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Remote Sensing
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Satellite Data Assimilation for Groundwater Analysis
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Satellite Data Assimilation for Groundwater Analysis

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 (30 November 2023) | Viewed by 11329

Special Issue Editors

Dr. Norman L. Jones

E-Mail Website
Guest Editor
Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: groundwater; sustainability; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Gustavious Paul Williams

E-Mail Website
Guest Editor
Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: remote sensing; water quality; statistical analysis; aquatic chemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Groundwater is increasingly in demand for municipal, agricultural, and industrial uses. Climate change has resulted in severe droughts, which further stress groundwater resources. Water managers are increasingly under pressure to manage aquifers in a sustainable fashion, but doing so requires a thorough understanding of aquifer dynamics, water balances, and how groundwater levels and storage are changing over time. Groundwater monitoring can be time-consuming and expensive and is especially difficult in developing countries where well data are both sparse and unreliable. Recent technological advances in remote sensing, data analytics, machine learning, etc. have made it possible to gain new insights into aquifer health, groundwater usage trends, recharge, and evapotranspiration using Earth observations. In particular, remote data products such as the Gravity Recovery and Climate Experiment (GRACE), Moderate Resolution Imaging Spectroradiometer (MODIS), Shuttle Radar Topography Mission (SRTM), and Enhanced Thematic Mapper (ETM) have all been used to directly or indirectly characterize groundwater. In this Special Issue, we invite papers that share state-of-the-art techniques for leveraging these and other remote sensing technologies for groundwater analysis, leading to more holistic and sustainable management of this precious natural resource.

Dr. Norman L. Jones
Dr. Gustavious Paul Williams
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
  • sustainability
  • aquifers
  • recharge
  • subsidence
  • evapotranspiration
  • data analytics
  • machine learning

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Published Papers (4 papers)

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Research

21 pages, 10639 KiB  
Article
Exploiting Earth Observations to Enable Groundwater Modeling in the Data-Sparse Region of Goulbi Maradi, Niger
by Sergio A. Barbosa, Norman L. Jones, Gustavious P. Williams, Bako Mamane, Jamila Begou, E. James Nelson and Daniel P. Ames
Remote Sens. 2023, 15(21), 5199; https://doi.org/10.3390/rs15215199 - 1 Nov 2023
Cited by 2 | Viewed by 1471
Abstract
Groundwater modeling is a useful tool for assessing sustainability in water resources planning. However, groundwater models are difficult to construct in regions with limited data availability, areas where planning is most crucial. We illustrate how remote sensing data can be used with limited [...] Read more.
Groundwater modeling is a useful tool for assessing sustainability in water resources planning. However, groundwater models are difficult to construct in regions with limited data availability, areas where planning is most crucial. We illustrate how remote sensing data can be used with limited in situ data to build and calibrate a regional groundwater model in the Goulbi Maradi alluvial aquifer in southern Niger in Western Africa. We used data from the NASA Gravity Recovery and Climate Experiment (GRACE) satellite mission to estimate recharge rates, the primary source of water to the aquifer. We used the groundwater storage changes obtained from GRACE data from 2009 to 2021 to establish an overall water budget. We used this water budget to back-calculate groundwater withdrawals from pumping in the region. There are only very limited historic data on withdrawals. This approach allowed us to calibrate the model and use it as a predictive tool to analyze the impact of various assumptions about future recharge and groundwater extraction patterns associated with the development of groundwater infrastructure in the region. The results indicate that water extraction from the Goulbi Maradi alluvial aquifer is sustainable, even if current groundwater extraction is increased by up to 28%. Full article
(This article belongs to the Special Issue Satellite Data Assimilation for Groundwater Analysis)
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Graphical abstract

