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Remote Sensing
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Remote Sensing of Groundwater Variations and Ground Response
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Remote Sensing of Groundwater Variations and Ground Response

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 6539

Special Issue Editors

Prof. Huili Gong

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Guest Editor
1. Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, Ministry of Education, Beijing, China
2. Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
Interests: hydrogeology; InSAR; land subsidence
Prof. Dr. Shahbaz Khan

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Guest Editor
Director of UNESCO Beijing and Rep to China, DPRK, Japan, Mongolia and Republic of Korea
Interests: arid ecosystems; water resources; environmental issues; climate change; soil and water management
Special Issues, Collections and Topics in MDPI journals
Dr. Yun Chen

E-Mail Website
Guest Editor
CSIRO Land and Water, Canberra, ACT 2601, Australia
Interests: remote sensing; GIS; risk/disaster evaluation; vulnerability/suitability assessment; multicriteria decision making; big data and geospatial modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Monica Rivas Casado

E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: surface water flooding; standardised monitoring approaches; systems engineering; disruptive technologies; climate change; extreme events
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Groundwater is one of the most important resources in the world. In regions where surface water resources are limited or inaccessible, groundwater is the major type of water resource that supplies the hydrologic needs of people, including domestic water use, irrigation, plantation, and industrial water use. During the past decades, many regions in the world have suffered from groundwater depletion due to excessive pumping. Declination of the groundwater level has caused severe hydrogeological disasters, including land subsidence and ground failures such as surface faulting and earth crack. It has also led to wetland shrinking, vegetation degradataion, and soil salinization because of reduced inflow to surface water systems and ecosystems. Therefore, evaluating and monitoring groundwater variation and its impacts on the environment have become urgent. In recent years, remote sensing has provided viable tools for monitoring groundwater storage changes, ground deformations, and wetland ecosystems. This enables us to better understand how groundwater variations affect geological and ecological environments.

This Issue focuses on but is not limited to publishing papers in the following fields:

  1. Monitoring groundwater storage using GRACE;
  2. Monitoring and modelling land subsidence or ground failures due to groundwater depletion using remote sensing techniques;
  3. Monitoring and modelling groundwater–surface water interactions by integrating remote sensing observations;
  4. Monitoring and modelling change in wetland and riparian ecosystem and its relationship with groundwater variations;
  5. Impact of groundwater variation on hydrological processes, such as evapotranspiration observed by remote sensing techniques.

Prof. Huili Gong
Prof. Shahbaz Khan
Prof. Yun Chen
Dr. Monica Rivas Casado
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

  • groundwater storage
  • groundwater variation
  • GRACE
  • land subsidence
  • ground failure
  • wetland
  • riparian ecosystem
  • groundwater–surface water interactions

Published Papers (2 papers)

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23 pages, 48527 KiB  
Article
Detection of Seasonal Deformation of Highway Overpasses Using the PS-InSAR Technique: A Case Study in Beijing Urban Area
by Mingyuan Lyu, Yinghai Ke, Xiaojuan Li, Lin Zhu, Lin Guo and Huili Gong
Remote Sens. 2020, 12(18), 3071; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183071 - 19 Sep 2020
Cited by 19 | Viewed by 3126
Abstract
In urban areas, deformation of transportation infrastructures may lead to serious safety accidents. Timely and accurate monitoring of the structural deformation is critical for prevention of transportation accidents and assurance of construction quality, particularly in areas with regional land subsidence, such as the [...] Read more.
In urban areas, deformation of transportation infrastructures may lead to serious safety accidents. Timely and accurate monitoring of the structural deformation is critical for prevention of transportation accidents and assurance of construction quality, particularly in areas with regional land subsidence, such as the city of Beijing. In this study, we proposed a method for the detection of seasonal deformation of highway overpasses using the integration of persistent scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) techniques and seasonal indices, i.e., deformation concentration degree (DCD) and deformation concentration period (DCP) indices. Taking eastern Beijing urban area as a case study area, we first used the PS-InSAR technique to derive time series surface deformation based on 55 TerraSAR-X images during 2010–2016. Then, we proposed DCD and DCP indices to characterize seasonal deformation of 25 highway overpasses in the study area, with DCD representing to what degree the annual deformation is distributed in a year, and DCP representing the period on which deformation concentrates in the year. Our results showed that the maximum annual deformation rate reached −141.3 mm/year in Beijing urban area, and the PS-InSAR measurements agreed well with levelling measurements (R2 > 0.97). For PS pixels with DCD ≥ 0.3, the monthly deformation showed obvious seasonal patterns with deformation values during some months greater than those during the other months. DCP revealed that the settlement during autumn and winter was more serious than that in spring and summer. The seasonal patterns seemed to be related to the location, structure, and construction age of the overpasses. The upper-level overpasses, the newly constructed overpasses, and those located in the subsidence area (rate < −40 mm/year) tended to show a greater seasonal pattern. The seasonal deformation variations were also affected by groundwater-level fluctuation, temperature, and compressible layer. Full article
(This article belongs to the Special Issue Remote Sensing of Groundwater Variations and Ground Response)
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23 pages, 4961 KiB  
Article
Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine
by Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames and E. James Nelson
Remote Sens. 2020, 12(12), 2044; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122044 - 25 Jun 2020
Cited by 15 | Viewed by 2792
Abstract
Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to [...] Read more.
Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture models, the Global Land Data Assimilation System (GLDAS) model and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) soil moisture model to impute the missing data. Our imputation method uses a machine learning technique called Extreme Learning Machine (ELM). Our implementation uses 11 input data-streams, all based on Earth observation data. We train and apply the model one well at a time. We selected ELM because it is a single hidden layer feedforward model that can be trained quickly on minimal data. We tested the ELM method using data from monitoring wells in the Cedar Valley and Beryl-Enterprise areas in southwest Utah, USA. We compute error estimates for the imputed data and show that ELM-computed estimates were more accurate than Kriging estimates. This ELM-based data imputation method can be used to impute missing data at wells. These complete time series can be used improve the accuracy of aquifer groundwater elevation maps in areas where in-situ well measurements are sparse, resulting in more accurate spatial estimates of the groundwater surface. The data we use are available globally from 1950 to the present, so this method can be used anywhere in the world. Full article
(This article belongs to the Special Issue Remote Sensing of Groundwater Variations and Ground Response)
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