{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:16:49Z","timestamp":1776277009005,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NOAA COM","award":["NA20OAR4310341"],"award-info":[{"award-number":["NA20OAR4310341"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Groundwater-based irrigation has dramatically expanded over the past decades. It has important implications for terrestrial water, energy fluxes, and food production, as well as local to regional climates. However, irrigation water use is hard to monitor at large scales due to various constraints, including the high cost of metering equipment installation and maintenance, privacy issues, and the presence of illegal or unregistered wells. This study estimates irrigation water amounts using machine learning to integrate in situ pumping records, remote sensing products, and climate data in the Kansas High Plains. We use a random forest regression to estimate the annual irrigation water amount at a reprojected spatial resolution of 6 km based on various data, including remotely sensed vegetation indices and evapotranspiration (ET), land cover, near-surface meteorological forcing, and a satellite-derived irrigation map. In addition, we assess the value of ECOSTRESS ET products for irrigation water use estimation and compare with the baseline results by using MODIS ET. The random forest regression model can capture the temporal and spatial variability of irrigation amounts with a satisfactory accuracy (R2 = 0.82). It performs reasonably well when it is calibrated on the western portion of the study area and tested on the eastern portion that receives more rain than the western one, suggesting its potential transferability to other regions. ECSOTRESS ET and MODIS ET yield a similar irrigation estimation accuracy.<\/jats:p>","DOI":"10.3390\/rs14133004","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"3004","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains"],"prefix":"10.3390","volume":"14","author":[{"given":"Shiqi","family":"Wei","sequence":"first","affiliation":[{"name":"School of Sustainable Engineering and The Built Environment, Arizona State University, Tempe, AZ 85287, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9565-9208","authenticated-orcid":false,"given":"Tianfang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Sustainable Engineering and The Built Environment, Arizona State University, Tempe, AZ 85287, USA"}]},{"given":"Guo-Yue","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3229-3146","authenticated-orcid":false,"given":"Ruijie","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Sustainable Engineering and The Built Environment, Arizona State University, Tempe, AZ 85287, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.5194\/hess-14-1863-2010","article-title":"Groundwater Use for Irrigation\u2014A Global Inventory","volume":"14","author":"Siebert","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_2","unstructured":"Gleick, P.H., Cooley, H., Morikawa, M., Morrison, J., and Cohen, M.J. (2009). The World\u2019s Water, 2008\u20132009: The Biennial Report on Freshwater Resources, Island Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1126\/science.271.5250.785","article-title":"Human Appropriation of Renewable Fresh Water","volume":"271","author":"Postel","year":"1996","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1111\/gwat.12350","article-title":"Water Level Declines in the High Plains Aquifer: Predevelopment to Resource Senescence","volume":"54","author":"Haacker","year":"2016","journal-title":"Ground Water"},{"key":"ref_5","unstructured":"Liu, G., Wilson, B., Whittemore, D., Jin, W., and Butler, J. (2022, March 22). Ground-Water Model for Southwest Kansas Groundwater Management District No. 3. Available online: https:\/\/www.kgs.ku.edu\/Hydro\/Publications\/2010\/OFR10_18\/index.html."},{"key":"ref_6","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"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2011GL048604","DOI":"10.1029\/2011GL048604","article-title":"Contribution of Global Groundwater Depletion since 1900 to Sea-Level Rise","volume":"38","author":"Konikow","year":"2011","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2006GL028679","DOI":"10.1029\/2006GL028679","article-title":"Irrigation Cooling Effect: Regional Climate Forcing by Land-Use Change","volume":"34","author":"Kueppers","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s00382-008-0445-z","article-title":"Effects of Global Irrigation on the Near-Surface Climate","volume":"33","author":"Sacks","year":"2009","journal-title":"Clim. Dyn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.1175\/2008JCLI2703.1","article-title":"Regional Differences in the Influence of Irrigation on Climate","volume":"22","author":"Lobell","year":"2009","journal-title":"J. Clim."