Skip to main content

Advertisement

SpringerLink
Log in

Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing

  • Published:
Surveys in Geophysics Aims and scope Submit manuscript

Abstract

Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ETa) and meteorological data to project ETa over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ETa and measured ETa are in the range of 0.45–0.95, and root mean square errors are in the range of 10–30% of mean ETa values across biomes, similar to methods that use thermal infrared bands to estimate ETa and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational algorithms for ET in agricultural and natural ecosystems are presented. The goal of the review is to enable potential end-users from different disciplines to adapt these methods to new applications that require spatially-distributed ET estimates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Albrizio R, Steduto P (2003) Photosynthesis, respiration and conservative carbon use efficiency of four field grown crops. Agr Forest Meteorol 116:19–36

    Article  Google Scholar 

  • Alfieri JG, Xiao XM, Niyogi D, Pielke RA, Chen F, LeMone MA (2009) Satellite-based modeling of transpiration from the grasslands in the Southern Great Plains, USA. Global Planet Change 67:78–86

    Article  Google Scholar 

  • Allen R (2005) The need for high-resolution satellite coverage including thermal (surface temperature) for water resources management. University of Idaho, Kimberly, on line document http://www.idwr.idaho.gov/gisdata/ET/Landsat%20issues/the_case_for_a_landsat_thermal_band.pdf (last visited April, 2006)

  • Allen R, Pereira L, Rais D, Smith M (1998) Crop evapotranspiration—guidelines for computing crop water requirements—FAO irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations, Rome

    Google Scholar 

  • Allen RG, Periera LS, Smith M, Raes D, Wright JL (2005) FAO-56 dual crop coefficient method for estimating evaporation from soil and application extentions. J Irrig Drain Eng 131:2–13

    Article  Google Scholar 

  • Allen R, Tasumi M, Trezza R (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. J Irrig Drain Eng 133:380–394

    Article  Google Scholar 

  • Baldocchi D, Falge E, Gu L, Olson R, Hollinger D, Running S, Anthone P, Berhofer C, Davis K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee X, Malhi Y, Meyers T, Munger W, Oechel W, Pilegaard K, Schmid H, Valentini R, Verma S, Vesala T, Wilson K, Wofsy S (2001) Fluxnet: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am Meteor Soci 82:2415–2434

    Article  Google Scholar 

  • Bannari A, Morin D, Bonn F, Huete A (1995) A review of vegetation indices. Int J Rem Sens 13:85–120

    Google Scholar 

  • Barz D, Watson RP, Kanneyh JF, Roberts JD, Groeneveld DP (2009) Cost/benefit considerations for recent saltcedar control, Middle Pecos River, New Mexico. Envir Manag 43:282–298

    Article  Google Scholar 

  • Bastiaanssen WGM, Noordman EJM, Pelgrum H, Davids G, Thoreson BP, Allen RG (2005) SEBAL model with remotely sensed data to improve water-resouces management under actual field conditions. J Irrig Drain 131:85–93

    Article  Google Scholar 

  • Bausch W (1993) Soil background effects on reflectance-based crop coefficients for corn. Remote Sens Environ 46:213–222

    Article  Google Scholar 

  • Bausch W (1995) Remote sensing of crop coefficients for improving the irrigation scheduling of corn. Agric Water Manag 27:55–68

    Article  Google Scholar 

  • Bausch W, Neale C (1987) Crop coefficients derived from reflected canopy radiation: a concept. Trans ASABE 30:703–709

    Google Scholar 

  • Bausch W, Neale C (1989) Spectral inputs improve corn crop coefficients and irrigation scheduling. Trans ASABE 32:901–1908

    Google Scholar 

  • Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36

    Article  Google Scholar 

  • Boegh E, Pooulsen RN, Butts M, Abrahamsen P, Dellwik E, Hansen S, Hasager CB, Ibrom A, Loerup JK, Pilegaard K, Soegaard H (2009) Remote sensing based evapotranspiration and runoff modeling of agricultural, forest and urban flux sites in Denmark: from field to macro-scale. J Hydrol 377:300–316

    Article  Google Scholar 

  • Brown JW, Brown OB, Evans RH (1993) Calibration of advanced very high-resolution radiometer infrared channels—a new approach to nonlinear correction. J Geophys Res Oceans 98:18527–18568

    Article  Google Scholar 

  • Chavez JL, Howell TA, Copeland KS (2009) Evaluating eddy covariance cotton ET measurements in an advective environment with large weighing lysimeters. Irrig Sci 28:35–50

