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Advanced Modelling in Water Resources Using GIS and Remote Sensing...
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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: closed (31 December 2021) | Viewed by 22313

Special Issue Editors

Dr. Richarde Marques Da Silva

E-Mail Website
Guest Editor
Department of Geosciences, Federal University of Paraíba/CCEN, João Pessoa 58051-900, PB, Brazil
Interests: land use and cover; 3D mapping; image classification; predicting; climate change; SWAT model; GIS applications; surface temperature
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Celso Augusto Guimarães Santos

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Federal University of Paraíba, João Pessoa 58051-900, Brazil
Interests: applied hydrology; drought; flood risk; environmental management
Special Issues, Collections and Topics in MDPI journals
Dr. Victor Hugo Coelho Rabelo

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Federal University of Paraíba, Center for Technology, João Pessoa, Paraiba State 58051-900, Brazil
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 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

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

Published Papers (5 papers)

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Research

20 pages, 12958 KiB  
Article
Remote-Sensing-Based Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models
by Mohammed M. Alquraish and Mosaad Khadr
Remote Sens. 2021, 13(20), 4147; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204147 - 16 Oct 2021
Cited by 15 | Viewed by 2560
Abstract
In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution [...] Read more.
In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution data derived from these products. The hydrological performance of the developed forecasting models was then evaluated using several statistical indices, including NSE, MAE, RMSE, and R2. The performance measures confirmed that the CHIRPS product has superior performance compared to RFE 2.0 and TRMM data, and it could provide reliable rainfall estimates for use as input in forecasting models. Likewise, the results of the forecasting models confirmed that the ANN and SVM both achieved acceptable levels of accuracy for forecasting streamflow; however, the ANN model was superior (R2 = 0.898–0.735) to the SVM (R2 = 0.742–0.635) in both the training and testing periods. Full article
(This article belongs to the Special Issue Advanced Modelling in Water Resources Using GIS and Remote Sensing Techniques)
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26 pages, 4901 KiB  
Article
Evaluation of the TRMM Product for Monitoring Drought over Paraíba State, Northeastern Brazil: A Statistical Analysis
by Reginaldo Moura Brasil Neto, Celso Augusto Guimarães Santos, Thiago Victor Medeiros do Nascimento, Richarde Marques da Silva and Carlos Antonio Costa dos Santos
Remote Sens. 2020, 12(14), 2184; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142184 - 08 Jul 2020
Cited by 21 | Viewed by 2804
Abstract
Drought is a natural phenomenon that originates from the absence of precipitation over a certain period and is capable of causing damage to societal development. With the advent of orbital remote sensing, rainfall estimates from satellites have appeared as viable alternatives to monitor [...] Read more.
Drought is a natural phenomenon that originates from the absence of precipitation over a certain period and is capable of causing damage to societal development. With the advent of orbital remote sensing, rainfall estimates from satellites have appeared as viable alternatives to monitor natural hazards in ungauged basins and complex areas of the world; however, the accuracies of these orbital products still need to be verified. Thus, this work aims to evaluate the performance of Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates in monitoring the spatiotemporal behavior of droughts at multiple temporal scales over Paraíba State based on the standardized precipitation index (SPI) over 20 years (1998–2017). For this purpose, rainfall data from 78 rain gauges and 187 equally spaced TRMM cell grids throughout the region are used, and accuracy analyses are performed at the single-gauge level and in four mesoregions at eight different time scales based on 11 statistical metrics calculations divided into three different categories. The results show that in the mesoregions close to the coast, the satellite-based product is less accurate in capturing the drought behavior regardless of the evaluated statistical metrics. At the temporal scale, the TRMM is more accurate in identifying the pattern of medium-term droughts; however, there is considerable spatial variation in the accuracy of the product depending on the performance index. Therefore, it is concluded that rainfall estimates from the TRMM satellite are a valuable source of data to identify drought behavior in a large part of Paraíba State at different time scales, and further multidisciplinary studies should be conducted to monitor these phenomena more accurately based on satellite data. Full article
(This article belongs to the Special Issue Advanced Modelling in Water Resources Using GIS and Remote Sensing Techniques)
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22 pages, 4096 KiB  
Article
Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration
by Matthew Schauer and Gabriel B. Senay
Remote Sens. 2019, 11(15), 1782; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11151782 - 30 Jul 2019
Cited by 27 | Viewed by 6952
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
(This article belongs to the Special Issue Advanced Modelling in Water Resources Using GIS and Remote Sensing Techniques)
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18 pages, 4783 KiB  
Article
Cluster Analysis Applied to Spatiotemporal Variability of Monthly Precipitation over Paraíba State Using Tropical Rainfall Measuring Mission (TRMM) Data
by Celso Augusto Guimarães Santos, Reginaldo Moura Brasil Neto, Richarde Marques da Silva and Samir Gonçalves Fernandes Costa
Remote Sens. 2019, 11(6), 637; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11060637 - 15 Mar 2019
Cited by 50 | Viewed by 5479
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
(This article belongs to the Special Issue Advanced Modelling in Water Resources Using GIS and Remote Sensing Techniques)
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21 pages, 9370 KiB  
Article
Fusion Feature Multi-Scale Pooling for Water Body Extraction from Optical Panchromatic Images
by Baogui Qi, Yin Zhuang, He Chen, Shan Dong and Lianlin Li
Remote Sens. 2019, 11(3), 245; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030245 - 24 Jan 2019
Cited by 15 | Viewed by 3318
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
(This article belongs to the Special Issue Advanced Modelling in Water Resources Using GIS and Remote Sensing Techniques)
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