Using Satellite‐Based Vegetation Cover as Indicator of Groundwater Storage in Natural Vegetation Areas
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 Indi...
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| Veröffentlicht in: | Geophysical research letters 2019-07, Vol.46 (14), p.8082-8092 |
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| creator | Bhanja, Soumendra N. Malakar, Pragnaditya Mukherjee, Abhijit Rodell, Matthew Mitra, Pabitra Sarkar, Sudeshna |
| description | 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.
Plain Language Summary
Long‐term groundwater resources monitoring is costly at large scales. Satellite‐based estimations based on Gravity Recovery and Climate Experiment satellite observations can provide groundwater resource information at coarse spatial and temporal resolution. In this study, we used widely available, high‐resolution vegetation index data and investigated the possibility of using it as a proxy of groundwater storage. Artificial intelligence‐based approaches that incorporates vegetation index data show good performance in estimating groundwater levels. The results are particularly encouraging in natural vegetation covered areas.
Key Points
Widely available, satellite‐based vegetation index may be used as a suitable indicator of groundwater storage
Artificial intelligence estimates show good performance of vegetation index on predicting future groundwater levels
Vegetation index can be used as an indicator of groundwater storage particularly at natural vegetation‐covered areas |
| doi_str_mv | 10.1029/2019GL083015 |
| format | Article |
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Plain Language Summary
Long‐term groundwater resources monitoring is costly at large scales. Satellite‐based estimations based on Gravity Recovery and Climate Experiment satellite observations can provide groundwater resource information at coarse spatial and temporal resolution. In this study, we used widely available, high‐resolution vegetation index data and investigated the possibility of using it as a proxy of groundwater storage. Artificial intelligence‐based approaches that incorporates vegetation index data show good performance in estimating groundwater levels. The results are particularly encouraging in natural vegetation covered areas.
Key Points
Widely available, satellite‐based vegetation index may be used as a suitable indicator of groundwater storage
Artificial intelligence estimates show good performance of vegetation index on predicting future groundwater levels
Vegetation index can be used as an indicator of groundwater storage particularly at natural vegetation‐covered areas</description><identifier>ISSN: 0094-8276</identifier><identifier>EISSN: 1944-8007</identifier><identifier>DOI: 10.1029/2019GL083015</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>ANN ; Artificial intelligence ; Artificial neural networks ; Computer simulation ; GRACE (experiment) ; Grasslands ; Gravity ; Groundwater ; Groundwater availability ; Groundwater level prediction ; Groundwater levels ; Groundwater resources ; Groundwater storage ; Groundwater table ; India ; Natural vegetation ; NDVI ; Neural networks ; Normalized difference vegetative index ; Observation wells ; Plant cover ; Resolution ; Satellite observation ; Satellites ; Shrubs ; Storage conditions ; Support vector machines ; SVM ; Temporal resolution ; Vegetation ; Vegetation cover ; Vegetation index ; Water resources ; Water table</subject><ispartof>Geophysical research letters, 2019-07, Vol.46 (14), p.8082-8092</ispartof><rights>2019. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a3295-1001456f82dbb1ea8efb53977026721e2115fb271631d2fc39fbd6fbf65b78f93</citedby><cites>FETCH-LOGICAL-a3295-1001456f82dbb1ea8efb53977026721e2115fb271631d2fc39fbd6fbf65b78f93</cites><orcidid>0000-0003-0106-7437 ; 0000-0002-0555-0875 ; 0000-0002-9434-8483 ; 0000-0003-3439-4282</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2019GL083015$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2019GL083015$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,1432,11512,27922,27923,45572,45573,46407,46466,46831,46890</link.rule.ids></links><search><creatorcontrib>Bhanja, Soumendra N.</creatorcontrib><creatorcontrib>Malakar, Pragnaditya</creatorcontrib><creatorcontrib>Mukherjee, Abhijit</creatorcontrib><creatorcontrib>Rodell, Matthew</creatorcontrib><creatorcontrib>Mitra, Pabitra</creatorcontrib><creatorcontrib>Sarkar, Sudeshna</creatorcontrib><title>Using Satellite‐Based Vegetation Cover as Indicator of Groundwater Storage in Natural Vegetation Areas</title><title>Geophysical research letters</title><description>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.
