干旱区生态环境知识资源中心(Arid): Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia

Arid

DOI	10.3390/rs12091361
	Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia
	Alshehri, Fahad 1,2; Sultan, Mohamed 1; Karki, Sita 3; Alwagdani, Essam 4; Alsefry, Saleh 4; Alharbi, Hassan 4; Sahour, Hossein 1; Sturchio, Neil 5
通讯作者	Sultan, Mohamed
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:9
英文摘要	Identifying shallow (near-surface) groundwater in arid and hyper-arid areas has significant societal benefits, yet it is a costly operation when traditional methods (geophysics and drilling) are applied over large domains. In this study, we developed and successfully applied methodologies that rely heavily on readily available temporal, visible, and near-infrared radar and thermal remote sensing data sets and field data, as well as statistical approaches to map the distribution of shallow (1-5 m deep) groundwater occurrences in Al Qunfudah Province, Saudi Arabia, and to identify the factors controlling their development. A four-fold approach was adopted: (1) constructing a digital database to host relevant geologic, hydrogeologic, topographic, land use, climatic, and remote sensing data sets, (2) identifying the distribution of areas characterized by shallow groundwater levels, (3) developing conceptual and statistical models to map the distribution of shallow groundwater occurrences, and (4) constructing an artificial neural network (ANN) and multivariate regression (MR) models to map the distribution of shallow groundwater, test the models over areas of known depth to groundwater (area of Al Qunfudah city and surroundings: 294 km(2)), and apply the better of the two models to map the shallow groundwater occurrences across the entire Al Qunfudah Province (area: 4680 km(2)). Findings include: (1) high performance for the ANN (92%) and MR (88%) models in predicting the distribution of shallow groundwater using temporal-derived remote sensing products (e.g., normalized difference vegetation index (NDVI), radar backscatter coefficient, precipitation, and brightness temperature) and field data (depth to water table), (2) areas witnessing shallow groundwater levels show high NDVI (mean and standard deviation (STD)), radar backscatter coefficient values (mean and STD), and low brightness temperature (mean and STD) compared to their surroundings, (3) correlations of temporal groundwater levels and satellite-based precipitation suggest that the observed (2017-2019) rise in groundwater levels is related to an increase in precipitation in these years compared to the previous three years (2014-2016), and (4) the adopted methodologies are reliable, cost-effective, and could potentially be applied to identify shallow groundwater along the Red Sea Hills and in similar settings worldwide.
英文关键词	Moderate resolution imaging spectroradiometer (MODIS) radar backscattering coefficient multivariate regression and artificial neural networks shallow groundwater
类型	Article
语种	英语
国家	USA ; Saudi Arabia ; Ireland
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000543394000008
WOS关键词	WATER ; VEGETATION ; AREA ; PRECIPITATION ; EVOLUTION ; PATTERNS ; RECHARGE ; SAHARAN ; IMAGERY ; COVER
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	King Saud University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319751
作者单位	1.Western Michigan Univ, Dept Geol & Environm Sci, Kalamazoo, MI 49008 USA; 2.King Saud Univ, Geol & Geophys Dept, Riyadh 11451, Saudi Arabia; 3.Irish Ctr High End Comp ICHEC, Galway H91 TK33, Ireland; 4.Saudi Geol Survey, Jeddah 21514, Saudi Arabia; 5.Univ Delaware, Dept Earth Sci, Newark, DE 19716 USA
推荐引用方式 GB/T 7714	Alshehri, Fahad,Sultan, Mohamed,Karki, Sita,et al. Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia[J]. King Saud University,2020,12(9).
APA	Alshehri, Fahad.,Sultan, Mohamed.,Karki, Sita.,Alwagdani, Essam.,Alsefry, Saleh.,...&Sturchio, Neil.(2020).Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia.REMOTE SENSING,12(9).
MLA	Alshehri, Fahad,et al."Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia".REMOTE SENSING 12.9(2020).