{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T03:56:51Z","timestamp":1772942211363,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:00:00Z","timestamp":1631664000000},"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>Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere\u2013atmosphere\u2013hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with in situ lysimeter measurements for different land covers (Grass, Wheat, Barley, and Vineyard) at the Barrax site, Spain, for the period 2014\u20132018. Daily estimates produced superior performance than hourly estimates in all the land covers, with an average difference of 12% and 15% for daily and hourly ET estimates, respectively. Grass and Vineyard showed the best performance, with an RMSE of 0.10 mm\/h and 0.09 mm\/h and 1.11 mm\/day and 0.63 mm\/day, respectively. Thus, the S-SEBI model is able to retrieve ET from Landsat 8 data with an average RMSE for daily ET of 0.86 mm\/day. Some model uncertainties were also analyzed, and we concluded that the overpass of the Landsat missions represents neither the maximum daily ET nor the average daily ET, which contributes to an increase in errors in the estimated ET. However, the S-SEBI model can be used to operationally retrieve ET from agriculture sites with good accuracy and sufficient variation between pixels, thus being a suitable option to be adopted into operational ET remote sensing programs for irrigation scheduling or other purposes.<\/jats:p>","DOI":"10.3390\/rs13183686","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T12:00:44Z","timestamp":1631707244000},"page":"3686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3787-9373","authenticated-orcid":false,"given":"Jos\u00e9 Antonio","family":"Sobrino","sequence":"first","affiliation":[{"name":"Unidad de Cambio Global (UCG), Image Processing Laboratory (IPL), University of Valencia (UVEG), 46071 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6381-1412","authenticated-orcid":false,"given":"N\u00e1jila","family":"Souza da Rocha","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Sensoriamento Remoto (PPGSR), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501970, Brazil"}]},{"given":"Drazen","family":"Skokovi\u0107","sequence":"additional","affiliation":[{"name":"Unidad de Cambio Global (UCG), Image Processing Laboratory (IPL), University of Valencia (UVEG), 46071 Valencia, Spain"}]},{"given":"P\u00e2mela","family":"Su\u00e9len K\u00e4fer","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Sensoriamento Remoto (PPGSR), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1910-5489","authenticated-orcid":false,"given":"Ram\u00f3n","family":"L\u00f3pez-Urrea","sequence":"additional","affiliation":[{"name":"Instituto T\u00e9cnico Agron\u00f3mico Provincial (ITAP), Parque Empresarial Campollano, 2a Avda. N\u00b0 61, 02007 Albacete, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7562-4895","authenticated-orcid":false,"given":"Juan Carlos","family":"Jim\u00e9nez-Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Unidad de Cambio Global (UCG), Image Processing Laboratory (IPL), University of Valencia (UVEG), 46071 Valencia, Spain"}]},{"given":"Silvia Beatriz","family":"Alves Rolim","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Sensoriamento Remoto (PPGSR), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501970, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0022-1694(98)00254-6","article-title":"A remote sensing surface energy balance algorithm for land (SEBAL): Validation","volume":"212\u2013213","author":"Bastiaanssen","year":"1998","journal-title":"J. Hydrol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.5194\/hess-6-85-2002","article-title":"The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes","volume":"6","author":"Su","year":"2002","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rubert, G.C., Roberti, D.R., Pereira, L.S., Quadros, F.L.F., de Campos Velho, H.F., and de Moraes, O.L.L. (2018). Evapotranspiration of the Brazilian Pampa biome: Seasonality and influential factors. Water, 10.","DOI":"10.3390\/w10121864"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.jhydrol.2005.03.027","article-title":"A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX campaigns","volume":"315","author":"Sobrino","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s10795-005-5186-0","article-title":"Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches","volume":"19","author":"Courault","year":"2005","journal-title":"Irrig. Drain. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/S1464-1909(99)00128-8","article-title":"S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance","volume":"25","author":"Roerink","year":"2000","journal-title":"Phys. Chem. Earth, Part B Hydrol. Ocean. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agwat.2020.106145","article-title":"Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm","volume":"237","author":"Mohammadi","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1126\/science.1128845","article-title":"Global Hydrological Cycles and World Water Resources","volume":"313","author":"Oki","year":"2006","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.rse.2019.111594","article-title":"Evolution of evapotranspiration models using thermal and shortwave remote sensing data","volume":"237","author":"Chen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3482","DOI":"10.1016\/j.rse.2008.04.004","article-title":"Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data","volume":"112","author":"Tang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2013.07.001","article-title":"Temporal upscaling of instantaneous evapotranspiration: An intercomparison of four methods using eddy covariance measurements and MODIS data","volume":"138","author":"Tang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"D15107","DOI":"10.1029\/2006JD008351","article-title":"A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature","volume":"112","author":"Wang","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2821","DOI":"10.3390\/en7052821","article-title":"Evapotranspiration estimation with remote sensing and various surface energy balance algorithms\u2014A review","volume":"7","author":"Liou","year":"2014","journal-title":"Energies"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6253832","DOI":"10.1155\/2019\/6253832","article-title":"Based on the Gaussian fitting method to derive daily evapotranspiration from remotely sensed instantaneous evapotranspiration","volume":"2019","author":"Liu","year":"2019","journal-title":"Adv. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1002\/hyp.7748","article-title":"Satellite-based actual evapotranspiration estimation in the middle reach of the Heihe River Basin using the SEBAL method","volume":"24","author":"Li","year":"2010","journal-title":"Hydrol. Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Talsma, C.J., Good, S.P., Miralles, D.G., Fisher, J.B., Martens, B., Jimenez, C., and Purdy, A.J. (2018). Sensitivity of evapotranspiration components in remote sensing-based models. Remote Sens., 10.","DOI":"10.3390\/rs10101601"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.rse.2005.03.006","article-title":"Retrieval of evapotranspiration over the Alpilles\/ReSeDA experimental site using airborne POLDER sensor and a thermal camera","volume":"96","author":"Olioso","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s00704-006-0241-9","article-title":"Regional land surface energy fluxes by satellite remote sensing in the Upper Xilin River Watershed (Inner Mongolia, China)","volume":"88","author":"FAN","year":"2007","journal-title":"Theor. Appl. Climatol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Calera, A., Campos, I., Osann, A., D\u2019Urso, G., and Menenti, M. (2017). Remote sensing for crop water management: From ET modelling to services for the end users. Sensors, 17.","DOI":"10.3390\/s17051104"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.rse.2005.03.004","article-title":"Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products","volume":"96","author":"Verstraeten","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"795","DOI":"10.3390\/rs1040795","article-title":"Mapping latent heat flux in the western forest covered regions of Algeria using remote sensing data and a spatialized model. Remote Sensing","volume":"1","author":"Zahira","year":"2009","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1016\/j.rse.2011.01.013","article-title":"Comparison of two temperature differencing methods to estimate daily evapotranspiration over a Mediterranean vineyard watershed from ASTER data","volume":"115","author":"Galleguillos","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"038504","article-title":"Artificial neural networks model based on remote sensing to retrieve evapotranspiration over the Brazilian Pampa","volume":"14","author":"Diaz","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Allies, A., Demarty, J., Olioso, A., Moussa, I.B., Issoufou, H.B.A., Velluet, C., Bahir, M., Ma\u00efnassara, I., O\u00ef, M., and Chazarin, J.P. (2020). Evapotranspiration estimation in the Sahel using a new ensemble-contextual method. Remote Sens., 12.","DOI":"10.3390\/rs12030380"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Da Rocha, N.S., K\u00e4fer, P.S., Skokovic, D., Veeck, G., Diaz, L.R., Kaiser, E.A., Carvalho, C.M., Cruz, R.C., Sobrino, J.A., and Roberti, D.R. (2020). The Influence of Land Surface Temperature in Evapotranspiration Estimated by the S-SEBI Model. Atmosphere, 11.","DOI":"10.3390\/atmos11101059"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0168-1923(00)00199-4","article-title":"A comparison of methods for determining forest evapotranspiration and its components: Sap-flow, soil water budget, eddy covariance and catchment water balance","volume":"106","author":"Wilson","year":"2001","journal-title":"Agric. For. Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gowda, P.H., Howell, T.A., Paul, G., Colaizzi, P.D., Marek, T.H., Su, B., and Copeland, K.S. (2013). Deriving Hourly Evapotranspiration Rates with SEBS: A Lysimetric Evaluation. Vadose Zo. J., 12.","DOI":"10.2136\/vzj2012.0110"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Moorhead, J.E., Marek, G.W., Colaizzi, P.D., Gowda, P.H., Evett, S.R., Brauer, D.K., Marek, T.H., and Porter, D.O. (2017). Evaluation of sensible heat flux and evapotranspiration estimates using a surface layer scintillometer and a large weighing lysimeter. Sensors, 17.","DOI":"10.3390\/s17102350"},{"key":"ref_29","first-page":"178","article-title":"Evaluating a thermal image sharpening model over a mixed agricultural landscape in India","volume":"13","author":"Jeganathan","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/S0022-1694(98)00253-4","article-title":"The surface energy balance algorithm for land (SEBAL): Part 1 formulation","volume":"212\u2013213","author":"Bastiaanssen","year":"1998","journal-title":"J. Hydrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2007.02.017","article-title":"Application of a simple algorithm to estimate daily evapotranspiration from NOAA-AVHRR images for the Iberian Peninsula","volume":"110","author":"Sobrino","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sobrino, J.A., and Skokovi\u0107, D. (2016). Permanent Stations for Calibration\/Validation of Thermal Sensors over Spain. Data, 1.","DOI":"10.3390\/data1020010"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2016.2633810","article-title":"Vicarious Calibration of the Landsat 7 Thermal Infrared Band and LST Algorithm Validation of the ETM+ Instrument Using Three Global Atmospheric Profiles","volume":"55","author":"Skokovic","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s00271-013-0418-9","article-title":"Consumptive water use and crop coefficients of irrigated sunflower","volume":"32","author":"Montoro","year":"2014","journal-title":"Irrig. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ke, Y., Im, J., Park, S., and Gong, H. (2016). Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sens., 8.","DOI":"10.3390\/rs8030215"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1109\/LGRS.2014.2312032","article-title":"Land surface temperature retrieval methods from landsat-8 thermal infrared sensor data","volume":"11","author":"Sobrino","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1080\/01431169608948760","article-title":"Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data","volume":"17","author":"Sobrino","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TGRS.2007.904834","article-title":"Land surface emissivity retrieval from different VNIR and TIR sensors","volume":"46","author":"Sobrino","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","unstructured":"Hatfield, J.L., and Baker, J.M. (2015). Soil Heat Flux, Agronomy Monographs."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1061\/(ASCE)IR.1943-4774.0000417","article-title":"ET Mapping with High-Resolution Airborne Remote Sensing Data in an Advective Semiarid Environment","volume":"138","author":"Gowda","year":"2012","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_42","first-page":"16","article-title":"A Comparative Study of Evapotranspiration Calculated from Remote Sensing, Meteorological and Lysimeter data","volume":"11","author":"Hassan","year":"2008","journal-title":"3rd Int. Conf. Water Resour. Arid Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.agwat.2006.03.014","article-title":"Testing evapotranspiration equations using lysimeter observations in a semiarid climate","volume":"85","author":"Fabeiro","year":"2006","journal-title":"Agric. Water Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"15046","DOI":"10.3390\/rs71115046","article-title":"SEBAL-A: A remote sensing ET algorithm that accounts for advection with limited data. Part I: Development and validation","volume":"7","author":"Mkhwanazi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1007\/s12524-020-01166-9","article-title":"Evaluation of Simplified Surface Energy Balance Index (S-SEBI) Method for Estimating Actual Evapotranspiration in Kangsabati Reservoir Command Using Landsat 8 Imagery","volume":"48","author":"Kumar","year":"2020","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hashem, A.A., Engel, B.A., Bralts, V.F., Marek, G.W., Moorhead, J.E., Rashad, M., Radwan, S., and Gowda, P.H. (2020). Landsat hourly evapotranspiration flux assessment using lysimeters for the Texas High Plains. Water, 12.","DOI":"10.3390\/w12041192"},{"key":"ref_47","first-page":"129","article-title":"Evaluation of the temporal variability of the evaporative fraction in a tropical watershed","volume":"5","author":"Farah","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Singh, R., and Senay, G. (2015). Comparison of Four Different Energy Balance Models for Estimating Evapotranspiration in the Midwestern United States. Water, 8.","DOI":"10.3390\/w8010009"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.agwat.2013.10.018","article-title":"Estimating high spatiotemporal resolution evapotranspiration over a winter wheat field using an IKONOS image based complementary relationship and Lysimeter observations","volume":"133","author":"Yang","year":"2014","journal-title":"Agric. Water Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/0168-1923(94)02181-I","article-title":"Evaluation of daily evapotranspiration estimates from instantaneous measurements","volume":"74","author":"Zhang","year":"1995","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.5194\/hess-19-2145-2015","article-title":"Actual evapotranspiration and precipitation measured by lysimeters: A comparison with eddy covariance and tipping bucket","volume":"19","author":"Gebler","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.5194\/hess-21-1017-2017","article-title":"Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion","volume":"21","author":"Yang","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0168-1923(02)00015-1","article-title":"Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter","volume":"111","author":"Liu","year":"2002","journal-title":"Agric. For. Meteorol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.asr.2020.04.037","article-title":"Inter-comparison of evapotranspiration datasets over heterogeneous landscapes across Australia","volume":"66","author":"Khan","year":"2020","journal-title":"Adv. Sp. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1016\/j.agwat.2018.11.009","article-title":"Mapping daily and seasonally evapotranspiration using remote sensing techniques over the Nile delta","volume":"213","author":"Elnmer","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Laipelt, L., Ruhoff, A.L., Fleischmann, A.S., Kayser, R.H.B., de Mello Kich, E., da Rocha, H.R., and Neale, C.M.U. (2020). Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest\u2013Savanna Transition in Brazil. Remote Sens., 12.","DOI":"10.3390\/rs12071108"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3686\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:06Z","timestamp":1760166006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":56,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183686"],"URL":"https:\/\/doi.org\/10.3390\/rs13183686","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,15]]}}}