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Soil evaporation and vegetation transpiration: Remotely sensed estimation and validation

  • role： First author第一作者
  • Affiliation：

    Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：songls@swu.edu.cn
  • Introduction：宋立生(1987—　)，男，副教授，研究方向为多尺度土壤蒸发和植被蒸腾遥感估算与验证等研究。E-mail：songls@swu.edu.cn
Lisheng SONG12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：smliu@bnu.edu.cn
  • Introduction：刘绍民(1967—　)，男，教授，研究方向为水文气象与遥感等研究。E-mail：smliu@bnu.edu.cn
Shaomin LIU2*,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Tongren XU2,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Ziwei XU2,  
  • Affiliation：

    School of Economics and Management, Department of Geography, Handan University, Handan 056005, China

Yanfei MA3

resumen

Land surface evapotranspiration (ET) and its partitioning between evaporation (E) and transpiration (T) is a significant component of water and energy cycles at all scales, from field and watershed to regional and global, and is essential to many applications in climate, weather, hydrology, and ecology. The land surface ET and its components E and T can be produced conveniently at a range of spatial and temporal scales by combining the advanced remotely sensed data and its land surface products such as land surface temperature, leaf area index, and landcover, among others. This work aims to evaluate and summarize available remotely sensed models currently used to determine ET and components E and T. The remotely sensed-based model of land surface E and T has undergone several stages of development, including series and parallel energy balance models, spatial variability model, remote sensing and meteorological combination model, and data assimilation technology divided based on diverse model mechanisms. However, these models provide wide ranges of E and T, whose uncertainty may be limited by the unreasonable component temperatures partitioned from land surface temperature, parameterization of the stress factors of T from vegetation and E from soil surface, and uncertainty of the reproduced meteorological data as model input data. Future studies should improve model performance under heterogeneous surface and upscale the point or patch ground measurements of E and T to satellite pixel scale to validate remotely sensed model simulations.

palabra clave

soil evaporation;vegetation transpiration;remotely sensed model;estimation;validation

References

  1. 1.
    Agam N, Evett S R, Tolk J A, Kustas W P, Colaizzi P D, Alfieri J G, McKee L G, Copeland K S, Howell T A and Chávez JL. 2012. Evaporative loss from irrigated interrows in a highly advective semi-arid agricultural area. Advances in Water Resources, 50: 20–30
  2. 2.
    Agam N, Kustas W P, Anderson M C, Norman J M, Colaizzi P D, Howell T A, Prueger J H, Meyers T P and Wilson T B. 2010. Application of the Priestley–Taylor approach in a two-source surface energy balance model. Journal of Hydrometeorology, 11(1): 185–198
  3. 3.
    Allen R, Irmak A, Trezza R, Hendrickx J M H, Bastiaanssen W and Kjaersgaard J. 2011. Satellite-based ET estimation in agriculture using SEBAL and METRIC. Hydrological Processes, 25(26): 4011–4027
  4. 4.
    Allen R G, Pereira L S, Raes D and Smith M. 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56. Rome: Food and Agriculture Organization of the United Nation
  5. 5.
    Anderson M C, Kustas W P, Norman J M, Hain C R, Mecikalski J R, Schultz L, González-Dugo M P, Cammalleri C, d’Urso G, Pimstein A and Gao F. 2011. Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrology and Earth System Sciences, 15(1): 223–239
  6. 6.
    Anderson M C, Norman J M, Diak G R, Kustas W P and Mecikalski J R. 1997. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sensing of Environment, 60(2): 195–216
  7. 7.
    Anderson M C, Norman J M, Kustas W P, Houborg R, Starks P J and Agam N. 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, 112(12): 4227–4241
  8. 8.
    Anderson M C, Norman J M, Mecikalski J R, Torn R D, Kustas W P and Basara J B. 2004. A multiscale remote sensing model for disaggregating regional fluxes to micrometeorological scales. Journal of Hydrometeorology, 5(2): 343–363
  9. 9.
