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Temporal upscaling methods for daily evapotranspiration estimation from remotely sensed instantaneous observations

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：tongwang_whu@163.com
  • Introduction：王桐，1993年生，男，硕士研究生，研究方向为地表蒸散发遥感反演。E-mail：tongwang_whu@163.com
WANG Tong12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：tangrl@lreis.ac.cn
  • Introduction：唐荣林，1983年生，男，研究员，研究方向为地表蒸散发和土壤水分的遥感反演。E-mail：tangrl@lreis.ac.cn
TANG Ronglin12*,  
  • Affiliation：

    Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

LI Zhaoliang31,  
  • Affiliation：

    University of Chinese Academy of Sciences, Beijing 100049, China

JIANG Yazhen2,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LIU Meng12,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

TANG Bohui1,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

WU Hua1

résumé

Evapotranspiration (ET), including evaporation from soil surface and vegetation transpiration, is an important component for water and energy balances on the Earth’s surface. The quantification of ET at daily or long time scales is significant in modeling the global hydrological cycle, studying climate change, and managing water resources. However, current remote sensing-based ET models can generally only provide snapshots of ET at the time of a satellite overpass and do not satisfy the expectations of hydrologists, irrigation engineers, and water resources managers concerned with practical applications. In this paper, a comprehensive overview of the methods for estimating daily ET from remotely sensed instantaneous observations is presented. These methods include the constant upscaling factor methods (constant evaporative fraction, constant decoupling factor, radiation-energy derived, constant reference evaporative fraction, and constant surface resistance) and data assimilation method. The commonly used approaches are compared with a discussion regarding the main merits, limitations, and accuracies. The problems and uncertainties of the temporal upscaling of ET, including the evaluation of model applicability, the daily variation of cloud, the spatial interpolation accuracy of meteorological parameters, nighttime ET, the uncertainties from the temporal upscaling methods and ET models, and the approaches of accuracy assessment, are discussed. To improve the accuracy of daily ET estimation from remotely sensed instantaneous observations, several suggestions for future research are proposed as follows: First, research on the continuous surface meteorological data at remote sensing image pixel scale should be enhanced because large-area applications of the temporal upscaling methods are hampered by the lack of appropriate ground-based observations and the spatial heterogeneity causes low accuracy of the spatial interpolation methods of meteorological parameters. Second, the accuracy of ET estimation using remotely sensed data can significantly affect the accuracy of the temporal upscaling of ET. As ET estimation models have not been perfected yet, the methods for the temporal upscaling of ET can be combined with those for ET estimation to reduce the influence of accumulated errors. Third, to weaken the influence of unstable upscaling factor during cloudy days, research on the relation between the constant upscaling factor and cloud (e.g., the appearing time, thickness, and duration of cloud) should be enhanced. Therefore, developing a robust method for the temporal upscaling of ET during cloudy days is vital. Fourth, research on the physical mechanisms of each commonly used method and development of an improved upscaling factor that can be independent of the variation in atmospheric variables and can incorporate the horizontal advection are essential. Fifth, numerous methods for the temporal upscaling of ET can only accurately provide daytime ET, whereas the daily ET is closely concerned with practical applications. Thus, research regarding the temporal upscaling methods should be enhanced in consideration of nighttime ET and its physical mechanisms. Finally, enhancing the research on the new technology and methods of accuracy assessment for ET can weaken the uncertainty of verification.

mots-clés

remote sensing;evapotranspiration;temporal scale;upscaling methods;uncertainty analysis

