{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T10:50:30Z","timestamp":1768560630405,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T00:00:00Z","timestamp":1719014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"intramural research program of the US Department of Agriculture, National Institute of Food and Agriculture, Agriculture and Food Research Initiative","doi-asserted-by":"publisher","award":["2021-69012-35916"],"award-info":[{"award-number":["2021-69012-35916"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"intramural research program of the US Department of Agriculture, National Institute of Food and Agriculture, Agriculture and Food Research Initiative","doi-asserted-by":"publisher","award":["2017-68007-26318"],"award-info":[{"award-number":["2017-68007-26318"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"NM Water Resources Research Institute, the United States Geological Survey (USGS), Transboundary Aquifer Assessment Program (TAAP) Cooperative Agreement Number G17AC00441; and the United States Department of Agriculture (USDA), National Institute of Food and Agriculture (NIFA)","doi-asserted-by":"publisher","award":["2021-69012-35916"],"award-info":[{"award-number":["2021-69012-35916"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"NM Water Resources Research Institute, the United States Geological Survey (USGS), Transboundary Aquifer Assessment Program (TAAP) Cooperative Agreement Number G17AC00441; and the United States Department of Agriculture (USDA), National Institute of Food and Agriculture (NIFA)","doi-asserted-by":"publisher","award":["2017-68007-26318"],"award-info":[{"award-number":["2017-68007-26318"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Prolonged drought exacerbated by climate change in the Mesilla Valley, one of the major agricultural areas of New Mexico, USA, is causing a shortage of surface water from the Rio Grande for irrigation. Farmers in the Valley are using groundwater for irrigation and complementing it with limited surface water from the river (Rio Grande). Managing irrigation water better is vital to sustaining agriculture in the Valley. Remote sensing (RS)-based crop evapotranspiration (ETa) models offer significant advantages over traditional methods. The ET maps generated by these RS models provide valuable information that can be used to manage irrigation water and crops in water-scarce areas. This study used METRIC and SSEBop RS models to map the ET of alfalfa on a private farm that is managed as commonly practiced in the Valley. The integrated ET values of the two models are compared to those of the ETa measured using the eddy covariance method. The comparison showed that 91.55% of the variability in SSEBop ETa estimates can be explained by the variability in the METRIC ETa estimates, and the variability in eddy covariance ETa can explain 93.07% of the variability in METRIC ETa and 86.01% in the SSEBop Eta estimates. Both METRIC and SSEBop reflected the ETa of alfalfa during full growth and harvesting periods. However, the absolute percent mean relative difference (MRD) of ET was higher for two out of three cuttings by SSEBop (&gt;32%) compared to those for METRIC and eddy covariance. The spatiotemporal variabilities in crop ET estimates using METRIC and SSEBop showed a need to improve on-farm irrigation conveyance and on-the-field irrigation efficiency. Overall, RS models can provide spatiotemporal maps of ET that can be used for decision-making to manage irrigation water better and improve crop yield on a field, farm, and regional scale.<\/jats:p>","DOI":"10.3390\/rs16132290","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T05:16:18Z","timestamp":1719206178000},"page":"2290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Spatiotemporal Variabilities in Evapotranspiration of Alfalfa: A Case Study Using Remote Sensing METRIC and SSEBop Models and Eddy Covariance"],"prefix":"10.3390","volume":"16","author":[{"given":"Zada M.","family":"Tawalbeh","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, New Mexico State University, Las Cruces, NM 88003, USA"}]},{"given":"A. Salim","family":"Bawazir","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, New Mexico State University, Las Cruces, NM 88003, USA"}]},{"given":"Alexander","family":"Fernald","sequence":"additional","affiliation":[{"name":"Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5313-3128","authenticated-orcid":false,"given":"Robert","family":"Sabie","sequence":"additional","affiliation":[{"name":"New Mexico Water Resources Research Institute, New Mexico State University, Las Cruces, NM 88003, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"ref_1","unstructured":"US Department of Agriculture\u2013National Agricultural Statistics Service (NASS) New Mexico Field Office (2022, March 20). 2018 New Mexico Agricultural Statistics, Available online: https:\/\/www.nass.usda.gov\/Statistics_by_State\/New_Mexico\/Publications\/Annual_Statistical_Bulletin\/2018\/2018-NM-Ag-Statistics.pdf."},{"key":"ref_2","unstructured":"Lacefield, G., Ball, D., Hancock, D., Andrae, J., and Smith, R. 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