    Article  Google Scholar 

  • Choudhury B, Ahmed N, Idso S, Reginato R, Daughtry C (1994) Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens Environ 50:1–17

    Article  Google Scholar 

  • Cleugh HA, Leuning R, Mu QZ, Running SW (2007) Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sens Envir 106:285–304

    Article  Google Scholar 

  • Courault D, Sequin B, Olioso A (2005) Review on estimation of evapotranspiration from remote sensing data: from empirical to numerical modeling approaches. Irr Drain Syst 19:223–249

    Article  Google Scholar 

  • Cowling SA, Betts RA, Cox PM, Ettwein VJ, Jones CD, Maslin MA, Spall SA (2004) Contrasting simulated past and future responses of the Amazonian forest to atmospheric change. Philos Trans R Soc Lond Ser B 359:539–547

    Article  Google Scholar 

  • Cox PM, Betts RA, Collins M, Harris PP, Huntingford C, Jones CD (2004) Amazonian forest dieback under climate-carbon cycle predictions for the 21st century. Theor Appl Climatol 78:1337–1356

    Article  Google Scholar 

  • Danger M, Daufresne T, Lucas F, Pissard S, Lacroix G (2008) Does Liebig’s Law of the minimum scale up from species to communities? Soikos 117:1741–1751

    Google Scholar 

  • Dennison PE, Nagler PL, Hultine KR, Ehlringer J, Glenn EP (2009) Remote monitoring of tamarisk defoliation and evapotranspiration following saltcedar leaf beetle attack. Rem Sens Environ 113:1462–1472

    Article  Google Scholar 

  • Di Tomaso JM (1998) Impact, biology and ecology of saltcedar (Tamarix spp.) in the southwestern United States. Weed Tech 12:326–336

    Google Scholar 

  • Diak G, Mecikalski J, Anderson M, Norman J, Kustas W, Torn R, DeWolf R (2004) Estimating land surface energy budgets from space—review and current efforts at the University of Madison—Wisconsin and USDA—ARS. Bull Am Meteor Soc 85:65–78

    Article  Google Scholar 

  • El-Shikha DM, Waller P, Hunsaker D, Clarke T, Barnes E (2007) Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agric Water Manag 92:182–193

    Google Scholar 

  • Er-Raki S, Chehbouni A, Duchemin B (2010) Combining satellite remote sensing data with the FAO-56 dual approach for water use mapping in irrigated wheat fields of a semi-arid region. Remote Sens 2:375–387

    Article  Google Scholar 

  • Field C (1991) Ecological scaling of carbon gain to stress and resource availability. In: Mooney H, Winner W, Pell E (eds) Response of plants to multiple stresses. Academic Press, London, pp 35–66

    Google Scholar 

  • Field C, Randerson J, Malmstromk C (1995) Global net primary production: combining ecology and remote sensing. Remote Sens Environ 51:74–88

    Article  Google Scholar 

  • Fisher JB, Tu KP, Baldocchi DD (2008) Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLCP-II data, validated at 16 FLUXNET sites. Remote Sens Environ 112:901–919

    Article  Google Scholar 

  • French AN, Hunsaker DJ, Clarke TR, Fitzgerald GJ, Pinter PJ (2010) Combining remotely sensed data and ground-based radiometers to estimate crop cover and surface temperatures at daily time steps. J Irrig Drainage Eng 136:2332–2339

    Google Scholar 

  • Glenn EP, Huete AR, Nagler PL, Hirschboek K, Brown P (2007) Integrating remote sensing and ground methods to estimate evapotranspiration. Crit Rev Plant Sci 26:139–168

    Article  Google Scholar 

  • Glenn EP, Huete AR, Nagler PL, Nelson SG (2008a) Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8:2136–2160

    Article  Google Scholar 

  • Glenn EP, Morino K, Didan K, Jordan F, Carroll KC, Nagler PL, Hultine K, Sheader L, Waugh J (2008b) Scaling sap flux measurements of grazed and ungrazed shrub communities with fine and coarse-resolution remote sensing. Ecohydrol 1:316–329

    Article  Google Scholar 

  • Gonzalez-Dugo MP, Neale CMU, Mateos L, Kustas WP, Prueger JH, Anderson MC, Li F (2009) A comparison of operational remote sensing-based models for estimating crop evapotranspiration. Agr Forest Meteor 149:1843–1853