    Bai J, Jia L, Liu S M, Xu Z W, Hu G C, Zhu M J and Song L S. 2015. Characterizing the footprint of eddy covariance system and large aperture scintillometer measurements to validate satellite-based surface fluxes. IEEE Geoscience and Remote Sensing Letters, 12(5): 943–947
  10. 10.
    Bateni S M and Liang S L. 2012. Estimating surface energy fluxes using a dual-source data assimilation approach adjoined to the heat diffusion equation. Journal of Geophysical Research, 117(D17): D17118
  11. 11.
    Bateni S M, Entekhabi D and Castelli F. 2013a. Mapping evaporation and estimation of surface control of evaporation using remotely sensed land surface temperature from a constellation of satellites. Water Resources Research, 49(2): 950–968
  12. 12.
    Bateni S M, Entekhabi D and Jeng D S. 2013b. Variational assimilation of land surface temperature and the estimation of surface energy balance components. Journal of Hydrology, 481: 143–156
  13. 13.
    Blyth E M and Harding R J. 1995. Application of aggregation models to surface heat flux from the Sahelian tiger bush. Agricultural and Forest Meteorology, 72(3/4): 213–235
  14. 14.
    Boast C W and Robertson T M. 1982. A “Micro-Lysimeter” method for determining evaporation from bare soil: description and laboratory evaluation. Soil Science Society of America Journal, 46(4): 689–696
  15. 15.
    Cammalleri C, Anderson M C, Ciraolo G, D’Urso G, Kustas W P, La Loggia G and Minacapilli M. 2012. Applications of a remote sensing-based two-source energy balance algorithm for mapping surface fluxes without in situ air temperature observations. Remote Sensing of Environment, 124: 502–515
  16. 16.
    Cammalleri C, Anderson M C, Gao F, Hain C R and Kustas W P. 2014. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agricultural and Forest Meteorology, 186: 1–11
  17. 17.
    Caparrini F, Castelli F and Entekhabi D. 2003. Mapping of land-atmosphere heat fluxes and surface parameters with remote sensing data. Boundary-Layer Meteorology, 107(3): 605–633
  18. 18.
    Caparrini F, Castelli F and Entekhabi D. 2004. Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resources Research, 40(12): W12515
  19. 19.
    Carlson T. 2007. An overview of the “Triangle Method” for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors, 7(8): 1612–1629
  20. 20.
    Cleugh H A, Leuning R, Mu Q Z and Running S W. 2007. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, 106(3): 285–304
  21. 21.
    Colaizzi P D, Kustas W P, Anderson M C, Agam N, Tolk J A, Evett S R, Howell T A, Gowda P H and O’Shaughnessy S A. 2012. Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures. Advances in Water Resources, 50: 134–151
  22. 22.
    Dijk A I J M V and Bruijnzeel L A. 2001. Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Journal of Hydrology, 247: 230–238
  23. 23.
    Fisher J B, Tu K P and Baldocchi D D. 2008. Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3): 901–919
  24. 24.
    Flumignan D L, de Faria R T and Prete C E C. 2011. Evapotranspiration components and dual crop coefficients of coffee trees during crop production. Agricultural Water Management, 98(5): 791–800
  25. 25.
    Gao Y C and Long D. 2008. Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on SWAT. Hydrological Processes, 22(25): 4850–4869
  26. 26.
    Gao Y C and Long D. 2008. Progress in models for evapotranspiration estimation using remotely sensed data. Journal of Remote Sensing, 12(3): 515–528 (
  27. 27.
    Gardiol J M, Serio L A and Della Maggiora A I. 2003. Modelling evapotranspiration of corn (Zea mays) under different plant densities. Journal of Hydrology, 271(1/4): 188–196
  28. 28.
    Granier A, Biron P, Köstner B, Gay L W and Najjar G. 1996. Comparisons of xylem sap flow and water vapour flux at the stand level and derivation of canopy conductance for Scots pine. Theoretical and Applied Climatology, 53(1/3): 115–122
  29. 29.
