Using Satellite-Based Vegetation Cover as Indicator of Groundwater Storage in Natural Vegetation Areas

被引:37
|
作者
Bhanja, Soumendra N. [1 ,2 ]
Malakar, Pragnaditya [1 ]
Mukherjee, Abhijit [1 ]
Rodell, Matthew [3 ]
Mitra, Pabitra [4 ]
Sarkar, Sudeshna [4 ]
机构
[1] Indian Inst Technol Kharagpur, Dept Geol & Geophys, Kharagpur, W Bengal, India
[2] Athabasca Univ, Fac Sci & Technol, Athabasca, AB, Canada
[3] NASA, Goddard Space Flight Ctr, Hydrol Sci Lab, Greenbelt, MD USA
[4] Indian Inst Technol Kharagpur, Dept Comp Sci & Engn, Khar, Kharagpur, W Bengal, India
关键词
Groundwater level prediction; NDVI; ANN; SVM; India; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; IN-SITU; WATER MANAGEMENT; GRACE; RAINFALL; DEPLETION; VARIABILITY; PREDICTION; MODELS;
D O I
10.1029/2019GL083015
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Normalized Difference Vegetation Index (NDVI) is widely used as an efficient indicator of vegetation cover. Here we assess the possibility of using NDVI as an indicator of groundwater storage. We used groundwater level (GWL) obtained from in situ groundwater observation wells (n > 15,000) in India in 2005-2013. Good correlation (r > 0.6) is observed between NDVI and GWL in natural vegetation-covered areas, that is, forest lands, shrubs, and grasslands. We apply artificial neural network and support vector machine approaches to investigate the relationship between GWL and NDVI using both of the parameters as input. Artificial neural network- and support vector machine-simulated GWL matches very well with observed GWL, particularly in naturally vegetated areas. Thus, we interpret that NDVI may be used as a suitable indicator of groundwater storage conditions in certain areas where the water table is shallow and the vegetation is natural and where in situ groundwater observations are not available.
引用
收藏
页码:8082 / 8092
页数:11
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