References

  1. 1.
    Alfieri J G, Anderson M C, Kustas W P and Cammalleri C. 2017. Effect of the revisit interval and temporal upscaling methods on the accuracy of remotely sensed evapotranspiration estimates. Hydrology and Earth System Sciences, 21(1): 83–98
  2. 2.
    Allen R G, Morse A, Tasumi M, Trezza R, Bastiaanssen W, Wright J L and Kramber W. 2002. Evapotranspiration from a satellite-based surface energy balance for the Snake Plain Aquifer in Idaho//Proceedings of the USCID/EWRI Conference on Energy, Climate, Environment and Water Issues and Opportunities for Irrigation and Drainage. SanLuis Obispo, California, USA: USCID: 167-178
  3. 3.
    Allen R G, Pereira L S, Raes D and Smith M. 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO
  4. 4.
    Allen R G, Tasumi M and Trezza R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)–model. Journal of Irrigation and Drainage Engineering, 133(4): 380–394
  5. 5.
    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
  6. 6.
    ASCE-EWRI (Environmental and Water Resources Institute). 2005. The ASCE standardized reference evapotranspiration equation. Bell Drive, Reston, VA: American Society of Civil Engineers
  7. 7.
    Boni G, Entekhabi D and Castelli F. 2001. Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation. Water Resources Research, 37(6): 1713–1722
  8. 8.
    Brutsaert W and Sugita M. 1992. Application of self‐preservation in the diurnal evolution of the surface energy budget to determine daily evaporation. Journal of Geophysical Research: Atmospheres, 97(D17): 18377–18382
  9. 9.
    Cammalleri C, Anderson M C and Kustas W P. 2014. Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications. Hydrology and Earth System Sciences, 18(5): 1885–1894
  10. 10.
    Chávez J L, Neale C M U, Prueger J H and Kustas W P. 2008. Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values. Irrigation Science, 27(1): 67–81
  11. 11.
    Chemin Y and Alexandridis T A. 2001. Improving spatial resolution of ET seasonal for irrigated rice in Zhanghe, China. Asian Journal of Geoinformatics, 5(1): 3–11
  12. 12.
    Chen H, Yang D W and Lü H F. 2013. Comparison of temporal extrapolation methods for evapotranspiration over variant underlying croplands. Transactions of the Chinese Society of Agricultural Engineering, 29(6): 73–81
  13. 13.
    Chen Y H, Li X B, Chen J and Shi P J. 2002. The change of NDVI time series based on change vector analysis in China 1983-1992. Journal of Remote Sensing, 6(1): 12–19
  14. 14.
    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
  15. 15.
    Colaizzi P D, Evett S R, Howell T A and Tolk J A. 2006. Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements. Transactions of the ASABE, 49(5): 1409–1417
  16. 16.
    Crago R D. 1996. Comparison of the evaporative fraction and the Priestley-Taylor α for parameterizing daytime evaporation. Water Resources Research, 32(5): 1403–1409
  17. 17.
    Dawson T E, Burgess S S O, Tu K P, Oliveira R S, Santiago L S, Fisher J B, Simonin K A and Ambrose A R. 2007. Nighttime transpiration in woody plants from contrasting ecosystems. Tree Physiology, 27(4): 561–575
  18. 18.
    Delogu E, Boulet G, Olioso A, Coudert B, Chirouze J, Ceschia E, Le Dantec V, Marloie O, Chehbouni G and Lagouarde J P. 2012. Reconstruction of temporal variations of evapotranspiration using instantaneous estimates at the time of satellite overpass. Hydrology and Earth System Sciences, 16(8): 2995–3010
  19. 19.
    Donohue R J, McVicar T R, Li L T and Roderick M L. 2010a. A data resource for analysing dynamics in Australian ecohydrological conditions. Austral Ecology, 35(5): 593–594
  20. 20.
    Donohue R J, McVicar T R and Roderick M L. 2010b. Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386(1/4): 186–197
  21. 21.
    Eichinger W E, Parlange M B and Stricker H. 1996. On the concept of equilibrium evaporation and the value of the Priestley-Taylor coefficient. Water Resources Research, 32(1): 161–164
  22. 22.
    Farah H O, Bastiaanssen W G M and Feddes R A. 2004. Evaluation of the temporal variability of the evaporative fraction in a tropical watershed. International Journal of Applied Earth Observation and Geoinformation, 5(2): 129–140
  23. 23.
    Feng Y, Cui N B, Zhao L, Hu X T and Gong D Z. 2016. Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. Journal of Hydrology, 536: 376–383
  24. 24.
    Galleguillos M, Jacob F, Prévot L, French A and Lagacherie P. 2011. Comparison of two temperature differencing methods to estimate daily evapotranspiration over a Mediterranean vineyard watershed from ASTER data. Remote Sensing of Environment, 115(6): 1326–1340