    Hong S H, Hendrickx J M H and Borchers B. 2009. Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30m) to MODIS (250m) scale. Journal of Hydrology, 370(1/4): 122–138
  30. 30.
    Hu G C and Jia L. 2015. Monitoring of evapotranspiration in a semi-arid inland river basin by combining microwave and optical remote sensing observations. Remote Sensing, 7(3): 3056–3087
  31. 31.
    Hu Z M, Wen X F, Sun X M, Li L H, Yu G R, Lee X and Li S G. 2014. Partitioning of evapotranspiration through oxygen isotopic measurements of water pools and fluxes in a temperate grassland. Journal of Geophysical Research, 119(3): 358–372
  32. 32.
    Huang G H, Liu S M and Liang S L. 2012. Estimation of net surface shortwave radiation from MODIS data. International Journal of Remote Sensing, 33(3): 804–825
  33. 33.
    Jasechko S, Sharp Z D, Gibson J J, Birks S J, Yi Y and Fawcett P J. 2013. Terrestrial water fluxes dominated by transpiration. Nature, 496(7445): 347–350
  34. 34.
    Jia Z Z, Liu S M, Xu Z W, Chen Y J and Zhu M J. 2012. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. Journal of Geophysical Research, 117(D13): D13113
  35. 35.
    Jiang L and Islam S. 1999. A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophysical Research Letters, 26(17): 2773–2776
  36. 36.
    Jiang X L, Kang S Z, Li F S, Du T S, Tong L and Comas L. 2016. Evapotranspiration partitioning and variation of sap flow in female and male parents of maize for hybrid seed production in arid region. Agricultural Water Management, 176: 132–141
  37. 37.
    Jung M, Reichstein M, Ciais P, Seneviratne S I, Sheffield J, Goulden M L, Bonan G, Cescatti A, Chen J Q, de Jeu R, Dolman A J, Eugster W, Gerten D, Gianelle D, Gobron N, Heinke J, Kimball J, Law B E, Montagnani L, Mu Q Z, Mueller B, Oleson K, Papale D, Richardson A D, Roupsard O, Running S, Tomelleri E, Viovy N, Weber U, Williams C, Wood E, Zaehle S and Zhang K. 2010. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318): 951–954
  38. 38.
    Kalma J D, McVicar T R and McCabe M F. 2008. Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4/5): 421–469
  39. 39.
    Kool D, Agam N, Lazarovitch N, Heitman J L, Sauer T J and Ben-Gal A. 2014. A review of approaches for evapotranspiration partitioning. Agricultural and Forest Meteorology, 184: 56–70
  40. 40.
    Kustas W and Anderson M. 2009. Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology, 149(12): 2071–2081
  41. 41.
    Kustas W P and Norman J M. 1999. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agricultural and Forest Meteorology, 94(1): 13–29
  42. 42.
    Kustas W P, Norman J M, Anderson M C and French A N. 2003. Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. Remote Sensing of Environment, 85: 429–440
  43. 43.
    Kustas W P, Alfieri J G, Anderson M C, Colaizzi P D, Prueger J H, Evett S R, Neale C M U, French A N, Hipps L E, Chávez J L, Copeland K S and Howell T A. 2012. Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area. Advances in Water Resources, 50: 120–133
  44. 44.
    Leuning R, Zhang Y Q, Rajaud A, Cleugh H and Tu K. 2008. A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation. Water Resources Research, 44(10): W10419
  45. 45.
    Li F Q, Kustas W P, Anderson M C, Prueger J H and Scott R L. 2008. Effect of remote sensing spatial resolution on interpreting tower-based flux observations. Remote Sensing of Environment, 112(2): 337–349
  46. 46.
    Li Z L, Tang R L, Wan Z M, Bi Y Y, Zhou C H, Tang B H, Yan G J and Zhang X Y. 2009. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9(5): 3801–3853
  47. 47.
