Advertisement

Advances in Atmospheric Sciences

, Volume 30, Issue 5, pp 1479–1490 | Cite as

Regional estimates of evapotranspiration over Northern China using a remote-sensing-based triangle interpolation method

  • Hesong Wang (王鹤松)
  • Gensuo Jia (贾根锁)
Article

Abstract

Regional estimates of evapotranspiration (ET) are critical for a wide range of applications. Satellite remote sensing is a promising tool for obtaining reasonable ET spatial distribution data. However, there are at least two major problems that exist in the regional estimation of ET from remote sensing data. One is the conflicting requirements of simple data over a wide region, and accuracy of those data. The second is the lack of regional ET products that cover the entire region of northern China. In this study, we first retrieved the evaporative fraction (EF) by interpolating from the difference of day/night land surface temperature (ΔT) and the normalized difference vegetation index (NDVI) triangular-shaped scatter space. Then, ET was generated from EF and land surface meteorological data. The estimated eight-day EF and ET results were validated with 14 eddy covariance (EC) flux measurements in the growing season (July-September) for the year 2008 over the study area. The estimated values agreed well with flux tower measurements, and this agreement was highly statistically significant for both EF and ET (p < 0.01), with the correlation coefficient for EF (R 2=0.64) being relatively higher than for ET (R 2=0.57). Validation with EC-measured ET showed the mean RMSE and bias were 0.78 mm d−1 (22.03 W m−2) and 0.31 mm d−1 (8.86 W m−2), respectively. The ET over the study area increased along a clear longitudinal gradient, which was probably controlled by the gradient of precipitation, green vegetation fractions, and the intensity of human activities. The satellite-based estimates adequately captured the spatial and seasonal structure of ET. Overall, our results demonstrate the potential of this simple but practical method for monitoring ET over regions with heterogeneous surface areas.

