{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:07:32Z","timestamp":1768590452609,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T00:00:00Z","timestamp":1594684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Agricultural production in the Great Plains provides a significant amount of food for the United States while contributing greatly to farm income in the region. However, recurrent droughts and expansion of crop production are increasing irrigation demand, leading to extensive pumping and attendant depletion of the Ogallala aquifer. In order to optimize water use, increase the sustainability of agricultural production, and identify best management practices, identification of food\u2013water conflict hotspots in the Ogallala Aquifer Region (OAR) is necessary. We used satellite remote sensing time series of agricultural production (net primary production, NPP) and total water storage (TWS) to identify hotspots of food\u2013water conflicts within the OAR and possible reasons behind these conflicts. Mean annual NPP (2001\u20132018) maps clearly showed intrusion of high NPP, aided by irrigation, into regions of historically low NPP (due to precipitation and temperature). Intrusion is particularly acute in the northern portion of OAR, where mean annual TWS (2002\u20132020) is high. The Oklahoma panhandle and Texas showed large decreasing TWS trends, which indicate the negative effects of current water demand for crop production on TWS. Nebraska demonstrated an increasing TWS trend even with a significant increase of NPP. A regional analysis of NPP and TWS can convey important information on current and potential conflicts in the food\u2013water nexus and facilitate sustainable solutions. Methods developed in this study are relevant to other water-constrained agricultural production regions.<\/jats:p>","DOI":"10.3390\/rs12142257","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T11:03:23Z","timestamp":1594724603000},"page":"2257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Inspecting the Food\u2013Water Nexus in the Ogallala Aquifer Region Using Satellite Remote Sensing Time Series"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6794-4861","authenticated-orcid":false,"given":"Yuting","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamed","family":"Gholizadeh","sequence":"additional","affiliation":[{"name":"Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4900-063X","authenticated-orcid":false,"given":"G. Thomas","family":"LaVanchy","sequence":"additional","affiliation":[{"name":"Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8337-0423","authenticated-orcid":false,"given":"Emad","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Geological Sciences and Environmental Studies, State University of New York at Binghamton, Binghamton, NY 13902, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3389\/fenvs.2019.00008","article-title":"The Development of the Water-Energy-Food Nexus as a Framework for Achieving Resource Security: A review","volume":"7","author":"Simpson","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1029\/2017RG000591","article-title":"The Global Food-Energy-Water Nexus","volume":"56","author":"Davis","year":"2018","journal-title":"Rev. Geophys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s10040-004-0385-6","article-title":"Groundwater recharge and sustainability in the High Plains aquifer in Kansas, USA","volume":"13","author":"Sophocleous","year":"2005","journal-title":"Hydrogeol. J."},{"key":"ref_4","first-page":"99","article-title":"The High Plains Aquifer, USA: Groundwater development and sustainability","volume":"Volume 193","author":"Hiscock","year":"2002","journal-title":"Sustainable Groundwater Development"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US department of agriculture, national agricultural statistics service Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2004.12.011","article-title":"Improvements of the MODIS terrestrial gross and net primary production global data set","volume":"95","author":"Zhao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/15481603.2015.1124488","article-title":"World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery","volume":"53","author":"Xie","year":"2016","journal-title":"GIScience Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/j.energy.2019.04.221","article-title":"Modeling electricity consumption using nighttime light images and artificial neural networks","volume":"179","year":"2019","journal-title":"Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"116351","DOI":"10.1016\/j.energy.2019.116351","article-title":"An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery","volume":"189","author":"Lu","year":"2019","journal-title":"Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1029\/2002WR001808","article-title":"Estimated accuracies of regional water storage variations inferred from the Gravity Recovery and