    Liu S M, Xu Z W, Song L S, Zhao Q Y, Ge Y, Xu T R, Ma Y F, Zhu Z L, Jia Z Z and Zhang F. 2016. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agricultural and Forest Meteorology, 230-231: 97–113
  48. 48.
    Long D and Singh V P. 2012. A two-source trapezoid model for evapotranspiration (TTME) from satellite imagery. Remote Sensing of Environment, 121: 370–388
  49. 49.
    Long D and Singh V P. 2013. Assessing the impact of end-member selection on the accuracy of satellite-based spatial variability models for actual evapotranspiration estimation. Water Resources Research, 49(5): 2601–2618
  50. 50.
    Long D, Singh V P and Li Z L. 2011. How sensitive is SEBAL to changes in input variables, domain size and satellite sensor?. Journal of Geophysical Research, 116(D21): D21107
  51. 51.
    Lund M R and Soegaard H. 2003. Modelling of evaporation in a sparse millet crop using a two-source model including sensible heat advection within the canopy. Journal of Hydrology, 280(1/4): 124–144
  52. 52.
    Michel D, Jiménez C, Miralles D G, Jung M, Hirschi M, Ershadi A, Martens B, McCabe M F, Fisher J B, Mu Q, Seneviratne S I, Wood E F and Fernández-Prieto D. 2016. The WACMOS-ET project–Part 1: tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms. Hydrology and Earth System Sciences, 20(2): 803–822
  53. 53.
    Miralles D G, Holmes T R H, De Jeu R A M, Gash J H, Meesters A G C A and Dolman A J. 2011. Global land-surface evaporation estimated from satellite-based observations. Hydrology and Earth System Sciences, 15(2): 453–469
  54. 54.
    Miralles D G, Jiménez C, Jung M, Michel D, Ershadi A, McCabe M F, Hirschi M, Martens B, Dolman A J, Fisher J B, Mu Q Z, Seneviratne S I, Wood E F and Fernández-Prieto D. 2016. The WACMOS-ET project–Part 2: evaluation of global terrestrial evaporation data sets. Hydrology and Earth System Science, 20(2): 823–842
  55. 55.
    Monteith J L. 1965. Evaporation and environment//Fogg G E, ed. The State and Movement of Water in Living Organisms. Cambridge: Cambridge University Press
  56. 56.
    Morillas L, Leuning R, Villagarcía L, García M, Serrano-Ortiz P and Domingo F. 2013. Improving evapotranspiration estimates in Mediterranean drylands: the role of soil evaporation. Water Resources Research, 49(10): 6572–6586
  57. 57.
    Mu Q Z, Heinsch F A, Zhao M S and Running S W. 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4): 519–536
  58. 58.
    Mu Q Z, Zhao M S and Running S W. 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8): 1781–1800
  59. 59.
    Newman B D, Wilcox B P, Archer S R, Breshears D D, Dahm C N, Duffy C J, McDowell N G, Phillips F M, Scanlon B R, and Vivoni E R. 2006. Ecohydrology of water-limited environments: a scientific vision. Water Resources Research, 42(6): W06302
  60. 60.
    Nishida K, Nemani R R, Running S W and Glassy J M. 2003. An operational remote sensing algorithm of land surface evaporation. Journal of Geophysical Research, 108(D9): 4270
  61. 61.
    Norman J M, Anderson M C, Kustas W P, French A N, Mecikalski J, Torn R, Diak G R, Schmugge T J and Tanner B C W. 2003. Remote sensing of surface energy fluxes at 101-m pixel resolutions. Water Resources Research, 39(8): 1221
  62. 62.
    Norman J M, Kustas W P and Humes K S. 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77(3/4): 263–293
  63. 63.
    Ortega-Farias S, Poblete-Echeverría C and Brisson N. 2010. Parameterization of a two-layer model for estimating vineyard evapotranspiration using meteorological measurements. Agricultural and Forest Meteorology, 150(2): 276–286
  64. 64.
