Special Issue "Advanced Modelling in Water Resources Using GIS and Remote Sensing Techniques"

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

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Dr. Richarde Marques Da Silva
E-Mail Website
Guest Editor
Department of Geosciences, Federal University of Paraíba/CCEN, 58051-900, João Pessoa, Paraiba State, Brazil
Tel. +55-83-3216-7750
Interests: land use and cover; 3D mapping; image classification; predicting; climate change; SWAT model; GIS applications; surface temperature
Dr. Celso Augusto Guimarães Santos
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Federal University of Paraíba, Center for Technology, 58051-900, João Pessoa, Paraiba State, Brazil
Tel. +55-83-3216-7684
Interests: droughts and water availability; hydrologic modeling; water resources management; trends; wavelet transform; TRMM products
Dr. Victor Hugo Coelho Rabelo
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Federal University of Paraíba, Center for Technology, 58051-900, João Pessoa, Paraiba State, Brazil
Tel. +55-83-3216-7684
Interests: evapotranspiration estimation; aquifer recharge; recharge estimation; SEBAL; MODIS; hydrodynamic characterization

Special Issue Information

Dear Colleagues,

Remote sensing data play an important role in the hydrological scientific community, mainly for overcoming and compensating for the limitations of observed data at regional and global scales. Currently, remotely-sensed data are being used in many applications related to water resources, such as rainfall, soil moisture, evapotranspiration, drought risk, water runoff-erosion modelling, groundwater, landslide, surface water inventory, and snowmelt runoff forecasts. The research in water resources using remotely-sensed data also has a great deal of relevance for studies related to climate change and global habitability. In this Special Issue, advanced techniques for estimation and modelling using GIS and remote sensing data in water resources will be presented.

Priority studies about novel techniques for quantifying and analyzing spatial distributions with the use of new products obtained by remote sensing or automated techniques, to improve the spatial knowledge of phenomena related to the hydrological cycle, are welcome. Combining geographical data from multiple spatial, spectral and thematic scales to quantify changes and their spatial patterns are also among our priorities. Issues related to spatial error distributions, as well as the detection of false changes through time, are of particular interest.

Papers showing novel and/or relevant techniques to study water resources management or some interesting applications in all subfields of the hydrological sciences will be considered. Well-prepared review papers are also welcomed.

Topics of interest may include, but are not limited to:

  • Droughts and Water availability
  • Evapotranspiration estimation and Hydrologic modeling
  • Land use predicting
  • Snow cover and glacial lands
  • Water resources management
  • Groundwater mapping
  • 3D mapping, Drone and high resolution images
  • Classifications and applications using Drone images

Dr. Richarde Marques da Silva
Dr. Celso Augusto Guimarães Santos
Dr. Victor Hugo Coelho Rabelo
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 papers will be 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 1800 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

  • land use and cover
  • 3D mapping
  • Drone
  • classification
  • forecasting
  • climate change
  • GIS
  • surface temperature
  • droughts
  • hydrologic modeling
  • evapotranspiration
  • groundwater

Published Papers (3 papers)

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Research

Open AccessArticle
Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration
Remote Sens. 2019, 11(15), 1782; https://doi.org/10.3390/rs11151782 - 30 Jul 2019
Abstract
Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and agricultural production. The main objective of this paper is to characterize the spatiotemporal dynamics of crop water use in the Central Valley of California [...] Read more.
Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and agricultural production. The main objective of this paper is to characterize the spatiotemporal dynamics of crop water use in the Central Valley of California using Landsat-based annual actual evapotranspiration (ETa) from 2008 to 2018 derived from the Operational Simplified Surface Energy Balance (SSEBop) model. Crop water use for 10 crops is characterized at multiple scales. The Mann–Kendall trend analysis revealed a significant increase in area cultivated with almonds and their water use, with an annual rate of change of 16,327 ha in area and 13,488 ha-m in water use. Conversely, alfalfa showed a significant decline with 12,429 ha in area and 13,901 ha-m in water use per year during the same period. A pixel-based Mann–Kendall trend analysis showed the changing crop type and water use at the level of individual fields for all of Kern County in the Central Valley. This study demonstrates the useful application of historical Landsat ET to produce relevant water management information. Similar studies can be conducted at regional and global scales to understand and quantify the relationships between land cover change and its impact on water use. Full article
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Open AccessArticle
Cluster Analysis Applied to Spatiotemporal Variability of Monthly Precipitation over Paraíba State Using Tropical Rainfall Measuring Mission (TRMM) Data
Remote Sens. 2019, 11(6), 637; https://doi.org/10.3390/rs11060637 - 15 Mar 2019
Cited by 1
Abstract
In Paraíba state, precipitation is strongly affected by several climate systems, such as trade winds, the intertropical convergence zone (ITCZ), easterly wave disturbances (EWDs), and the South Atlantic subtropical high. Accordingly, the objective of this study was to analyze the spatiotemporal variability in [...] Read more.
In Paraíba state, precipitation is strongly affected by several climate systems, such as trade winds, the intertropical convergence zone (ITCZ), easterly wave disturbances (EWDs), and the South Atlantic subtropical high. Accordingly, the objective of this study was to analyze the spatiotemporal variability in precipitation to identify homogeneous trends of that variable and the effects of climate systems in Paraíba state by cluster analysis. The precipitation data used in this study derive from the Tropical Rainfall Measuring Mission (TRMM) satellite for the period from January 1998 to December 2015, and hierarchical clustering was used to classify the sites into different groups with similar trends. The findings show an uneven spatiotemporal precipitation distribution in all mesoregions of the state and considerable monthly precipitation variation in space. The estimated precipitation depth was highest in coastal regions and in high-altitude areas due to orographic precipitation. In general, the precipitation over Paraíba is characterized by strong gradients in the coastal zone towards the continent (Agreste, Borborema, and Sertão mesoregions) and from north to south due to the physiography of the region and the effects of climate systems with different time scales. Finally, the proposed clustering method using TRMM data was effective in characterizing climatic systems. Full article
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Open AccessArticle
Fusion Feature Multi-Scale Pooling for Water Body Extraction from Optical Panchromatic Images
Remote Sens. 2019, 11(3), 245; https://doi.org/10.3390/rs11030245 - 24 Jan 2019
Abstract
Water body extraction is a hot research topic in remote sensing applications. Using panchromatic optical remote sensing images to extract water bodies is a challenging task, because these images have one level of gray information, variable imaging conditions, and complex scene information. Refined [...] Read more.
Water body extraction is a hot research topic in remote sensing applications. Using panchromatic optical remote sensing images to extract water bodies is a challenging task, because these images have one level of gray information, variable imaging conditions, and complex scene information. Refined water body extraction from optical panchromatic images often experiences serious under- or over- segmentation problems. In this paper, for producing refined water body extraction results from optical panchromatic images, we propose a fusion feature multi-scale pooling for Markov modeling method. Markov modeling includes two aspects: label field initialization and feature field establishment. These two aspects are jointly created by the fusion feature multi-scale pooling process, and this process is proposed to enhance the feature difference between water bodies and land cover. Then, the greedy algorithm in the iteration conditional method is used to extract refined water bodies according to the rebuilt Markov initial label and feature fields. Finally, to prove the effectiveness of proposed method, extensive experiments were used with collected 2.5m SPOT 5 and 1m GF-2 optical panchromatic images and evaluation indexes (precision, recall, overall accuracy, kappa coefficient and boundary detection ratios) to demonstrate that our proposed method can produce more refined water body extraction results than the state-of-the-art methods. The global and local refined indexes are improved by about 7% and 10%, respectively. Full article
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