    Article  Google Scholar 

  • Groeneveld DP, Baugh WM (2007) Correcting satellite data to detect vegetation signal for eco-hydrologic analyses. J Hydrol 344:135–145

    Article  Google Scholar 

  • Groeneveld DP, Baugh WM, Sanderson JS, Cooper DJ (2007) Annual groundwater evapotranspiration mapped from single satellite scenes. J Hydrol 344:146–156

    Article  Google Scholar 

  • Guerschman JP, Van Dijk AIJM, Mattersdorf G, Beringer J, Hutley LB, Leuning R, Pipunic RC, Sherman BS (2009) Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia. J Hydrol 369:107–119

    Article  Google Scholar 

  • Hall FG, Sellers PJ (1995) First International Satellite Land Surface Climatology Project (ISLSCP) field experiment (FIFE) in 1995. J Geophys Res 100:25,383–25395

    Google Scholar 

  • Hall F, Huemmrich K, Goetz S, Sellers P, Nickeson J (1992) Satellite remote sensing of surface energy balance: success, failures and unresolved issues in FIFE. J Geophys Res 97:19061–19090

    Google Scholar 

  • Harper AB, Denning AS, Baker IT, Branson MD, Prihodko L, Randall DA (2010) Role of deep soil moisture in modulating climate in the Amazon rainforest. Geophys Res Lett 37:Art No L05802

    Article  Google Scholar 

  • Hook SJ, Oaida BV (2010) NASA 2009 HyspIRI science workship report. Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration. http://www.hdl.handle.net/2014/41511 (last visited May 2010)

  • Horton JL, Kolb TE, Hart SC (2001) Responses of riparian trees to interannual variation in ground water depth in a semi-arid river basin. Plant Cell Environ 24:293–304

    Article  Google Scholar 

  • Huete A, Didan K, Miura T, Rodriquez E, Gao X, Ferreira L (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Rem Sens Environ 83:195–213

    Article  Google Scholar 

  • Hunsaker DJ, Barnes EM, Clarke TR, Fitzgerald GJ, Pinter PJ (2005a) Cotton irrigation scheduling using remotely sensed and FAO-56 basal crop coefficients. Trans ASAE 48:1395–1407

    Google Scholar 

  • Hunsaker DJ, Pinter PJ, Kimball BA (2005b) Wheat basal crop coefficients determined by normalized difference vegetation index. Irr Sci 24:1–14

    Article  Google Scholar 

  • Hunsaker DJ, Fitzgerald GJ, French AN, Clarke TR, Ottman MJ, Pinter PJ (2007) Wheat irrigation management using multispectral crop coefficients: I. Crop evapotranspiration prediction. Trans ASABE 50:2017–2033

    Google Scholar 

  • Huxman T, Smith M, Fay P, Knapp S, Shaw M, Loik M, Smith D, Tissue C, Zak J, Weltzin J, Pockman W, Sala O, Haddad B, Harte J, Koch G, Schwinning S, Small E, Williams D (2004) Convergence across biomes to a common rain-use efficiency. Nature 429:651–654

    Article  Google Scholar 

  • Jackson R, Reginato R, Idso S (1977) Wheat canopy temperature: a practical tool for evaluating water requirements. Water Resour Res 13:651–656

    Article  Google Scholar 

  • Jackson RD, Hatfield J, Reginato R, Idso S, Pinter P (1983) Estimation of daily evapo-transpiration from one time-of-day measurements. Agr Water Manag 7:351–362

    Article  Google Scholar 

  • Jayanthi H, Neale CMU, Wright JL (2007) Development and validation of canopy reflectance-based crop coefficients for potato. Agr Water Manag 88:235–246

    Article  Google Scholar 

  • Jensen M (1998) Coefficients for vegetative evapotranspiration and open water evaporation for the Lower Colorado River Accounting System. United States Bureau of Reclamation, Boulder Canyon Operations Office, Boulder City

    Google Scholar 

  • Jiang L, Islam S, Carlson T (2004) Uncertainties in latent heat flux measurement and estimation: implications for using a simplified approach with remote sensing data. Can J Rem Sens 30:769–787

    Google Scholar 

  • Jordan F, Waugh WJ, Glenn EP, Sam L, Thompson T, Thompson TL (2008) Natural bioremediation of a nitrate-contaminated soil-and-aquifer system in a desert environment. J Arid Environ 72:748–763

    Article  Google Scholar 

  • Juarez RIN, Goulden ML, Myneni RB, Fu R, Bernardes S, Gao H (2008) Estimating catchment evpotranspiration and runoff using MODIS leaf area index and the Penman–Monteith equation. Int J Remote Sens 29:7045–7063

