Validation and application of the modified satellite-based Priestley-Taylor algorithm for mapping terrestrial evapotranspiration

Remote Sensing

Volumn 6, Issue 1, 2013, Pages 880-904

  Yao, Yunjun ^a   Liang, Shunlin ^a,b   Zhao, Shaohua ^c   Zhang, Yuhu ^d   Qin, Qiming ^e   Cheng, Jie ^a   Jia, Kun ^a   Xie, Xianhong ^a   Zhang, Nannan ^a,f   Liu, Meng ^a,g  

^a BEIJING NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF MARYLAND   (United States)

^c NANJING INSTITUTE OF ENVIRONMENTAL SCIENCES   (China)

^d CAPITAL NORMAL UNIVERSITY   (China)

^e PEKING UNIVERSITY   (China)

^f HOHAI UNIVERSITY   (China)

^g CHENGDU UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Apparent thermal inertia; Modified satellite based priestley Taylor algorithm; Pt jpl algorithm; Terrestrial evapotranspiration; Vegetation index

Indexed keywords

CORRELATION COEFFICIENT; GLOBAL LAND SURFACE; INTERANNUAL VARIABILITY; PRIESTLEY-TAYLOR; ROOT MEAN SQUARE ERRORS; TERRESTRIAL EVAPOTRANSPIRATION; THERMAL INERTIA; VEGETATION INDEX;

DROUGHT; EVAPOTRANSPIRATION; FORESTRY; MEAN SQUARE ERROR; SATELLITES; SOIL MOISTURE; VEGETATION;

ALGORITHMS;

ALGORITHMS; DROUGHT; EVAPORATION; FORESTRY; PLANTS; TRANSPIRATION; CORRELATION; TEMPERATURE;

EID: 84893425499     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs6010880     Document Type: Article

References (73)

