{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T16:02:37Z","timestamp":1770220957941,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2013,3,26]],"date-time":"2013-03-26T00:00:00Z","timestamp":1364256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop coefficient (Kc)-based estimation of crop  evapotranspiration is one of the most commonly used methods for  irrigation water management. However, uncertainties of the generalized  dual crop coefficient (Kc) method of the Food and  Agricultural Organization of the United Nations Irrigation and Drainage  Paper No. 56 can contribute to crop evapotranspiration estimates that  are substantially different from actual crop evapotranspiration.  Similarities between the crop coefficient curve and a satellite-derived  vegetation index showed potential for modeling a crop coefficient as a  function of the vegetation index. Therefore, the possibility of directly  estimating the crop coefficient from satellite reflectance of a crop  was investigated. The Kc data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop  coefficients procedure using field data obtained during 2007 from  representative US cropping systems in the High Plains from AmeriFlux  sites. A simple linear regression model (                                                   ) is developed to establish a general relationship  between a normalized difference vegetation index (NDVI) from a moderate resolution satellite data (MODIS) and the crop coefficient (Kc)  calculated from the flux data measured for different crops and cropping  practices using AmeriFlux towers. There was a strong linear correlation  between the NDVI-estimated Kc and the measured Kc with an r2 of 0.91 and 0.90, while the root-mean-square error (RMSE) for Kc in 2006 and 2007 were 0.16 and 0.19, respectively. The procedure for quantifying crop coefficients from NDVI data presented in this paper should be useful in other regions of the  globe to understand regional irrigation water consumption.<\/jats:p>","DOI":"10.3390\/rs5041588","type":"journal-article","created":{"date-parts":[[2013,3,26]],"date-time":"2013-03-26T13:39:39Z","timestamp":1364305179000},"page":"1588-1602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":208,"title":["Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index"],"prefix":"10.3390","volume":"5","author":[{"given":"Baburao","family":"Kamble","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, 3310 Holdrege Street, University of Nebraska-Lincoln,  Lincoln, NE 68583, USA"}]},{"given":"Ayse","family":"Kilic","sequence":"additional","affiliation":[{"name":"School of Natural Resources, 3310 Holdrege Street, University of Nebraska-Lincoln,  Lincoln, NE 68583, USA"}]},{"given":"Kenneth","family":"Hubbard","sequence":"additional","affiliation":[{"name":"School of Natural Resources, 3310 Holdrege Street, University of Nebraska-Lincoln,  Lincoln, NE 68583, USA"}]}],"member":"1968","published-online":{"date-parts":[[2013,3,26]]},"reference":[{"key":"ref_1","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M (1998). 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