subsurface domains of the Earth system.

Dr. Amen Al-Yaari
Dr. Xiaoyong Xu
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. Sensors 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 2600 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

  • earth observation
  • satellite hydrologic products
  • water cycle
  • water resources sustainability
  • water security
  • climate change
  • data assimilation

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Published Papers (3 papers)

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Research

17 pages, 3520 KiB  
Article
Representation of Spatial Variability of the Water Fluxes over the Congo Basin Region
by Marc De Benedetti, G. W. K. Moore and Xiaoyong Xu
Sensors 2022, 22(1), 84; https://doi.org/10.3390/s22010084 - 23 Dec 2021
Viewed by 2346
Abstract
The Congo Basin, being one of the major basins in the tropics, is important to the global climate, yet its hydrology is perhaps the least understood. Although various reanalysis/analysis datasets have been used to improve our understanding of the basin’s hydroclimate, they have [...] Read more.
The Congo Basin, being one of the major basins in the tropics, is important to the global climate, yet its hydrology is perhaps the least understood. Although various reanalysis/analysis datasets have been used to improve our understanding of the basin’s hydroclimate, they have been historically difficult to validate due to sparse in situ measurements. This study analyzes the impact of model resolution on the spatial variability of the Basin’s hydroclimate using the Decorrelation Length Scale (DLCS) technique, as it is not subject to uniform model bias. The spatial variability within the precipitation (P), evaporation/evapotranspiration (E), and precipitation-minus-evaporation (P-E) fields were investigated across four spatial resolutions using reanalysis/analysis datasets from the ECMWF ranging from 9–75 km. Results show that the representation of P and P-E fields over the Basin and the equatorial Atlantic Ocean are sensitive to model resolution, as the spatial patterns of their DCLS results are resolution-dependent. However, the resolution-independent features are predominantly found in the E field. Furthermore, the P field is the dominant source of spatial variability of P-E, occurring over the land and the equatorial Atlantic Ocean, while over the Southern Atlantic, P-E is mainly governed by the E field, with both showing weak spatial variability. Full article
(This article belongs to the Special Issue Earth Observation Applications: Towards a Better Understanding of Variability in the Water Cycle Behavior and Water Resources)
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25 pages, 5854 KiB  
Article
Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region
by Mohd Anul Haq, Prashant Baral, Shivaprakash Yaragal and Biswajeet Pradhan
Sensors 2021, 21(21), 7416; https://doi.org/10.3390/s21217416 - 8 Nov 2021
Cited by 28 | Viewed by 3210
Abstract
Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity [...] Read more.
Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region. Full article
(This article belongs to the Special Issue Earth Observation Applications: Towards a Better Understanding of Variability in the Water Cycle Behavior and Water Resources)
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22 pages, 7979 KiB  
Article
Drought Vulnerability Assessment Using Geospatial Techniques in Southern Queensland, Australia
by Muhammad Hoque, Biswajeet Pradhan, Naser Ahmed and Abdullah Alamri
Sensors 2021, 21(20), 6896; https://doi.org/10.3390/s21206896 - 18 Oct 2021
Cited by 18 | Viewed by 5498
Abstract
In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop [...] Read more.
In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies. Full article
(This article belongs to the Special Issue Earth Observation Applications: Towards a Better Understanding of Variability in the Water Cycle Behavior and Water Resources)
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