1. Trenberth, K.E.; Smith, L.; Qian, T.; Dai, A.; Fasullo, J. Estimates of the global water budget and its annual cycle using observational and model data. J. Hydrometeorol. 2007, 8, 758-769.
2. Wang, K.; Wang, P.; Li, Z.Q.; Cribb, M.; Sparrow, M. A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. J. Geophys. Res. 2007, doi:10.1029/2006JD00 8351.
3. Li, Z.L.; Tang, R.L.; Wan, Z.M.; Bi, Y.Y.; Zhou, C.H.; Tang, B.H.; Yan, G.J.; Zhang, X.Y. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors 2009, 9, 3801-3853.
4. Liang, S.; Wang, K.; Zhang, X.; Wild, M. Review on estimation of land surface radiation and energy budgets from ground measurements, remote sensing and model simulations. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2010, 3, 225-240.
5. Wang, K.; Dickinson, R. A review of global terrestrial evapotranspiration: Observation, modeling, climatology and climatic variability. Rev. Geophys. 2012, doi:10.1029/2011RG000373.
6. Stisen, S.; Sandholt, I.; Nørgaard, A.; Fensholt, R.; Jensen, K.H. Combining the triangle method with thermal inertia to estimate regional evapotranspiration-Applied to MSGSEVIRI data in the Senegal River basin. Remote Sens. Environ. 2008, 112, 1242-1255.
7. Zhang, K.; Kimball, J.S.; Mu, Q.; Jones, L.A.; Goetz, S.J.; Running, S.W. Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005. J. Hydrol. 2009, 379, 92-110.
8. Zhang, K.; Kimball, J.S.; Nemani, R.R.; Running, S.W. A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resour. Res. 2010, doi:10.1029/2009WR008800.
9. Yuan, W.; Liu, S.; Yu, G.; Bonnefond, J.M.; Chen, J.; Davis, K.; Desai, A.R.; Goldstein, A.H.; Gianelle, D.; Rossi, F.; et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens. Environ. 2010, 114, 1416-1431.
10. Yao, Y.; Qin, Q.; Ghulam, A.; Liu, S.; Zhao, S.; Xu, Z.; Dong, H. Simple method to determine the Priestley-Taylor parameter for evapotranspiration estimation using Albedo-VI triangular space from MODIS data. J. Appl. Remote Sens. 2011, doi:10.1117/1.3557817.
11. Norman, J.M.; Kustas, W.P.; Humes, K.S. A two-source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric. For. Meteorol. 1995, 77, 263-293.
12. Kustas, W.P.; Norman, J.M. Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrol. Sci. J. 1996, 41, 495-516.
13. Anderson, M.C.; Norman, J.M.; Diak, G.R.; Kustas, W.P.; Mecikalski, J.R. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ. 1997, 60, 195-116.
14. Cleugh, H.A.; Leuning, R.; Mu, Q.; Running, S.W. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sens. Environ. 2007, 106, 285-304.
15. Mu, Q.; Heinsch, F.A.; Zhao, M.; Running, S.W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 2007, 111, 519-536.
16. Mu, Q.; Zhao, M.; Running, S.W. Improvements to a MODIS global terrestrial evapotrans piration algorithm. Remote Sens. Environ. 2011, 115, 1781-1800.
17. Fisher, J.; Tu, K.; Baldocchi, D. Global estimates of the land atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ. 2008, 112, 901-919.
18. Wang, K.; Liang, S. An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture. J. Hydrometeorol. 2008, 9, 712-727.
19. Wang, K.; Dickinson, R.; Wild, M.; Liang, S. Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002. Part 1: Model development. J. Geophys. Res. 2010, doi:10.1029/2009JD013671.
20. Wang, K.; Dickinson, R.; Wild, M.; Liang, S. Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002. Part 2: Results. J. Geophys. Res. 2010, doi:10.1029/2010JD013847.
21. Yao, Y.; Liang, S.; Cheng, J.; Liu, S.; Fisher, J.; Zhang, X.; Jia, K.; Zhao, X.; Qin, Q.; Zhao, B.; et al. MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm. Agric. For. Meteorol. 2013, 171-172, 187-202.
22. Kalma, J.; McVicar, T.; McCabe, M. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data accomplished. Surv. Geophys. 2008, 29, 421-469.
23. Jackson, R.; Reginato, R.; Idso, S. Wheat canopy temperature: A practical tool for evaluating water requirements. Water Resour. Res. 1977, 13, 651-656.
24. Jung, M.; Reichstein, M.; Ciais, P.; Seneviratne, S.; Sheffield, J.; Goulden, M.; Bonan, G.; Cescatti, A.; Chen, J.; Richard, D.; et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 2010, 467, 951-954.