    Paço T A, David T S, Henriques M O, Pereira J S, Valente F, Banza J, Pereira F L, Pinto C and David J S. 2009. Evapotranspiration from a Mediterranean evergreen oak savannah: the role of trees and pasture. Journal of Hydrology, 369(1/2): 98–106
  65. 65.
    Paço T A, Ferreira M I, Rosa R D, Paredes P, Rodrigues G C, Conceição N, Pacheco C A and Pereira L S. 2012. The dual crop coefficient approach using a density factor to simulate the evapotranspiration of a peach orchard: SIMDualKc model versus eddy covariance measurements. Irrigation Science, 30(2): 115–126
  66. 66.
    Phillips O L, Aragão L E O C, Lewis S L, Fisher J B, Lloyd J, López-González G, Malhi Y, Monteagudo A, Peacock J, Quesada C A, van der Heijden G, Almeida S, Amaral I, Arroyo L, Aymard G, Baker T R, Bánki O, Blanc L, Bonal D, Brando P, Chave J, de Oliveira Á C A, Cardozo N D, Czimczik C I, Feldpausch T R, Freitas M A, Gloor E, Higuchi N, Jiménez E, Lloyd G, Meir P, Mendoza C, Morel A, Neill D A, Nepstad D, Patiño S, Peñuela M C, Prieto A, Ramírez F, Schwarz M, Silva J, Silveira M, Thomas A S, ter Steege H, Stropp J, Vásquez R, Zelazowski P, Dávila E A, Andelman S, Andrade A, Chao K J, Erwin T, Di Fiore A, Honorio C E, Keeling H, Killeen T J, Laurance W F, Cruz A P, Pitman N C A, Vargas P N, Ramírez-Angulo H, Rudas A, Salamão R, Silva N, Terborgh J and Torres-Lezama A. 2009. Drought sensitivity of the amazon rainforest. Science, 323(5919): 1344–1347
  67. 67.
    Priestley C H B and Taylor R J. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2): 81–92
  68. 68.
    Rousseaux M C, Figuerola P I, Correa-Tedesco G and Searles P S. 2009. Seasonal variations in sap flow and soil evaporation in an olive (Olea europaea L. ) grove under two irrigation regimes in an arid region of Argentina. Agricultural Water Management, 96(6): 1037–1044
  69. 69.
    Scanlon T M and Kustas W P. 2010. Partitioning carbon dioxide and water vapor fluxes using correlation analysis. Agricultural and Forest Meteorology, 150(1): 89–99
  70. 70.
    Semmens K A, Anderson M C, Kustas W P, Gao F, Alfieri J G, McKee L, Prueger J H, Hain C R, Cammalleri C, Yang Y, Xia T, Sanchez L, Alsina M M and Vélez M. 2016. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sensing of Environment, 185: 155–170
  71. 71.
    Shawcroft R W and Gardner H R. 1983. Direct evaporation from soil under a row crop canopy. Agricultural Meteorology, 28(3): 229–238
  72. 72.
    Shuttleworth W J and Gurney R J. 1990. The theoretical relationship between foliage temperature and canopy resistance in sparse crops. Quarterly Journal of the Royal Meteorological Society, 116(492): 497–519
  73. 73.
    Shuttleworth W J and Wallace J S. 1985. Evaporation from sparse crops-an energy combination theory. Quarterly Journal of the Royal Meteorological Society, 111(469): 839–855
  74. 74.
    Song L S, Kustas W P, Liu S M, Colaizzi P D, Nieto H, Xu Z W, Ma Y F, Li M S, Xu T R, Agam N, Tolk J A and Evett S R. 2016a. Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions. Journal of Hydrology, 540: 574–587
  75. 75.
    Song L S, Liu S M, Kustas W P, Zhou J, Xu Z W, Xia T and Li M S. 2016b. Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures. Agricultural and Forest Meteorology, 230-231: 8–19
  76. 76.
    Song L S, Liu S M, Zhang X, Zhou J and Li M S. 2015. Estimating and validating soil evaporation and crop transpiration during the HiWATER-MUSOEXE. IEEE Geoscience and Remote Sensing Letters, 12(2): 334–338
  77. 77.