  25. 25.
    Gao Y C, Long D and Li Z L. 2008. Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China. International Journal of Remote Sensing, 29(11): 3295–3315
  26. 26.
    Gentine P, Entekhabi D, Chehbouni A, Boulet G and Duchemin B. 2007. Analysis of evaporative fraction diurnal behaviour. Agricultural and Forest Meteorology, 143(1/2): 13–29
  27. 27.
    Gentine P, Entekhabi D and Polcher J. 2011. The diurnal behavior of evaporative fraction in the soil-vegetation-atmospheric boundary layer continuum. Journal of Hydrometeorology, 12(6): 1530–1546
  28. 28.
    Gómez M, Olioso A, Sobrino J A and Jacob F. 2005. Retrieval of evapotranspiration over the Alpilles/ReSeDA experimental site using airborne POLDER sensor and a thermal camera. Remote Sensing of Environment, 96(3/4): 399–408
  29. 29.
    Hoedjes J C B, Chehbouni A, Jacob F, Ezzahar J and Boulet G. 2008. Deriving daily evapotranspiration from remotely sensed instantaneous evaporative fraction over olive orchard in semi-arid Morocco. Journal of Hydrology, 354(1/4): 53–64
  30. 30.
    Huang M F, Xing X F, Yu W Y, Zhang Y M and Shen J L. 2008. Estimation mode of chlorophyII-a concentration on water based on remote sensing technique. Arid Land Geography, 31(1): 62–66
  31. 31.
    Ishak A M, Bray M, Remesan R and Han D W. 2010. Estimating reference evapotranspiration using numerical weather modelling. Hydrological Processes, 24(24): 3490–3509
  32. 32.
    Jackson R D, Hatfield J L, Reginato R J, Idso S B and Pinter Jr P J. 1983. Estimation of daily evapotranspiration from one time-of-day measurements. Agricultural Water Management, 7(1/3): 351–362
  33. 33.
    Jang K, Kang S, Kim J, Lee C B, Kim T, Kim J, Hirata R and Saigusa N. 2010. Mapping evapotranspiration using MODIS and MM5 four-dimensional data assimilation. Remote Sensing of Environment, 114(3): 657–673
  34. 34.
    Jia L, Xi G, Liu S, Huang C, Yan Y and Liu G. 2009. Regional estimation of daily to annual regional evapotranspiration with MODIS data in the Yellow River Delta wetland. Hydrology and Earth System Sciences, 13(10): 1775–1787
  35. 35.
    Kisi O. 2014. Comparison of different empirical methods for estimating daily reference evapotranspiration in mediterranean climate. Journal of Irrigation and Drainage Engineering, 140(1): 49–55
  36. 36.
    Lecina S, Martı́nez-Cob A, Pérez P J, Villalobos F J and Baselga J J. 2003. Fixed versus variable bulk canopy resistance for reference evapotranspiration estimation using the Penman–Monteith equation under semiarid conditions. Agricultural Water Management, 60(3): 181–198
  37. 37.
    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): W01701
  38. 38.
    Lhomme J P and Elguero E. 1999. Examination of evaporative fraction diurnal behaviour using a soil-vegetation model coupled with a mixed-layer model. Hydrology and Earth System Sciences, 3(2): 259–270
  39. 39.
    Li X. 2014. Characterization, controlling, and reduction of uncertainties in the modeling and observation of land-surface systems. Science China Earth Sciences, 57(1): 80–87
  40. 40.
    Li Y, Huang C L, Lu L and Gu J. 2014. Progress in evapotranspiration estimation using remotely sensed data. Journal of Lanzhou University (Natural Sciences), 50(6): 765–772
  41. 41.
    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
  42. 42.
    Liu G S, Hafeez M, Liu Y, Xu D and Vote C. 2012a. A novel method to convert daytime evapotranspiration into daily evapotranspiration based on variable canopy resistance. Journal of Hydrology, 414-415: 278–283
  43. 43.
    Liu G S, Liu Y and Xu D. 2011a. Investigation on performance of evapotranspiration temporal upscaling methods based on eddy covariance measurements. Transactions of the CSAE, 27(6): 7–12
  44. 44.
    Liu G S, Liu Y and Xu D. 2011b. Comparison of evapotranspiration temporal scaling methods based on lysimeter measurements. Journal of Remote Sensing, 15(2): 271–280
  45. 45.
    Liu G S, Xu D and Liu Y. 2012b. Influence of spatial scale variation on ET temporal upscaling methods. Journal of Hydraulic Engineering, 43(8): 999–1003
  46. 46.
    McNaughton K G and Jarvis P G. 1983. Predicting effects of vegetation changes on transpiration and evaporation//Kozlowski T T, ed. Water Deficits and Plant Growth: Volume VII: Additional Woody Crop Plants. New York: Academic Press: 1-47 [DOI: 10.1016/B978-0-12-424157-2.50007-0]
  47. 47.
    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
  48. 48.