25. Boegh, E.; Soegaard, H.; Hanan, N.; Kabat, P.; Lesch, L. A remote sensing study of the NDVI-Ts relationship and the transpiration from sparse vegetation in the Sahel based on high-resolution satellite data. Remote Sens. Environ. 1999, 69, 224-240.
26. Shuttleworth, W.J.; Wallace, J.S. Evaporation from sparse crops an energy combination theory. Quart. J. R. Meteorol. Soc. 1985, 111, 839-855.
27. Monteith, J. Evaporation and environment. Symp. Soc. Exp. Biol. 1965, 19, 205-224.
28. Boni, G.; Entekhabi, D.; Castelli, F. Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation. Water Resour. Res. 2001, 37, 1713-1722.
29. Qin, J.; Liang, S.; Liu, R.; Zhang, H.; Hu, B. A weak-constraint based data assimilation scheme for estimating surface turbulent fluxes. IEEE Geosci. Remote Sens. 2007, 4, 649-653.
30. Pipunic, R.; Walker, J.; Western, A. Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study. Remote Sens. Environ. 2008, 112, 1295-1305.
31. Jiang, L.; Islam, S. Estimation of surface evaporation map over Southern Great Plains using remote sensing data. Water Resour. Res. 2001, 37, 329-340.
32. Yao, Y.; Liang, S.; Qin, Q.; Wang, K.; Zhao, S. Monitoring global land surface drought based on a hybrid evapotranspiration model. Int. J. Appl. Earth Observ. 2011, 13, 447-457.
33. Priestley, C.H.B.; Taylor, R.J. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weath. Rev. 1972, 100, 81-92.
34. Jin, Y.; Randerson, J.; Goulden, M. Continental-scale net radiation and evapotranspiration estimated using MODIS satellite observations. Remote Sens. Environ. 2011, 115, 2302-2319.
35. Miralles, D.G.; Holmes, T.R.H.; de Jeu1, R.A.M.; Gash, G.H.; Meesters, A.G.C.A.; Dolman, A.J. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 2011, 15, 453-469.
36. Mueller, B.; Seneviratne, S.I.; Jimenez, C.; Corti, T.; Hirschi, M.; Balsamo, G.; Ciais, P.; Dirmeyer, P.; Fisher, J.; Guo, Z.; et al. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophys. Res. Lett. 2011, doi:10.1029/2010GL046230.
37. Jiang, L.; Islam, S.; Guo, W.; Jutla, A.S.; Senarath, S.U.S.; Ramsay, B.H.; Eltahir, E. A satellite-based daily actual evapotranspiration estimation algorithm over South Florida. Glob. Planet. Chang. 2009, 67, 62-77.
38. Nemani, R.R.; Keeling, C.D.; Hashimoto, H.; Jolly, W.M.; Piper, S.C.; Tucker, C.J.; Myneni, R.B.; Running, S.W. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 2003, 300, 1560-1563.
39. Jiang, L.; Islam, S. A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophys. Res. Lett. 1999, 26, 2773-2776.
40. Wang, K.; Li, Z.; Cribb, M. 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 Sens. Environ. 2006, 102, 293-305.
41. Carlson, T. An overview of the ?Triangle Method? for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors 2007, 7, 1612-1629.
42. Tang, R.; Li, Z.; Tang, B. 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 Sens. Environ. 2010, 114, 540-551.
43. Anderson, M.C.; Kustas, W.P.; Norman, J.M.; Hain, C.R.; Mecikalski, J.R.; Schultz, Z.; González-Dugo, M.P.; Cammalleri, C.; D'Urso, G.; Pimstein, A.; et al. Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol. Earth Syst. Sci. 2011, 15, 223-239.
44. Yebra, M.; Dijk, A.V.; Leuning, R.; Huete, A.; Guerschman, J.P. Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance. Remote Sens. Environ. 2013, 129, 250-261.
45. Anderson, M.C.; Norman, J.M.; Mecikalski, J.R.; Otkin, J.A.; Kustas, W.P. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. J. Geophys. Res. 2007, doi:10.1029/2006JD007506.
46. Anderson, M.C.; Norman, J.M.; Mecikalski, J.R.; Otkin, J.A.; Kustas, W.P. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. J. Geophys. Res. 2007, doi:10.1029/2006JD007507.
47. Yao, Y.; Liang, S.; Qin, Q.; Wang, K. Monitoring drought over the conterminous united states using MODIS and NCEP reanalysis-2 data. J. Meteor. Appl. Climatol. 2010, 49, 1665-1680.
48. Hargreaves, G.H. Defining and using reference evapotranspiration. J. Irrig. Drain. Eng. 1994, 120, 1132-1139.
49. Pinker, R.T.; Zhang, B.; Dutton, E.G. Do satellites detect trends in surface solar radiation? Science 2005, 308, 850-854.
50. Twine, T.E.; Kustas, W.P.; Norman, J.M.; Cook, D.R.; Houser, P.R.; Meyers, T.P.; Prueger, J.H.; Starks, P.J.; Wesely, M.L. Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteorol. 2000, 103, 279-300.