Climate Experiment (GRACE)","volume":"39","author":"Swenson","year":"2003","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1038\/nature08238","article-title":"Satellite-based estimates of groundwater depletion in India","volume":"460","author":"Rodell","year":"2009","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1038\/nclimate2425","article-title":"The global groundwater crisis","volume":"4","author":"Famiglietti","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1038\/s41586-018-0123-1","article-title":"Emerging trends in global freshwater availability","volume":"557","author":"Rodell","year":"2018","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Koppa, A., and Gebremichael, M. (2020). Improving the Applicability of Hydrologic Models for Food-Energy-Water Nexus Studies Using Remote Sensing Data. Remote Sens., 12.","DOI":"10.3390\/rs12040599"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.jclepro.2016.01.034","article-title":"The energy-water agriculture nexus: The past present and future of holistic resource management via remote sensing technologies","volume":"117","author":"Sanders","year":"2016","journal-title":"J. Clean. Prod."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alam, S., Gebremichael, M., and Li, R. (2019). Remote Sensing-Based Assessment of the Crop, Energy and Water Nexus in the Central Valley, California. Remote Sens., 11.","DOI":"10.3390\/rs11141701"},{"key":"ref_18","unstructured":"(2020, May 27). USDA National Agricultural Economic Research Service, Available online: https:\/\/www.ers.usda.gov\/topics\/farm-practices-management\/irrigation-water-use\/#importance."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Houston, N.A., Gonzales-Bradford, S.L., Flynn, A.T., Qi, S.L., Peterson, S.M., Stanton, J.S., Ryter, D.W., Sohl, T.L., and Senay, G.B. (2013). Geodatabase Compilation of Hydrogeologic, Remote Sensing, and Water-Budget-Component Data for the High Plains Aquifer 2011.","DOI":"10.3133\/ds777"},{"key":"ref_20","unstructured":"(2020, May 29). USDA National Agricultural Statistics Service, Available online: http:\/\/www.nass.usda.gov."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Stanton, J.S., Qi, S.L., Ryter, D.W., Houston, S.E., Peterson, N.A., Westenbroek, S.M., and Christenson, S.C. (2011). Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009, U.S. Geological Survey Scientific Investigations Report 2011-5183.","DOI":"10.3133\/sir20115183"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s10040-009-0540-1","article-title":"Review: Groundwater management practices, challenges, and innovation in the High Plains Aquifer, USA\u2014Lessons and recommended actions","volume":"18","author":"Sophocleous","year":"2010","journal-title":"Hydrogeol. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qi, S.L. (2010). Digital Map of the Aquifer Boundary of the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming.","DOI":"10.3133\/ds543"},{"key":"ref_24","unstructured":"PRISM Climate Group, Oregon State University (2020, January 04). Available online: http:\/\/prism.oregonstate.edu."},{"key":"ref_25","unstructured":"Schowalter, T.D. (2016). Insect Ecology: An Ecosystem Approach, Academic Press. [4th ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.ecolmodel.2004.08.023","article-title":"Remote sensing of crop production in China by production efficiency models: Models, comparisons, estimates and uncertainties","volume":"183","author":"Tao","year":"2005","journal-title":"Ecol. Model."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1890\/1051-0761(2001)011[1194:NPPOUS]2.0.CO;2","article-title":"Net Primary Production of U.S. Midwest Croplands from Agricultural Harvest Yield Data","volume":"11","author":"Prince","year":"2001","journal-title":"Ecol. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5368","DOI":"10.3390\/rs6065368","article-title":"Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China","volume":"6","author":"Zhao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_29","first-page":"A90","article-title":"Mapping Weekly Rangeland Vegetation Productivity Using MODIS Algorithms","volume":"54","author":"Reeves","year":"2001","journal-title":"J. Range Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1890\/1051-0761(2001)011[1174:BANEFT]2.0.CO;2","article-title":"Biomass and NPP Estimation for the Mid-Atlantic Region (USA) using plot-level forest inventory data","volume":"11","author":"Jenkins","year":"2001","journal-title":"Ecol. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chapin, F.S., Matson, P.A., and Vitousek, P.M. (2011). Principles of Terrestrial Ecosystem Ecology, Springer. [2nd ed.].","DOI":"10.1007\/978-1-4419-9504-9"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1641\/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2","article-title":"A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production","volume":"54","author":"Running","year":"2004","journal-title":"BioScience"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2006.02.017","article-title":"Evaluation of MODIS NPP and GPP products across multiple biomes","volume":"102","author":"Turner","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1111\/j.1466-8238.2008.00442.x","article-title":"Global pattern of NPP to GPP ratio derived from MODIS data: Effects of ecosystem type, geographical location and climate","volume":"18","author":"Zhang","year":"2009","journal-title":"Glob. Ecol. Biogeog."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2111\/1551-5028(2006)59[001:AIEOMP]2.0.CO;2","article-title":"Applying improved estimates of MODIS productivity to characterize grassland vegetation dynamics","volume":"59","author":"Reeves","year":"2006","journal-title":"Range Ecol. Manag."},{"key":"ref_36","unstructured":"Heinsch, F.A., Reeves, M., and Bowker, C.F. (2020, January 20). User\u2019s Guide, GPP and NPP (MOD 17A2\/A3) Products, NASA MODIS Land Algorithm. Available online: http:\/\/www.forestry.umt.edu\/ntsg\/."},{"key":"ref_37","unstructured":"Running, S.W., Mu, Q., and Zhao, M. (2019, October 31). MOD17A3HGF MODIS\/Terra Net Primary Production Yearly Gap-Filled L4 Global 500 m SIN Grid V006 [Data set]. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MOD17A3HGF.006."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., Zhu, Z., Xian, G., and Howard, D. (2019). Overall methodology design for the United States Land Cover Database 2016 products. Remote Sens., 11.","DOI":"10.3390\/rs11242971"},{"key":"ref_39","unstructured":"Save, H. (2020, March 15). CSR GRACE RL06 Mascon Solutions. Available online: https:\/\/doi.org\/10.18738\/T8\/UN91VR."},{"key":"ref_40","unstructured":"Wiese, D.N., Yuan, D.-N., Boening, C., Landerer, F.W., and Watkins, M.M. (2020, March 15). JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height Coastal Resolution Improvement (CRI) Filtered Release 06 Version 02. Ver. 02. PO.DAAC, CA, USA. Available online: https:\/\/doi.org\/10.5067\/TEMSC-3JC62."},{"key":"ref_41","unstructured":"Felix Landerer (2020, March 15). CSR TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 Version 03 in netCDF\/ASCII\/GeoTIFF Formats. Available online: https:\/\/doi.org\/10.5067\/GFLND-3AC63."},{"key":"ref_42","unstructured":"ASA\/JPL (2020, March 15). Monthly Gridded Global Land Data Assimilation System (GLDAS) from Noah-v3.3 Land Hydrology Model for GRACE and GRACE-FO over Nominal Months. Available online: https:\/\/doi.org\/10.5067\/GGDAS-3NH33."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"L18401","DOI":"10.1029\/2009GL039401","article-title":"Dwindling groundwater resources in northern India, from satellite gravity observations","volume":"36","author":"Tiwari","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"L14402","DOI":"10.1029\/2007GL030139","article-title":"Comparison of seasonal terrestrial water storage variations from GRACE with groundwater-level measurements from the High Plains Aquifer (USA)","volume":"34","author":"Strassberg","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9412","DOI":"10.1002\/2016WR019494","article-title":"Global evaluation of new GRACE mascon products for hydrologic applications","volume":"52","author":"Scanlon","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hasan, E., Tarhule, A., Hong, Y., and Moore, B. (2019). Assessment of Physical Water Scarcity in Africa Using GRACE and TRMM Satellite Data. Remote Sens., 11.","DOI":"10.3390\/rs11080904"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"12327","DOI":"10.1038\/s41598-019-48813-x","article-title":"+50 Years of Terrestrial Hydroclimatic Variability in Africa\u2019s Transboundary Waters","volume":"9","author":"Hasan","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/0304-4076(88)90077-2","article-title":"Regression by Local Fitting","volume":"37","author":"Cleveland","year":"1988","journal-title":"J. Econ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/S0022-1694(97)00125-X","article-title":"A modified Mann-Kendall trend test for autocorrelated data","volume":"204","author":"Hamed","year":"1998","journal-title":"J. Hydrol."},{"key":"ref_50","unstructured":"Gilbert, R.O. (1987). Statistical Methods for Environmental Pollution Monitoring, Van Nostrand Reinhold Company."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"9320","DOI":"10.1073\/pnas.1200311109","article-title":"Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley","volume":"109","author":"Scanlon","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/14\/2257\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:51:22Z","timestamp":1760176282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/14\/2257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,14]]},"references-count":51,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12142257"],"URL":"https:\/\/doi.org\/10.3390\/rs12142257","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,14]]}}}