    Sun L, Liang S L, Yuan W P and Chen Z X. 2013. Improving a Penman–Monteith evapotranspiration model by incorporating soil moisture control on soil evaporation in semiarid areas. International Journal of Digital Earth, 6(S1): 134–156
  78. 78.
    Tang R L, Li Z L and Tang B H. 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: implementation and validation. Remote Sensing of Environment, 114(3): 540–551
  79. 79.
    Tang R L, Li Z L, Jia Y Y, Li C R, Sun X M, Kustas W P and Anderson M C. 2011. An intercomparison of three remote sensing-based energy balance models using Large Aperture Scintillometer measurements over a wheat–corn production region. Remote Sensing of Environment, 115(12): 3187–3202
  80. 80.
    Trenberth K E, Fasullo J T and Kiehl J. 2009. Earth’s global energy budget. Bulletin of the American Meteorological Society, 90(3): 311–323
  81. 81.
    Vinukollu R K, Meynadier R, Sheffield J and Wood E F. 2011. Multi-model, multi-sensor estimates of global evapotranspiration: climatology, uncertainties and trends. Hydrological Processes, 25(26): 3993–4010
  82. 82.
    Wang K C and Dickinson R E. 2012. A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2): RG2005
  83. 83.
    Wang K C, Li Z Q and Cribb M. 2006. Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: a new method to determine the Priestley–Taylor parameter. Remote Sensing of Environment, 102(3/4): 293–305
  84. 84.
    Wei Z W, Yoshimura K, Okazaki A, Kim W, Liu Z F and Yokoi M. 2015. Partitioning of evapotranspiration using high-frequency water vapor isotopic measurement over a rice paddy field. Water Resources Research, 51(5): 3716–3729
  85. 85.
    Welp L R, Lee X, Kim K, Griffis T J, Billmark K A and Baker J M. 2008. δ18O of water vapour, evapotranspiration and the sites of leaf water evaporation in a soybean canopy. Plant, Cell and Environment, 31(9): 1214–1228
  86. 86.
    Wen X F, Lee X, Sun X M, Wang J L, Hu Z M, Li S G and Yu G R. 2012. Dew water isotopic ratios and their relationships to ecosystem water pools and fluxes in a cropland and a grassland in China. Oecologia, 168(2): 549–561
  87. 87.
    Wen X F, Yang B, Sun X M and Lee X. 2016. Evapotranspiration partitioning through in-situ oxygen isotope measurements in an oasis cropland. Agricultural and Forest Meteorology, 230-231: 89–96
  88. 88.
    Williams D G, Cable W, Hultine K, Hoedjes J C B, Yepez E A, Simonneaux V, Er-Raki S, Boulet G, de Bruin H A R, Chehbouni A, Hartogensis O K and Timouk F. 2004. Evapotranspiration components determined by stable isotope, sap flow and eddy covariance techniques. Agricultural and Forest Meteorology, 125(3/4): 241–258
  89. 89.
    Xia T, Kustas W P, Anderson M C, Alfieri J G, Gao F, McKee L, Prueger J H, Geli H M E, Neale C M U, Sanchez L, Alsina M M and Wang Z J. 2016. Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one- and two-source modeling schemes. Hydrology and Earth System Science, 20(4): 1523–1545
  90. 90.
    Xia Y L, Mitchell K, Ek M, Cosgrove B, Sheffield J, Luo L F, Alonge C, Wei H L, Meng J, Livneh B, Duan Q Y and Lohmann D. 2012a. Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow. Journal of Geophysical Research, 117(D3): D03110
  91. 91.
    Xia Y L, Mitchell K, Ek M, Sheffield J, Cosgrove B, Wood E, Luo L F, Alonge C, Wei H L, Meng J, Livneh B, Lettenmaier D, Koren V, Duan Q Y, Mo K, Fan Y and Mocko D. 2012b. Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. Journal of Geophysical Research, 117(D3): D3
  92. 92.