    Nichols W E and Cuenca R H. 1993. Evaluation of the evaporative fraction for parameterization of the surface energy balance. Water Resources Research, 29(11): 3681–3690
  49. 49.
    Novick K A, Oren R, Stoy P C, Siqueira M B S and Katul G G. 2009. Nocturnal evapotranspiration in eddy-covariance records from three co-located ecosystems in the Southeastern U. S.: implications for annual fluxes. Agricultural and Forest Meteorology, 149(9): 1491–1504
  50. 50.
    Pereira A R. 2004. The Priestley-Taylor parameter and the decoupling factor for estimating reference evapotranspiration. Agricultural and Forest Meteorology, 125(3/4): 305–313
  51. 51.
    Perez P J, Lecina S, Castellvi F, Martínez-Cob A and Villalobos F J. 2006. A simple parameterization of bulk canopy resistance from climatic variables for estimating hourly evapotranspiration. Hydrological Processes, 20(3): 515–532
  52. 52.
    Pham M T, Vernieuwe H, de Baets B, Willems P and Verhoest N E C. 2016. Stochastic simulation of precipitation-consistent daily reference evapotranspiration using vine copulas. Stochastic Environmental Research and Risk Assessment, 30(8): 2197–2214
  53. 53.
    Pipunic R C, Walker J P and Western A. 2008. Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: a comparative synthetic study. Remote Sensing of Environment, 112(4): 1295–1305
  54. 54.
    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
  55. 55.
    Ruan Z J, Jia L and Menenti M. 2014. Evaluation of algorithms to estimate daily evapotranspiration from instantaneous measurements under all-sky conditions. IOP Conference Series: Earth and Environmental Science, 17(1): 012133
  56. 56.
    Ryu Y, Baldocchi D D, Black T A, Detto M, Law B E, Leuning R, Miyata A, Reichstein M, Vargas R, Ammann C, Beringer J, Flanagan L B, Gu L H, Hutley L B, Kim J, McCaughey H, Moors E J, Rambal S, Vesala T. 2012. On the temporal upscaling of evapotranspiration from instantaneous remote sensing measurements to 8-day mean daily-sums. Agricultural and Forest Meteorology, 152: 212–222
  57. 57.
    Schuepp P H, Leclerc M Y, MacPherson J I and Desjardins R L. 1990. Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation. Boundary-Layer Meteorology, 50(1/4): 355–373
  58. 58.
    Schuurmans J M, Troch P A, Veldhuizen A A, Bastiaanssen W G M and Bierkens M F P. 2003. Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Advances in Water Resources, 26(2): 151–159
  59. 59.
    Shuttleworth W J. 1989. FIFE: the variation in energy partitioning at surface flux sites, remote sensing and large-scale global processes//Proceedings of the Baltimore Symposium. Oxfordshire: IAHS Publication: 67-74
  60. 60.
    Sobrino J A, Gómez M, Jiménez-Muñoz J C and Olioso A. 2007. Application of a simple algorithm to estimate daily evapotranspiration from NOAA–AVHRR images for the Iberian Peninsula. Remote Sensing of Environment, 110(2): 139–148
  61. 61.
    Sugita M and Brutsaert W. 1991. Daily evaporation over a region from lower boundary layer profiles measured with radiosondes. Water Resources Research, 27(5): 747–752
  62. 62.
    Sun Z G, Wang Q X, Matsushita B, Fukushima T, Ouyang Z and Watanabe M. 2009. Development of a simple remote sensing EvapoTranspiration model (Sim-ReSET): algorithm and model test. Journal of Hydrology, 376(3/4): 476–485
  63. 63.
    Tang R L and Li Z L. 2017a. An improved constant evaporative fraction method for estimating daily evapotranspiration from remotely sensed instantaneous observations. Geophysical Research Letters, 44(5): 2319–2326
  64. 64.
    Tang R L and Li Z L. 2017b. Estimating daily evapotranspiration from remotely sensed instantaneous observations with simplified derivations of a theoretical model. Journal of Geophysical Research: Atmospheres, 122(19): 10177–10190
  65. 65.
    Tang R L, Li Z L and Sun X M. 2013. Temporal upscaling of instantaneous evapotranspiration: an intercomparison of four methods using eddy covariance measurements and MODIS data. Remote Sensing of Environment, 138: 102–118
  66. 66.
    Tang R L, Li Z L, Sun X M and Bi Y Y. 2017. Temporal upscaling of instantaneous evapotranspiration on clear-sky days using the constant reference evaporative fraction method with fixed or variable surface resistances at two cropland sites. Journal of Geophysical Research: Atmospheres, 122(2): 784–801
  67. 67.
    Tang R L, Tang B H, Wu H and Li Z L. 2015. On the feasibility of temporally upscaling instantaneous evapotranspiration using weather forecast information. International Journal of Remote Sensing, 36(19/20): 4918–4935
  68. 68.