    Article  Google Scholar 

  • Kalma JD, McVicar TR, McCabe MF (2008) Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv Geophys 29:421–469

    Article  Google Scholar 

  • Kustas W, Anderson M (2009) Advances in thermal infrared remote sensing for land surface modeling. Ag For Meteor 149:2071–2081

    Article  Google Scholar 

  • Kustas W, Norman J (1996) Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydr Sci J 41:495–516

    Article  Google Scholar 

  • Leuning R, Zhang YQ, Rajaud A, Cleugh H, Tu K (2008) A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation. Water Resour Res 44:Art. No. W10419

    Article  Google Scholar 

  • Li R, Min QL, Lin B (2009) Estimation of evapotranspiration in a mid-latitude forest using the Microwave Emissivity Difference Vegetation Index (EDVI). Rem Sens Environ 113:2011–2018

    Article  Google Scholar 

  • Mahrt L (1998) Flux sampling error for aircrafts and towers. J Atmos Oceanic Technol 15:416–429

    Article  Google Scholar 

  • Malhi Y, Aragao LEOC, Galbraith D, Huntingford C, Fisher R, Zelazowski P, Sitch S, McSweeney C, Meir P (2009) Tipping elements in earth systems special feature: exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc Nat Acad Sci 106:20610–20616

    Article  Google Scholar 

  • Mallick K, Bhattacharya BK, Chaurasia S, Dutta S, Nigam R, Mukherjee J, Banerjee S, Kar G, Rao V, Gadgil A, Parihar JS (2007) Evapotranspiration using MODIS data and limited ground observations over selected agroecosystems in India. Int J Rem Sens 28:2091–2110

    Article  Google Scholar 

  • Mata-Gonzalez R, McLendon T, Martin DW (2005) The inappropriate use of crop transpiration coefficients (Kc) to estimate evapotranspiration in arid ecosystems. Arid Land Res Manag 19:285–295

    Article  Google Scholar 

  • McCabe MF, Wood EF (2006) Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Rem Sens Environ 105:271–295

    Article  Google Scholar 

  • Monteith J, Unsworth M (1990) Principles of environmental physics, 2nd edn. Edward Arnold, London

    Google Scholar 

  • Mu Q, Heinsch FA, Zhao M, Running SW (2007) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Rem Sens Environ 111:519–536

    Article  Google Scholar 

  • Mu QZ, Jones LA, Kimball JS, McDonald KC, Running SW (2009) Satellite assessment of land survace evapotranspiration for the pan-Arctic domain. Water Resour Res 45:Art. No. W09420

    Article  Google Scholar 

  • Murray RS, Nagler PL, Morino K, Glenn EP (2009) An empirical algorithm for estimating agricultural and riparian evapotranspiration using MODIS Enhanced Vegetation Index and ground measurements of ET. II. Application to the Lower Colorado River, U.S. Rem Sens 1:1125–1138

    Article  Google Scholar 

  • Myneni RB, Hoffman S, Knyazikhim Y, Privette JL, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith GR, Lotsch A, Friedl M, Morisette JT, Votava P, Nemani RR, Running SW (2002) Global products of vegetation leaf area index and fraction absorbed PAR from year one of MODIS data. Rem Sens Environ 83:214–231

    Article  Google Scholar 

  • Nagler P, Glenn E, Huete A (2001) Assessment of vegetation indices for riparian vegetation in the Colorado River delta, Mexico. J Arid Environ 49:91–110

    Article  Google Scholar 

  • Nagler P, Glenn E, Thompson T, Huete A (2004) Leaf area index and Normalized Difference Vegetation Index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River. Agr Forest Meteor 116:103–112

    Google Scholar 

  • Nagler P, Cleverly J, Lampkin D, Glenn E, Huete A, Wan Z (2005a) Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data. Rem Sens Environ 94:17–30

    Article  Google Scholar 

  • Nagler P, Scott R, Westenberg C, Cleverly J, Glenn E, Huete A (2005b) Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers. Rem Sens Environ 97:337–351

    Article  Google Scholar 

  • Nagler P, Glenn E, Kim H, Emmerich W, Scott R, Huxman T, Huete A (2007) Seasonal and interannual variation of ET for a semiarid watershed estimated by moisture flux towers and MODIS vegetation indices. J Arid Environ 70:443–463