Plain Language Summary
Long‐term groundwater resources monitoring is costly at large scales. Satellite‐based estimations based on Gravity Recovery and Climate Experiment satellite observations can provide groundwater resource information at coarse spatial and temporal resolution. In this study, we used widely available, high‐resolution vegetation index data and investigated the possibility of using it as a proxy of groundwater storage. Artificial intelligence‐based approaches that incorporates vegetation index data show good performance in estimating groundwater levels. The results are particularly encouraging in natural vegetation covered areas.
Key Points
Widely available, satellite‐based vegetation index may be used as a suitable indicator of groundwater storage
Artificial intelligence estimates show good performance of vegetation index on predicting future groundwater levels
Vegetation index can be used as an indicator of groundwater storage particularly at natural vegetation‐covered areas</description><subject>ANN</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Computer simulation</subject><subject>GRACE (experiment)</subject><subject>Grasslands</subject><subject>Gravity</subject><subject>Groundwater</subject><subject>Groundwater availability</subject><subject>Groundwater level prediction</subject><subject>Groundwater levels</subject><subject>Groundwater resources</subject><subject>Groundwater storage</subject><subject>Groundwater table</subject><subject>India</subject><subject>Natural vegetation</subject><subject>NDVI</subject><subject>Neural networks</subject><subject>Normalized difference vegetative index</subject><subject>Observation wells</subject><subject>Plant cover</subject><subject>Resolution</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Shrubs</subject><subject>Storage conditions</subject><subject>Support vector machines</subject><subject>SVM</subject><subject>Temporal resolution</subject><subject>Vegetation</subject><subject>Vegetation cover</subject><subject>Vegetation index</subject><subject>Water resources</subject><subject>Water table</subject><issn>0094-8276</issn><issn>1944-8007</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kMFOwzAQRC0EEqVw4wMscSWwa9dJfCwVhEoRSJRyjZzGLqlCXGyHqjc-gW_kSwgqh544zWr0ZlYaQs4RrhCYvGaAMssh5YDigAxQjkZRCpAckgGA7G-WxMfkxPsVAHDgOCCvc1-3SzpTQTdNHfT359eN8rqiL3qpgwq1benEfmhHlafTtqoXKlhHraGZs11bbfqgo7PeU0tN65Y-qNA51eznx04rf0qOjGq8PvvTIZnf3T5P7qP8MZtOxnmkOJMiQgAcidikrCpL1CrVphRcJgmwOGGoGaIwJUsw5lgxs-DSlFVsShOLMkmN5ENysetdO_veaR-Kle1c278sGOsbUHKEnrrcUQtnvXfaFGtXvym3LRCK3y2L_S17nO3wTd3o7b9skT3lQrJY8B8kjHXH</recordid><startdate>20190728</startdate><enddate>20190728</enddate><creator>Bhanja, Soumendra N.</creator><creator>Malakar, Pragnaditya</creator><creator>Mukherjee, Abhijit</creator><creator>Rodell, Matthew</creator><creator>Mitra, Pabitra</creator><creator>Sarkar, Sudeshna</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>8FD</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-0106-7437</orcidid><orcidid>https://orcid.org/0000-0002-0555-0875</orcidid><orcidid>https://orcid.org/0000-0002-9434-8483</orcidid><orcidid>https://orcid.org/0000-0003-3439-4282</orcidid></search><sort><creationdate>20190728</creationdate><title>Using Satellite‐Based Vegetation Cover as Indicator of Groundwater Storage in Natural Vegetation Areas</title><author>Bhanja, Soumendra N. ; Malakar, Pragnaditya ; Mukherjee, Abhijit ; Rodell, Matthew ; Mitra, Pabitra ; Sarkar, Sudeshna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a3295-1001456f82dbb1ea8efb53977026721e2115fb271631d2fc39fbd6fbf65b78f93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>ANN</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Computer simulation</topic><topic>GRACE (experiment)</topic><topic>Grasslands</topic><topic>Gravity</topic><topic>Groundwater</topic><topic>Groundwater availability</topic><topic>Groundwater level prediction</topic><topic>Groundwater levels</topic><topic>Groundwater resources</topic><topic>Groundwater