Key words

remote sensing evapotranspiration northern China triangle interpolation method MODIS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, M. C., and Coauthors, 2011: Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrology and Earth System Sciences, 15, 223–239.CrossRefGoogle Scholar
  2. Baldocchi, D. D., and Coauthors, 2001: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Amer. Meteor. Soc., 82, 2415–2434.CrossRefGoogle Scholar
  3. Bastiaanssen, W. G. M., 2000: SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J. Hydrol., 229, 87–100.CrossRefGoogle Scholar
  4. Bastiaanssen W. G. M., M. Menenti, R. A. Feddes, and A. A. M. Holtslag, 1998: A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. J. Hydrol., 212–213, 198–212.CrossRefGoogle Scholar
  5. Chen, B., J. M. Chen, G. Mo, T. A. Black, and D. E. J. Worthy, 2008: Comparison of regional carbon flux estimates from CO2 concentration measurements and remote sensing based footprint integration. Global Biogeochemical Cycles, 22, GB2012, doi: 10.1029/2007GB003024.CrossRefGoogle Scholar
  6. Cleugh, H. A., R. Leuning, Q. Z. Mu, and S. W. Running, 2007: Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sens. Environ., 106, 285–304.CrossRefGoogle Scholar
  7. Fisher, J. B., K. P. Tu, and D. D. Baldocchi, 2008: Global estimates of the land atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at FLUXNET sites. Remote Sens. Environ., 112, 901–919.CrossRefGoogle Scholar
  8. Foken, T., and B. Wichura, 1996: Tools for quality assessment of surface-based flux measurements. Agricultural and Forest Meteorology, 78, 83–105.CrossRefGoogle Scholar
  9. Franssen, H. J., R. Stöckli, I. Lehnera, E. Rotenberg, and S. I. Seneviratne, 2010: Energy balance closure of eddy-covariance data: A multisite analysis for European FLUXNET stations. Agricultural and Forest Meteorology, 150, 1553–1567.CrossRefGoogle Scholar
  10. French, A., T. J. Schmugge, W. P. Kustas, K. L. Brubaker, and J. Prueger, 2003: Surface energy fluxes over El Reno, Oklahoma using high resolution remotely sensed data. Water Resour. Res., 39, 1164, doi: 10.1029/2002WR001734.CrossRefGoogle Scholar
  11. Fu, C. B., and Z. S. An, 2002: Study of aridication in northern China—A global change issue forcing directly the demand of nation. Earth Science Frontiers, 9, 271–275. (in Chinese)Google Scholar
  12. Jia, L., and Coauthors, 2003: Estimation of sensible heat flux using the Surface Energy Balance System (SEBS) and ATSR measurements. Physics and Chemistry of the Earth, 28, 75–88.CrossRefGoogle Scholar
  13. Jiang, L., and S. Islam, 1999: A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophys. Res. Lett., 26, 2773–2776.CrossRefGoogle Scholar
  14. Jiang, L., and S. Islam, 2001: Estimation of surface evap oration map over southern Great Plains using remote sensing data. Water Resour. Res., 37, 329–340.CrossRefGoogle Scholar
  15. Jiang, L., S. Islam, and T. N. Carlson, 2004: Uncertainties in latent heat flux measurement and estimation: Implications for using a simplified approach with remote sensing data. Canadian Journal of Remote Sensing, 30, 769–787.CrossRefGoogle Scholar
  16. Jiang, L., S. Islam,W. Guo, A. S. Jutla, S. U. S. Senarath, B. H. Ramsay, and E. Eltahir, 2009: A satellite-based daily actual evapotranspiration estimation algorithm over south Florida. Global and Planetary Change, 67, 62–77.CrossRefGoogle Scholar
  17. Kidston, J., C. Brümmer, T. A. Black, K. Morgenstern, Z. Nesic, J. H. McCaughey, and A. G. Barr, 2010: Energy balance closure using eddy covariance above two different land surfaces and implications for CO2 flux measurements. Bound. Layer Meteor., 136, 193–218.CrossRefGoogle Scholar
  18. Kormann, R., and F. X. Meixner, 2001: An analytical footprint model for non-neutral stratification. Bound.-Layer Meteor., 99, 207–224.CrossRefGoogle Scholar
  19. Li, Z. L., R. L. Tang, Z. M. Wan, Y. Y. Bi, C. H. Zhou, B. H. Tang, G. J. Yan, and X. Y. Zhang, 2009: A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9, 3801–3853.CrossRefGoogle Scholar
  20. Liang, S. L., 2000: Narrowband to broadband conversions of land surface albedo I algorithms. Remote Sensing of Environment, 76, 213–238.CrossRefGoogle Scholar
  21. Liu, H. Z., G. Tu, C. B. Fu, and L. Q. Shi, 2008: Threeyear variations of water, energy and fluxes of cropland and degraded grassland surfaces in a semi-arid area of northeastern China. Adv. Atmos. Sci., 25, 1009–1020, doi: 10.1007/s00376-008-1009-1.CrossRefGoogle Scholar
  22. Liu, S. M., R. Sun, and Z. P. Sun, 2006: Evaluation of three complementary relationship approaches for evapotranspiration over the Yellow River basin. Hydrological Processes, 20, 2347–2361.CrossRefGoogle Scholar
  23. Liu, S. M., Z. W. Xu, W. Z. Wang, Z. Z. Jia, M. J. Zhu, J. Bai, and J. M. Wang, 2011: A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrology and Earth System Sciences, 15, 1291–1306.CrossRefGoogle Scholar