    Article  Google Scholar 

  • Nagler PL, Glenn EP, Hinojosa-Huerta O (2009a) Synthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, Mexico. Rem Sens Environ 113:1473–1485

    Article  Google Scholar 

  • Nagler PL, Morino K, Murray R, Osterberg J, Glenn EP (2009b) An empirical algorithm for estimating agricultural and riparian evapotranspiration using MODIS Enhanced Vegetation Index and ground ground measurements of ET. I. Descpription of method. Rem Sens 1:1273–1297

    Article  Google Scholar 

  • Nagler PL, Shafroth PB, LaBaugh JW, Snyder KA, Scott RL, Merritt DM, Osterberg J (2010) The potential for water savings through the control of saltcedar and Russian olive. In: PB Shafroth, CA Brown, DM. Merritt (eds.). Saltcedar and Russian olive control demonstration act science assessment. Scientific Investigations Report 2009–5247. : U.S. Geological Survey: Fort Collins, CO pp 35-47, available on line at: http://www.fort.usgs.gov/Products/Publications/pub_abstract.asp?PubId=22899

  • Neale C, Jayanthi H, Wright JL (2005) Irrigation water management using high resolution airborne remote sensing. Irr Drain Syst 19:321–336

    Article  Google Scholar 

  • Nichols W (1993) Estimating discharge of shallow groundwater by transpiration from greasewood in the northern Great Basin. Water Resour Res 29:2771–2778

    Article  Google Scholar 

  • Nishida K, Nemani R, Glassy J, Running S (2003a) Development of an evapotranspiration index from aqua/MODIS for monitoring surface moisture status. IEEE Trans Geosci Rem Sens 41:93–501

    Article  Google Scholar 

  • Nishida K, Nemani R, Running S, Glassy J (2003b) An operational remote sensing algorithm of land surface evaporation. J Geophys Res Atmos 108:Article No. 4270

    Google Scholar 

  • Overgaard J, Rosbjereg D, Butts M (2006) Land-surface modeling in hydrological perspective—a review. Biogeosci 3:229–241

    Article  Google Scholar 

  • Owens MK, Moore GW (2007) Saltcedar water use: Realistic and unrealistic expectations. Range Ecol Manage 60:553–557

    Article  Google Scholar 

  • Pan Y, Birsey R, Hom J, McCullough K, Clark K (2006) Improved estimates of net primary productivity from MODIS satellite data at regional and local scales. Ecol Appl 16:125–132

    Article  Google Scholar 

  • Papadavid GC, Agapiou A, Michaelides S, Hadjimitsis DG (2009) Brief communication, the integration of remote sensing and meteorological data for monitoring irrigation demand in Cyprus. Nat Haz Earth Sys Sci 9:2009–2014

    Article  Google Scholar 

  • Paris Q (1992) The return of van Liebig’s “Law of the Minimum”. Agron J 84:1040–1046

    Article  Google Scholar 

  • Petropoulos G, Carlson TN, Wooster MJ, Islam S (2009) A review of T-s/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Prog Phys Geog 33:224–250

    Article  Google Scholar 

  • Priestley C, Taylor R (1972) On the assessment of surface heat flux and evaporation using large scale parameters. Mon Weather Rev 100:81–92

    Article  Google Scholar 

  • Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H (2004) A continuous satellite derived measure of terrestrial primary production. Biosci 54:547–560

    Article  Google Scholar 

  • Scheffield J, Ferguson CR, Troy TJ, Wood EF, McCabe MF (2009) Closing the terrestrial water budget from satellite remote sensing. Geophy Res Lett 36:L07403

    Article  Google Scholar 

  • Scott RL, Cable WL, Huxman TE, Nagler PL, Hernandez M, Goodrich DC (2008) Multiyear riparian evapotranspiration and groundwater use for a semiarid watershed. J Arid Environ 72:1232–1246

    Article  Google Scholar 

  • Sellers P (1987) Canopy reflectance, photosynthesis, and transpiration, 2. The role of biophysics in the linearity of their interdependence. Rem Sens Environ 21:143–183

    Article  Google Scholar 

  • Sellers P, Berry J, Collatz G, Field C, Hall F (1992) Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Rem Sens Environ 42:187–216

    Article  Google Scholar 

  • Shuttleworth WJ (2007) Putting the “vap” into evaporation. Hydrol Earth Syst Sci 11:210–244

    Article  Google Scholar 

  • Singh RK, Irmak A (2009) Estimation of crop coefficients using satellite remote sensing. J Irrig Drain Eng ASCE 135:597–608