storage</topic><topic>Groundwater table</topic><topic>India</topic><topic>Natural vegetation</topic><topic>NDVI</topic><topic>Neural networks</topic><topic>Normalized difference vegetative index</topic><topic>Observation wells</topic><topic>Plant cover</topic><topic>Resolution</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Shrubs</topic><topic>Storage conditions</topic><topic>Support vector machines</topic><topic>SVM</topic><topic>Temporal resolution</topic><topic>Vegetation</topic><topic>Vegetation cover</topic><topic>Vegetation index</topic><topic>Water resources</topic><topic>Water table</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bhanja, Soumendra N.</creatorcontrib><creatorcontrib>Malakar, Pragnaditya</creatorcontrib><creatorcontrib>Mukherjee, Abhijit</creatorcontrib><creatorcontrib>Rodell, Matthew</creatorcontrib><creatorcontrib>Mitra, Pabitra</creatorcontrib><creatorcontrib>Sarkar, Sudeshna</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Geophysical research letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bhanja, Soumendra N.</au><au>Malakar, Pragnaditya</au><au>Mukherjee, Abhijit</au><au>Rodell, Matthew</au><au>Mitra, Pabitra</au><au>Sarkar, Sudeshna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using Satellite‐Based Vegetation Cover as Indicator of Groundwater Storage in Natural Vegetation Areas</atitle><jtitle>Geophysical research letters</jtitle><date>2019-07-28</date><risdate>2019</risdate><volume>46</volume><issue>14</issue><spage>8082</spage><epage>8092</epage><pages>8082-8092</pages><issn>0094-8276</issn><eissn>1944-8007</eissn><abstract>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.
Plain Language Summary
Long‐term groundwater resources monitoring is costly at large scales. Satellite‐based estimations based on Gravity Recovery and Climate Experiment satellite observations can provide groundwater resource information at coarse spatial and temporal resolution. In this study, we used widely available, high‐resolution vegetation index data and investigated the possibility of using it as a proxy of groundwater storage. Artificial intelligence‐based approaches that incorporates vegetation index data show good performance in estimating groundwater levels. The results are particularly encouraging in natural vegetation covered areas.
Key Points
Widely available, satellite‐based vegetation index may be used as a suitable indicator of groundwater storage
Artificial intelligence estimates show good performance of vegetation index on predicting future groundwater levels
Vegetation index can be used as an indicator of groundwater storage particularly at natural vegetation‐covered areas</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2019GL083015</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-0106-7437</orcidid><orcidid>https://orcid.org/0000-0002-0555-0875</orcidid><orcidid>https://orcid.org/0000-0002-9434-8483</orcidid><orcidid>https://orcid.org/0000-0003-3439-4282</orcidid></addata></record> |
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| ispartof | Geophysical research letters, 2019-07, Vol.46 (14), p.8082-8092 |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Free Content; Wiley-Blackwell AGU Digital Library; Wiley Online Library All Journals |
| subjects | ANN Artificial intelligence Artificial neural networks Computer simulation GRACE (experiment) Grasslands Gravity Groundwater Groundwater availability Groundwater level prediction Groundwater levels Groundwater resources Groundwater storage Groundwater table India Natural vegetation NDVI Neural networks Normalized difference vegetative index Observation wells Plant cover Resolution Satellite observation Satellites Shrubs Storage conditions Support vector machines SVM Temporal resolution Vegetation Vegetation cover Vegetation index Water resources Water table |
| title | Using Satellite‐Based Vegetation Cover as Indicator of Groundwater Storage in Natural Vegetation Areas |
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