18 pages, 5347 KiB  
Article
Lessons for Sustainable Urban Development: Interplay of Construction, Groundwater Withdrawal, and Land Subsidence at Battersea, London
by Vivek Agarwal, Amit Kumar, Zhengyuan Qin, Rachel L. Gomes and Stuart Marsh
Remote Sens. 2023, 15(15), 3798; https://doi.org/10.3390/rs15153798 - 30 Jul 2023
Cited by 9 | Viewed by 2950
Abstract
The capacity of aquifers to store water and the stability of infrastructure can each be adversely influenced by variations in groundwater levels and subsequent land subsidence. Along the south bank of the River Thames, the Battersea neighbourhood of London is renovating a vast [...] Read more.
The capacity of aquifers to store water and the stability of infrastructure can each be adversely influenced by variations in groundwater levels and subsequent land subsidence. Along the south bank of the River Thames, the Battersea neighbourhood of London is renovating a vast 42-acre (over 8 million sq ft) former industrial brownfield site to become host to a community of homes, shops, bars, restaurants, cafes, offices, and over 19 acres of public space. For this renovation, between 2016 and 2020, a significant number of bearing piles and secant wall piles, with diameters ranging from 450 mm to 2000 mm and depths of up to 60 m, were erected inside the Battersea Power Station. Additionally, there was considerable groundwater removal that caused the water level to drop by 2.55 ± 0.4 m/year between 2016 and 2020, as shown by Environment Agency data. The study reported here used Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology to analyse the associated land movement for Battersea, London, during this period. The average land subsidence was found to occur at the rate of −6.8 ± 1.6 mm/year, which was attributed to large groundwater withdrawals and underground pile construction for the renovation work. Thus, this study underscores the critical interdependence between civil engineering construction, groundwater management, and land subsidence. It emphasises the need for holistic planning and sustainable development practices to mitigate the adverse effects of construction on groundwater resources and land stability. By considering the Sustainable Development Goals (SDGs) outlined by the United Nations, particularly Goal 11 (Sustainable Cities and Communities) and Goal 6 (Clean Water and Sanitation), city planners and stakeholders can proactively address these interrelated challenges. Full article
(This article belongs to the Special Issue Satellite Data Assimilation for Groundwater Analysis)
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18 pages, 7771 KiB  
Article
GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE
by Sarva T. Pulla, Hakan Yasarer and Lance D. Yarbrough
Remote Sens. 2023, 15(9), 2247; https://doi.org/10.3390/rs15092247 - 24 Apr 2023
Cited by 7 | Viewed by 3501
Abstract
Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially [...] Read more.
Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially in countries with limited infrastructure and resources. However, long-term data collection is required to characterize and evaluate trends. To address these challenges, we propose a framework that uses data from the Gravity Recovery and Climate Experiment (GRACE) mission and downscaling models to generate higher-resolution (1 km) groundwater predictions. The framework is designed to be flexible, allowing users to implement any machine learning model of interest. We selected four models: deep learning model, gradient tree boosting, multi-layer perceptron, and k-nearest neighbors regressor. To evaluate the effectiveness of the framework, we offer a case study of Sunflower County, Mississippi, using well data to validate the predictions. Overall, this paper provides a valuable contribution to the field of groundwater resource management by demonstrating a framework using remote sensing data and machine learning techniques to improve monitoring and management of this critical resource, especially to those who seek a faster way to begin to use these datasets and applications. Full article
(This article belongs to the Special Issue Satellite Data Assimilation for Groundwater Analysis)
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23 pages, 5706 KiB  
Article
Groundwater Level Data Imputation Using Machine Learning and Remote Earth Observations Using Inductive Bias
by Saul G. Ramirez, Gustavious Paul Williams and Norman L. Jones
Remote Sens. 2022, 14(21), 5509; https://doi.org/10.3390/rs14215509 - 1 Nov 2022
Cited by 4 | Viewed by 2593
Abstract
Sustainable groundwater management requires an accurate characterization of aquifer-storage change over time. This process begins with an analysis of historical water levels at observation wells. However, water-level records can be sparse, particularly in developing areas. To address this problem, we developed an imputation [...] Read more.
Sustainable groundwater management requires an accurate characterization of aquifer-storage change over time. This process begins with an analysis of historical water levels at observation wells. However, water-level records can be sparse, particularly in developing areas. To address this problem, we developed an imputation method to approximate missing monthly averaged groundwater-level observations at individual wells since 1948. To impute missing groundwater levels at individual wells, we used two global data sources: Palmer Drought Severity Index (PDSI), and the Global Land Data Assimilation System (GLDAS) for regression. In addition to the meteorological datasets, we engineered four additional features and encoded the temporal data as 13 parameters that represent the month and year of an observation. This extends previous similar work by using inductive bias to inform our models on groundwater trends and structure from existing groundwater observations, using prior estimates of groundwater behavior. We formed an initial prior by estimating the long-term ground trends and developed four additional priors by using smoothing. These prior features represent the expected behavior over the long term of the missing data and allow the regression approach to perform well, even over large gaps of up to 50 years. We demonstrated our method on the Beryl-Enterprise aquifer in Utah and found the imputed results follow trends in the observed data and hydrogeological principles, even over long periods with no observed data. Full article
(This article belongs to the Special Issue Satellite Data Assimilation for Groundwater Analysis)
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