    Article  Google Scholar 

  • Stevens MD, Malthus TJ, Baret F, Xu H, Chopping MJ (2003) Intercalibration of vegetation indices from different sensor systems. Rem Sens Environ 88:412–422

    Article  Google Scholar 

  • Su H, Wood EF, McCabe MF, Su Z (2007) Evaluation of remotely sensed evapotranspiration over the CEOP EOP-1 reference sites. J Meteorol Soc Japan 85A:439–459

    Article  Google Scholar 

  • Tang QH, Gao HL, Lu H, Lettenmaier DP (2009) Remote sensing: hydrology. Prog Phys Geog 33:490–509

    Article  Google Scholar 

  • Tang RL, Li ZL, Tang BH (2010) An application of the Ts-VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implentation and validation. Rem Sens Environ 114:540–551

    Article  Google Scholar 

  • Teuling A, Hirschi M, Ohmura A, Wild M, Reichstein M, Ciasis P, Buchmann N, Ammann C, Montagnani L, Richardson AD, Wohifahrt G, Seneviratne SI (2009) A regional prespective on trends in continental evaporation. Geophy Res Lett 36:Art No. L02404

    Article  Google Scholar 

  • United States Bureau of Reclamation (2009) Lower Colorado River Accounting System. Evapotranspiration and evaporation calculations, calendar year 2008. United States Bureau of Reclamation, Boulder City

    Google Scholar 

  • Verstraeten WW, Veroustraete F, Feyen J (2008) Assessment of evapotranspiration and soil moisture content across different scales of observation. Sensors 8:70–117

    Article  Google Scholar 

  • Wang KC, Liang SL (2008) An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature and soil moisture. J Hydrometeor 9:712–727

    Article  Google Scholar 

  • Wang KC, Wang P, Li ZQ, Cribb M, Sparrow M (2007) A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. J Geophy Res Atmos 112:Art No D151107

    Google Scholar 

  • Williams D, Cable W, Hultine K, Hoedjes H, Yepez E, Simonneaux V, Er-Raki S, Boulet G, de Bruin H, Chehbouni A, Hartogensis O, Timouk F (2004) Evapotranspiration components determined by stable isotope, sap flow and eddy covariance techniques. Agr Forest Meteor 125:241–258

    Article  Google Scholar 

  • Xu D, Shen Y (2005) External and internal factors responsible for midday depression of photosynthesis. In: Pessarakli M (ed) Handbook of photosynthesis, 2nd edn. Taylor & Francis, Boca Raton, pp 297–298

    Google Scholar 

  • Xu CY, Singh VP (2002) Cross comparison of empirical equations for calculating potential evapotranspiration with data from Switzerland. Water Resour Manag 16:197–219

    Article  Google Scholar 

  • Yang F, White M, Michaelis A, Ichii K, Hashimoto H, Votava P, Zhu S, Nemani R (2006) Prediction of continental-scale evapotranspiration by combining MODIS and Ameriflux data through support vector machine. IEEE Trans Geosci Rem Sens 44:3452–3461

    Article  Google Scholar 

  • Zhang YQ, Chew FHS, Zhang L, Li HX (2009a) Use of remotely sensing actual evapotranspiration to improve rainfall-runoff modeling in Southeast Australia. J Hydro Meteor 10:969–980

    Article  Google Scholar 

  • Zhang K, Kimball JS, Mu QZ, Jones LA, Goetz SJ, Running SW (2009b) Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005. J Hydrol 379:92–110

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Environmental Research Laboratory of the University of Arizona, 2601 East Airport Drive, Tucson, AZ, 86706, USA

    Edward P. Glenn

  2. U.S. Geological Survey, Southwest Biological Science Center, Sonoran Desert Research Station, University of Arizona, 1110 E. South Campus Drive, Room 123, Tucson, AZ, 85721, USA

    Pamela L. Nagler

  3. Department of Soil, Water and Environmental Science, University of Arizona, Tucson, AZ, 86721, USA

    Alfredo R. Huete

  4. Plant Functional Biology and Climate Change Cluster, University of Technology, Sydney, NSW, Australia

    Alfredo R. Huete

Authors
  1. Edward P. Glenn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pamela L. Nagler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alfredo R. Huete
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edward P. Glenn.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Glenn, E.P., Nagler, P.L. & Huete, A.R. Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing. Surv Geophys 31, 531–555 (2010). https://doi.org/10.1007/s10712-010-9102-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10712-010-9102-2

Keywords

Search

Navigation