51. Tucker, C.J.; Pinzon, J.E.; Brown, M.E.; Slayback, D.A.; Pak, E.W.; Mahoney, R.; Vermote, E.F.; Saleous, N. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 2005, 26, 4485-4498.
52. Wu, J.B.; Xiao, X.M.; Guan, D.X.; Shi, T.T.; Jin, C.J.; Han, S.J. Estimation of the gross primary production of an old-growth temperate mixed forest using eddy covariance and remote sensing. Int. J. Remote Sens. 2009, 30, 463-479.
53. Wang, H.S.; Jia, G.S.; Fu, C.B.; Feng, J.M.; Zhao, T.B.; Ma, Z.G. Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ. 2010, 114, 2248-2258.
54. Glenn, E.P.; Huete, A.R.; Nagler, P.L.; Nelson, S.G. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape. Sensors 2008, 8, 2136-2160.
55. Zeng, X.B.; Dickinson, R.E.; Walker, A.; Shaikh, M.; DeFries, R.S.; Qi, J.G. Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. J. Appl. Meteorol. 2000, 39, 826-839.
56. Tucker, C.J. Red and photographic infrared linear combinations for monitoring. Remote Sens. Environ. 1979, 8, 127-150.
57. Li, R.; Min, Q.L.; Lin, B. Estimation of evapotranspiration in a mid-latitude forest using the microwave emissivity difference vegetation index (EDVI). Remote Sens. Environ. 2009, 113, 2011-2018.
58. Jang, K.; Kang, S.; Kim, J.; Lee, C.B.; Kim, T.; Kim, J.; Hirata, R.; Saigusa, N. Mapping evapotranspiration using MODIS and MM5 four-dimensional data assimilation. Remote Sens. Environ. 2010, 114, 657-673.
59. Hwang, K.; Choi, M. Seasonal trends of satellite-based evapotranspiration algorithms over a complex ecosystem in East Asia. Remote Sens. Environ. 2013, 137, 244-263.
60. Salvucci, G.D. Soil and moisture independent estimation of stage-two evaporation from potential evaporation and albedo or surface temperature. Water Resour. Res. 1997, 33, 111-122.
61. Gu, L.; Meyers, T.; Pallardy, S.G.; Hanson, P.J.; Yang, B.; Heuer, M.; Hosman, K.P.; Riggs, J.S.; Sluss, D.; Wullschleger, S.D. Direct and indirect effects of atmospheric conditions and soil moisture on surface energy partitioning revealed by a prolonged drought at a temperate forest site. J. Geophys. Res. 2006, doi:10.1029/2006JD007161.
62. Wu, Y.; Zeng, Y.; Wu, B. Retrieval and analysis of vegetation cover in the Three-North Regions of China based on MODIS data. Chin. J. Ecol. 2009, 28, 1712-1718.
63. Duan, H.; Yan, C.; Tsunekawa, A.; Song, X.; Li, S.; Xie, J. Assessing vegetation dynamics in the Three-North Shelter Forest region of China using AVHRR NDVI data. Environ. Earth Sci. 2011, 64, 1011-1020.
64. Li, S.; Zhai, H. The comparison study on forestry ecological projects in the world (In Chinese). Acta. Ecol. Sin. 2002, 22, 1976-1982.
65. Yao, Y.; Liang, S.; Qin, Q.; Wang, K.; Liu, S.; Zhao, S. Satellite detection of increases in global land surface evapotranspiration during 1984-2007. Int. J. Digit. Earth 2012, 5, 299-318.
66. Lu, E.; Luo, Y.L.; Zhang, R.H.; Wu, Q.X.; Liu, L.P. Regional atmospheric anomalies responsible for the 2009-2010 severe drought in China. J. Geophys. Res. 2011, doi:10.1029/2011JD015706.
67. Myneni, R.B.; Yang, W.; Nemani, R.R.; Huete, A.R.; Dickinson, R.E.; Knyazikhin, Y.; Didan, K.; Fu, R.; Negron Juarez, R.I.; Saatchi, S.S.; et al. Large seasonal swings in leaf area of Amazon rainforests. Proc. Natl. Acad. Sci. USA 2007, 104, 4820-4823.
68. Sasai, T.; Saigusa, N.; Nasahara, K.N.; Ito, A.; Hashimoto, H.; Nemani, R.; Hirata, R.; Ichii, K.; Takagi, K.; Saitoh, T.M.; et al. Satellite-driven estimation of terrestrial carbon flux over Far East Asia with 1-km grid resolution. Remote Sens. Environ. 2011, 115, 1758-1771.
69. Zhao, M.; Running, S.W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 2010, 329, 940-943.
70. Dai, A.G.; Trenberth, K.E.; Qian, T. A global data set of Palmer Drought Severity Index for 1870-2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeorol. 2004, 5, 1117-1130.
71. Robeson, S.M. Applied climatology: Drought. Progr. Phys. Geogr. 2008, 32, 303-309.
72. Sandholt, I.; Rasmussen, K.; Andersen, J. A simple interpretation of the surface temperature-vegetation index space for assessment of surface moisture status. Remote Sens. Environ. 2002, 79, 213-224.
73. Mu, Q.; Zhao, M.; Kimball, J.S.; McDowell, N.G.; Running, S.W. A remotely sensed global terrestrial drought severity index. Bull. Am. Meteor. Soc. 2013, 94, 83-97.