    Xin X Z and Liu Q H. 2010. The two-layer surface energy balance parameterization scheme (TSEBPS) for estimation of land surface heat fluxes. Hydrology and Earth System Science, 14(3): 491–504
  93. 93.
    Xu T R, Bateni S M, Liang S, Entekhabi D and Mao K B. 2014. Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from geostationary operational environmental satellites. Journal of Geophysical Research, 119(18): 10780–10798
  94. 94.
    Xu T R, Bateni S M, Margulis S A, Song L S, Liu S M and Xu Z W. 2016. Partitioning evapotranspiration into soil evaporation and canopy transpiration via a two-source variational data assimilation system. Journal of Hydrometeorology, 17(9): 2353–2370
  95. 95.
    Xu T R, Liu S M, Qin J and Liang S L. 2009. Estimation of sensible and latent heat flux by assimilating MODIS LST products. Journal of Remote Sensing, 13(6): 989–1009 (
  96. 96.
    Xu T R, Liu S M, Xu Z W, Liang S L and Xu L. 2015. A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature. Science China Earth Sciences, 58(2): 211–230
  97. 97.
    Yang K, Watanabe T, Koike T, Li X, Fujii H, Tamagawa K, Ma Y M and Ishikawa H. 2007. Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget. Journal of the Meteorological Society of Japan Seris II, 85A: 229–242
  98. 98.
    Yang Y M, Su H B, Zhang R H, Tian J and Li L. 2015. An enhanced two-source evapotranspiration model for land (ETEML): Algorithm and evaluation. Remote Sensing of Environment, 168: 54–65
  99. 99.
    Yang Y T, Long D, Guan H D, Liang W, Simmons C and Batelaan O. 2015. Comparison of three dual-source remote sensing evapotranspiration models during the MUSOEXE-12 campaign: revisit of model physics. Water Resources Research, 51(5): 3145–3165
  100. 100.
    Yao Y J, Liang S L, Cheng J, Liu S M, Fisher J B, Zhang X D, Jia K, Zhao X, Qin Q M, Zhao B, Han S J, Zhou G S, Zhou G Y, Li Y L and Zhao S H. 2013. MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley–Taylor algorithm. Agricultural and Forest Meteorology, 171-172: 187–202
  101. 101.
    Yuan W P, Liu S G, Yu G R, Bonnefond J M, Chen J Q, Davis K, Desai A R, Goldstein A H, Gianelle D, Rossi F, Suyker A E and Verma S B. 2010. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment, 114(7): 1416–1431
  102. 102.
    Zhang K, Kimball J S and Running S W. 2016. A review of remote sensing based actual evapotranspiration estimation. Wiley Interdisciplinary Reviews: Water, 3(6): 834–853
  103. 103.
    Zhang R H, Tian J, Li Z L, Su H B, Chen S H and Tang X Z. 2010. Principles and methods for the validation of quantitative remote sensing products. Science China Earth Sciences, 53(5): 741–751
  104. 104.
    Zhang R H. 2009. Quantitative Thermal Infrared Remote Sensing Model and Ground Experimental Foundation. Beijing: Science Publishing Press: 382–417
  105. 105.
    Zhang Y Q, Kang S Z, Ward E J, Ding R S, Zhang X and Zheng R. 2011. Evapotranspiration components determined by sap flow and microlysimetry techniques of a vineyard in northwest China: dynamics and influential factors. Agricultural Water Management, 98(8): 1207–1214
  106. 106.
    Zhou S, Yu B F, Huang Y F and Wang G Q. 2014. The effect of vapor pressure deficit on water use efficiency at the subdaily time scale. Geophysical Research Letters, 41(14): 5005–5013
  107. 107.
    Zhou S, Yu B F, Zhang Y, Huang Y F and Wang G Q. 2016. Partitioning evapotranspiration based on the concept of underlying water use efficiency. Water Resources Research, 52(2): 1160–1175

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