  24. Loescher, H. W., B. E. Law, L. Mahrt, D. Y. Hollinger, J. Campbell, and S. C. Wofsy, 2006: Uncertainties in, and interpretation of, carbon flux estimates using the eddy covariance technique. J. Geophys. Res., 111, D21S90, doi: 10.1029/2005JD006932.CrossRefGoogle Scholar
  25. Luo, X. P., K. L. Wang, H. Jiang, J. Sun, and Q. L. Zhu, 2012: Estimation of land surface evapotranspiration over the Heihe River basin based on the revised three-temperature model. Hydrological Processes, 26, 1263–1269.CrossRefGoogle Scholar
  26. Mallick, K., and Coauthors, 2009: Latent heat flux estimation in clear sky days over Indian agroecosystems using noontime satellite remote sensing data. Agricultural and Forest Meteorology, 149, 1646–1665.CrossRefGoogle Scholar
  27. Mu, Q. Z., F. A. Heinsch, M. S. Zhao, and S.W. Running, 2007: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ., 111, 519–536.CrossRefGoogle Scholar
  28. Mu, Q. Z., M. S. Zhao, and S. W. Running, 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 1781–1800.CrossRefGoogle Scholar
  29. Nemani, R. R., M. A. White, P. Thornton, K. Nishida, S. Reddy, J. Jenkins, and S. W. Running, 2002: Recent trends in hydrologic balance have enhanced the carbon sink in the United States. Geophys. Res. Lett., 29, 1468, doi: 10.1029/2002GL014867.CrossRefGoogle Scholar
  30. Nishida, K., R. R. Nemani, S. W. Running, and J. M. Glassy, 2003: An operational remote sensing algorithm of land surface evaporation. J. Geophys. Res., 108(D9), 4270, doi: 10.1029/2002JD002062.CrossRefGoogle Scholar
  31. Norman, J. M., W. P. Kustas, and K. S. Humes, 1995: Two source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77, 263–293.CrossRefGoogle Scholar
  32. Parlange, M. B., and J. D. Albertson, 1995: Regional scale evaporation and the atmospheric boundary layer. Rev. Geophys., 33, 99–124.CrossRefGoogle Scholar
  33. Priestley, C. H. B., and R. J. Taylor, 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100, 81–92.CrossRefGoogle Scholar
  34. Schmid, H. P., 1997: Experimental design for flux measurements: Matching the scales of observations and fluxes. Agricultural and Forest Meteorology, 87, 179–200.CrossRefGoogle Scholar
  35. Schmid, H. P., and C. R. Lloyd, 1999: Spatial representativeness and the location bias of flux footprints over inhomogeneous areas. Agricultural and Forest Meteorology, 93, 195–209.CrossRefGoogle Scholar
  36. Shu, Y., S. Stisen, K. H. Jensen, and I. Sandholt, 2011: Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data. International Journal of Applied Earth Observation and Geoinformation, 13, 192–206.CrossRefGoogle Scholar
  37. Sobrino, J. A., M. Gómez, J. C. Jiménez-Muñoz, and A. Olioso, 2007: Application of a simple algorithm to estimate daily evapotranspiration from NOAA-AVHRR images for the Iberian Peninsula. Remote Sens. Environ., 110, 139–148.CrossRefGoogle Scholar
  38. Su, Z., 2002: The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6, 85–99.CrossRefGoogle Scholar
  39. Sun, Z. G., M. Gebremichael, J. Ardo, A. Nickless, B. Caquet, L. Merboldh, and W. Kutschi, 2012: Estimation of daily evapotranspiration over Africa using MODIS/Terra and SEVIRI/MSG data. Atmos. Res., 112, 35–44.CrossRefGoogle Scholar
  40. Tang, R., Z. L. Li, and B. Tang, 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 Sens. Environ., 114, 540–551.CrossRefGoogle Scholar
  41. Venturini, V., G. Bisht, S. Islam, and L. Jiang, 2004: Comparison of evaporative fractions estimated from AVHRR and MODIS sensors over South Florida. Remote Sens. Environ., 93, 77–86.CrossRefGoogle Scholar
  42. Vinukollu, R. K., E. F. Wood, C. R. Ferguson, and J. B. Fisher, 2011: Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches. Remote Sens. Environ., 115, 801–823.CrossRefGoogle Scholar
  43. Wang, K., and R. E. Dickinson, 2012: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys., 50, RG2005, doi: 10.1029/2011RG000373.CrossRefGoogle Scholar
  44. Wang, K. C., Z. Q. Li, and M. Crib, 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 Sens. Environ., 102, 293–305.CrossRefGoogle Scholar
  45. Wang, K. C., and S. L. Liang, 2008: An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture. Journal of Hydrometeorology, 9, 712–727.CrossRefGoogle Scholar
  46. Wang, H. S., G. S. Jia, C. B. Fu, J. M. Feng, T. B. Zhao, and Z. G. Ma, 2010: Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ., 114, 2248–2258.CrossRefGoogle Scholar
  47. Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapour transfer. Quart. J. Roy. Meteor. Soc., 106, 85–100.CrossRefGoogle Scholar
  48. Yi, C., 2008: Momentum transfer within canopies. Journal of Applied Meteorology and Climatology, 47, 262–275.CrossRefGoogle Scholar
  49. Yi, C., and Coauthors, 2010: Climate control of terrestrial carbon exchange across biomes and continents. Environmental Research Letters, 5, 034007, doi: 10.1088/1748-9326/5/3/034007.CrossRefGoogle Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hesong Wang (王鹤松)
    • 1
  • Gensuo Jia (贾根锁)
    • 1
  1. 1.RCE-TEA, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

Personalised recommendations