    Tasumi M, Allen R G, Trezza R and Wright J L. 2005. Satellite-based energy balance to assess within-population variance of crop coefficient curves. Journal of Irrigation and Drainage Engineering, 131(1): 94–109
  69. 69.
    Tian D and Martinez C J. 2012. Forecasting reference evapotranspiration using retrospective forecast analogs in the southeastern United States. Journal of Hydrometeorology, 13(6): 1874–1892
  70. 70.
    Todorovic M. 1999. Single-layer evapotranspiration model with variable canopy resistance. Journal of Irrigation and Drainage Engineering, 125(5): 235–245
  71. 71.
    Tolk J A, Howell T A and Evett S R. 2006. Nighttime evapotranspiration from alfalfa and cotton in a semiarid climate. Agronomy Journal, 98(3): 730–736
  72. 72.
    Trezza R. 2002. Evapotranspiration using a satellite-based surface energy balance with standardized ground control. Logan, UT: Utah State University: 339
  73. 73.
    Twine T E, Kustas W P, Norman J M, Cook D R, Houser P R, Meyers T P, Prueger J H, Starks P J and Wesely M L. 2000. Correcting eddy-covariance flux underestimates over a grassland. Agricultural and Forest Meteorology, 103(3): 279–300
  74. 74.
    van Niel T G, McVicar T R, Roderick M L, van Dijk A I J M, Renzullo L J and van Gorsel E. 2011. Correcting for systematic error in satellite-derived latent heat flux due to assumptions in temporal scaling: assessment from flux tower observations. Journal of Hydrology, 409(1/2): 140–148
  75. 75.
    van Niel T G, McVicar T R, Roderick M L, van Dijk A I J M, Beringer J, Hutley L B and van Gorsel E. 2012. Upscaling latent heat flux for thermal remote sensing studies: comparison of alternative approaches and correction of bias. Journal of Hydrology, 468-469: 35–46
  76. 76.
    Wandera L, Mallick K, Kiely G, Roupsard O, Peichl M and Magliulo V. 2017. Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an Artificial Neural Network approach. Hydrology and Earth System Sciences, 21(1): 197–215
  77. 77.
    Wang K, Tang R L and Li Z L. 2013. Comparison of integrating LAS/MODIS data into a land surface model for improved estimation of surface variables through data assimilation. International Journal of Remote Sensing, 34(9/10): 3193–3207
  78. 78.
    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
  79. 79.
    Xie X Q. 1991. Estimation of daily Evapotranspiration(ET)from one time-of-Day remotely sensed canopy temperature. Journal of Remote Sensing(4): 253–260
  80. 80.
    Xiong J, Wu B F, Yan N N, Hu M G and Sun M Z. 2008. Research on temporal reconstruction of evapotranspiration by using remote sensing. Progress in Geography, 27(2): 53–59
  81. 81.
    Xiong Y J, Qiu G Y, Chen X H, Zhao S H and Tian F. 2012. Estimation of evapotranspiration using three-temperature model based on MODIS data. Journal of Remote Sensing, 16(5): 969–985
  82. 82.
    Xu T R, Liu S M, Liang S L and Qin J. 2011. Improving predictions of water and heat fluxes by assimilating MODIS land surface temperature products into the common land model. Journal of Hydrometeorology, 12(2): 227–244
  83. 83.
    Xu T R, Liu S M, Xu L, Chen Y J, Jia Z Z, Xu Z W and Nielson J. 2015. Temporal upscaling and reconstruction of thermal remotely sensed instantaneous evapotranspiration. Remote Sensing, 7(3): 3400–3425
  84. 84.
    Yang Y M. 2014. A new remotely sensed model to retrieve regional evapotranspiration considering advection effects. Beijing: University of Chinese Academy of Sciences.
  85. 85.
    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
  86. 86.
    Zhang B Z, Chen H, Xu D and Li F S. 2017. Methods to estimate daily evapotranspiration from hourly evapotranspiration. Biosystems Engineering, 53: 129–139
  87. 87.
    Zhang B Z, Xu D, Liu Y and Chen H. 2015. Review of multi-scale evapotranspiration estimation and spatio-temporal scale expansion. Transactions of the Chinese Society of Agricultural Engineering, 31(6): 8–16
  88. 88.
    Zhang L and Lemeur R. 1995. Evaluation of daily evapotranspiration estimates from instantaneous measurements. Agricultural and Forest Meteorology, 74(1/2): 139–154
  89. 89.
    Zhang R H, Du J P and Sun R. 2012. Review of estimation and validation of regional evapotranspiration based on remote sensing. Advances in Earth Science, 27(12): 1295–1307
  90. 90.
    Zhao X S, Liu Y B and Wu G P. 2013. A satellite-based study on spatiotemporal variation in evapotranspiration and its controlling factors over the Poyang lake basin of China during 2000-2009. Resources and Environment in the Yangtze Basin, 22(3): 369–378

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