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2003WR002822","DOI":"10.1029\/2003WR002822","article-title":"Irrigation-Induced Changes in Potential Evapotranspiration in Southeastern Turkey: Test and Application of Bouchet\u2019s Complementary Hypothesis","volume":"40","author":"Ozdogan","year":"2004","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1175\/2009JHM1116.1","article-title":"Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data","volume":"11","author":"Ozdogan","year":"2010","journal-title":"J. Hydrometeorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1002\/2017JD027010","article-title":"Snow Cover and Vegetation-Induced Decrease in Global Albedo from 2002 to 2016","volume":"123","author":"Li","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1038\/nclimate3001","article-title":"Fate of Water Pumped from Underground and Contributions to Sea-Level Rise","volume":"6","author":"Wada","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16120","DOI":"10.1029\/2010JD014122","article-title":"Effects of Irrigation on Global Climate during the 20th Century","volume":"115","author":"Puma","year":"2010","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12245","DOI":"10.1002\/2017JD027597","article-title":"A Systematic Evaluation of Noah-MP in Simulating Land-Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United States","volume":"122","author":"Ma","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00382-016-3327-9","article-title":"Continental-Scale Convection-Permitting Modeling of the Current and Future Climate of North America","volume":"49","author":"Liu","year":"2017","journal-title":"Clim. Dyn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"E1080","DOI":"10.1073\/pnas.1704665115","article-title":"Global Models Underestimate Large Decadal Declining and Rising Water Storage Trends Relative to GRACE Satellite Data","volume":"115","author":"Scanlon","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5282","DOI":"10.1029\/2017WR022178","article-title":"Groundwater Withdrawals under Drought: Reconciling GRACE and Land Surface Models in the United States High Plains Aquifer","volume":"54","author":"Nie","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Massari, C., Modanesi, S., Dari, J., Gruber, A., De Lannoy, G.J.M., Girotto, M., Quintana-Segu\u00ed, P., Le Page, M., Jarlan, L., and Zribi, M. (2021). A Review of Irrigation Information Retrievals from Space and Their Utility for Users. Remote Sens., 13.","DOI":"10.3390\/rs13204112"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4463","DOI":"10.5194\/hess-19-4463-2015","article-title":"Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes","volume":"19","author":"Kumar","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2016.02.046","article-title":"Comparison of Remote Sensing and Simulated Soil Moisture Datasets in Mediterranean Landscapes","volume":"180","author":"Escorihuela","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"303","DOI":"10.2166\/wcc.2016.122","article-title":"Discerning Shifting Irrigation Practices from Passive Microwave Radiometry over Punjab and Haryana","volume":"8","author":"Singh","year":"2017","journal-title":"J. Water Clim. Chang."},{"key":"ref_24","first-page":"17","article-title":"Comparison of Temporal Trends from Multiple Soil Moisture Data Sets and Precipitation: The Implication of Irrigation on Regional Soil Moisture Trend","volume":"48","author":"Qiu","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11860","DOI":"10.1002\/2017GL075733","article-title":"Irrigation Signals Detected from SMAP Soil Moisture Retrievals","volume":"44","author":"Lawston","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_26","first-page":"752","article-title":"How Much Water Is Used for Irrigation? A New Approach Exploiting Coarse Resolution Satellite Soil Moisture Products","volume":"73","author":"Brocca","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111226","DOI":"10.1016\/j.rse.2019.111226","article-title":"Quantification of Irrigation Water Using Remote Sensing of Soil Moisture in a Semi-Arid Region","volume":"231","author":"Jalilvand","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dari, J., Brocca, L., Quintana-Segu\u00ed, P., Escorihuela, M.J., Stefan, V., and Morbidelli, R. (2020). Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region. Remote Sens., 12.","DOI":"10.3390\/rs12162593"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107537","DOI":"10.1016\/j.agwat.2022.107537","article-title":"Irrigation Estimates from Space: Implementation of Different Approaches to Model the Evapotranspiration Contribution within a Soil-Moisture-Based Inversion Algorithm","volume":"265","author":"Dari","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"897","DOI":"10.5194\/hess-23-897-2019","article-title":"Estimating Irrigation Water Use over the Contiguous United States by Combining Satellite and Reanalysis Soil Moisture Data","volume":"23","author":"Zaussinger","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zappa, L., Schlaffer, S., Bauer-Marschallinger, B., Nendel, C., Zimmerman, B., and Dorigo, W. (2021). Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture. Remote Sens., 13.","DOI":"10.3390\/rs13091727"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1016\/j.agwat.2010.03.017","article-title":"Estimating Actual Irrigation Application by Remotely Sensed Evapotranspiration Observations","volume":"97","author":"Droogers","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10033","DOI":"10.3390\/rs61010033","article-title":"Application of a Remote Sensing Method for Estimating Monthly Blue Water Evapotranspiration in Irrigated Agriculture","volume":"6","author":"Romaguera","year":"2014","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.agee.2014.10.023","article-title":"A Novel Approach to Estimate Direct and Indirect Water Withdrawals from Satellite Measurements: A Case Study from the Incomati Basin","volume":"200","author":"Bastiaanssen","year":"2015","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5251","DOI":"10.5194\/hess-24-5251-2020","article-title":"Mapping Groundwater Abstractions from Irrigated Agriculture: Big Data, Inverse Modeling, and a Satellite-Model Fusion Approach","volume":"24","author":"Valencia","year":"2020","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_36","first-page":"102067","article-title":"An Object-Based Image Analysis Approach to Assess Irrigation-Water Consumption from MODIS Products in Ethiopia","volume":"88","author":"Vogels","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2020WR027738","DOI":"10.1029\/2020WR027738","article-title":"Irrigation Water Demand Sensitivity to Climate Variability Across the Contiguous United States","volume":"57","author":"Nie","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2020WR028867","DOI":"10.1029\/2020WR028867","article-title":"Influence of Irrigation Drivers Using Boosted Regression Trees: Kansas High Plains","volume":"57","author":"Lamb","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2020WR028378","DOI":"10.1029\/2020WR028378","article-title":"Satellite-Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy","volume":"56","author":"Foster","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"044014","DOI":"10.1088\/1748-9326\/aafe39","article-title":"Quantifying Irrigation Adaptation Strategies in Response to Stakeholder-Driven Groundwater Management in the US High Plains Aquifer","volume":"14","author":"Deines","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9350","DOI":"10.1002\/2017GL074071","article-title":"Annual Irrigation Dynamics in the U.S. Northern High Plains Derived from Landsat Satellite Data","volume":"44","author":"Deines","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, T., Deines, J.M., Kendall, A.D., Basso, B., and Hyndman, D.W. (2019). Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data. Remote Sens., 11.","DOI":"10.4211\/hs.3766845be72d45969fca21530a67bb2d"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e1533","DOI":"10.1002\/wat2.1533","article-title":"Machine Learning for Hydrologic Sciences: An Introductory Overview","volume":"8","author":"Xu","year":"2021","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112445","DOI":"10.1016\/j.rse.2021.112445","article-title":"Mapping Annual Irrigation from Landsat Imagery and Environmental Variables across the Conterminous United States","volume":"260","author":"Xie","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107458","DOI":"10.1016\/j.agwat.2022.107458","article-title":"Tracking Spatiotemporal Dynamics of Irrigated Croplands in China from 2000 to 2019 through the Synergy of Remote Sensing, Statistics, and Historical Irrigation Datasets","volume":"263","author":"Zhang","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111400","DOI":"10.1016\/j.rse.2019.111400","article-title":"Mapping Three Decades of Annual Irrigation across the US High Plains Aquifer Using Landsat and Google Earth Engine","volume":"233","author":"Deines","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2020WR028059","DOI":"10.1029\/2020WR028059","article-title":"Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning","volume":"56","author":"Majumdar","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_48","unstructured":"Kansas Geological Survey (2022, June 17). Water Information Management and Analysis System (WIMAS). Available online: https:\/\/geohydro.kgs.ku.edu\/geohydro\/wimas\/query_setup.cfm."},{"key":"ref_49","first-page":"441","article-title":"Groundwater Management in Kansas: A Brief History and Assessment","volume":"15","author":"Peck","year":"2006","journal-title":"Kans. J. Law Public Policy"},{"key":"ref_50","unstructured":"United States Department of Agriculture (2022, June 18). 2021 State Agriculture Overview for Kansas, Available online: https:\/\/www.nass.usda.gov\/Quick_Stats\/Ag_Overview\/stateOverview.php?state=KANSAS."},{"key":"ref_51","unstructured":"Whittemore, D.O., Butler, J.J., and Brownie Wilson, B. (2018). Status of the High Plains Aquifer in Kansas, Kansas Geological Survey."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lanning-Rush, J.L. (2016). Irrigation Water Use in Kansas, 2013.","DOI":"10.3133\/ds981"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s10584-017-1947-7","article-title":"Groundwater Depletion and Climate Change: Future Prospects of Crop Production in the Central High Plains Aquifer","volume":"146","author":"Cotterman","year":"2018","journal-title":"Clim. Chang."},{"key":"ref_54","unstructured":"United States Department of Agriculture (2010). Field Crops Usual Planting and Harvesting Dates 2010."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mancino, G., Ferrara, A., Padula, A., and Nol\u00e8, A. (2020). Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment. Remote Sens., 12.","DOI":"10.3390\/rs12020291"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 Reflective Wavelength and Normalized Difference Vegetation Index Continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Mu, Q., Zhao, M., and Running, S.W. (2013). MODIS Global Terrestrial Evapotranspiration (ET) Product, Algorithm Theoretical Basis Document, Collection."},{"key":"ref_58","unstructured":"Halverson, G.H., Fisher, J.B., and Lee, C.M. (2021, June 22). Level 3 Evapotranspiration Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) Data User Guide, Available online: https:\/\/ecostress.jpl.nasa.gov\/downloads\/atbd\/ECOSTRESS_L3_ET_PT-JPL_ATBD_20180509.pdf."},{"key":"ref_59","unstructured":"Cawse-Nicholson, K., and Anderson, M. (2021, June 22). ECOSTRESS Level-3 DisALEXI-JPL Evapotranspiration (ECO3ETALEXI) Algorithm Theoretical Basis Document, Available online: https:\/\/lpdaac.usgs.gov\/documents\/999\/ECO3ETALEXI_User_Guide_V1.pdf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.rse.2007.06.025","article-title":"Global Estimates of the Land-Atmosphere Water Flux Based on Monthly AVHRR and ISLSCP-II Data, Validated at 16 FLUXNET Sites","volume":"112","author":"Fisher","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112662","DOI":"10.1016\/j.rse.2021.112662","article-title":"An Evaluation of ECOSTRESS Products of a Temperate Montane Humid Forest in a Complex Terrain Environment","volume":"265","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1002\/joc.3413","article-title":"Development of Gridded Surface Meteorological Data for Ecological Applications and Modelling","volume":"33","author":"Abatzoglou","year":"2013","journal-title":"Int. J. Climatol."},{"key":"ref_63","unstructured":"United States Department of Agriculture (2022, June 17). National Agricultural Statistics Service (USDA NASS) Cropland Data Layer, Available online: https:\/\/www.nass.usda.gov\/Research_and_Science\/Cropland\/metadata\/meta.php."},{"key":"ref_64","unstructured":"United States Geological Survey (2022, June 17). LP DAAC\u2014MOD16A2, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod16a2v061\/."},{"key":"ref_65","unstructured":"United States Geological Survey (2022, June 17). LP DAAC\u2014ECO3ETPTJPL, Available online: https:\/\/lpdaac.usgs.gov\/products\/eco3etptjplv001\/."},{"key":"ref_66","unstructured":"United States Geological Survey (2022, June 17). LP DAAC\u2014ECO3ETALEXI, Available online: https:\/\/lpdaac.usgs.gov\/products\/eco3etalexiv001\/."},{"key":"ref_67","unstructured":"United States Geological Survey (2022, June 17). Landsat Data Access|U.S. Geological Survey, Available online: https:\/\/www.usgs.gov\/landsat-missions\/landsat-data-access."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","unstructured":"Louppe, G. (2014). Understanding Random Forests: From Theory to Practice. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1061\/(ASCE)1084-0699(1999)4:2(135)","article-title":"Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration","volume":"4","author":"Gupta","year":"1999","journal-title":"J. Hydrol. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"106736","DOI":"10.1016\/j.agwat.2021.106736","article-title":"Lessons from Local Governance and Collective Action Efforts to Manage Irrigation Withdrawals in Kansas","volume":"247","author":"Hendricks","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.envsci.2021.07.021","article-title":"Sustainable Irrigation through Local Collaborative Governance: Evidence for a Structural Fix in Kansas","volume":"124","author":"Zwickle","year":"2021","journal-title":"Environ. Sci. Policy"},{"key":"ref_74","unstructured":"Kansas Department of Agriculture (2022, June 17). Sheridan County 6 LEMA, Available online: https:\/\/agriculture.ks.gov\/divisions-programs\/dwr\/managing-kansas-water-resources\/local-enhanced-management-areas\/sheridan-county-6-lema."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e2020WR027961","DOI":"10.1029\/2020WR027961","article-title":"Charting Pathways Toward Sustainability for Aquifers Supporting Irrigated Agriculture","volume":"56","author":"Butler","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_76","unstructured":"Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Priol, R.L., and Courville, A. (2021, January 18\u201324). Out-of-Distribution Generalization via Risk Extrapolation (REx). Proceedings of the 38th International Conference on Machine Learning, Virtual."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"112189","DOI":"10.1016\/j.rse.2020.112189","article-title":"Interoperability of ECOSTRESS and Landsat for Mapping Evapotranspiration Time Series at Sub-Field Scales","volume":"252","author":"Anderson","year":"2021","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3004\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:38:42Z","timestamp":1760139522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":77,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133004"],"URL":"https:\/\/doi.org\/10.3390\/rs14133004","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,23]]}}}