6.7
Improvements and Evaluations of the MODIS Global Evapotranspiration Algorithm

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Tuesday, 25 January 2011: 5:00 PM
Improvements and Evaluations of the MODIS Global Evapotranspiration Algorithm
611 (Washington State Convention Center)
Qiaozhen Mu, University of Montana, Missoula, MT; and M. Zhao and S. W. Running

MODIS global evapotranspiration (ET) products (MOD16) by Mu et al. (2007) are the first regular, near-real-time data sets for repeated monitoring of vegetation evapotranspiration on vegetated land at 0.05-degree and 1-km resolution at an 8-day interval. In this study, the RS-ET method in Mu et al.'s 2007 paper (hereafter called beta version algorithm) has been improved to version 1 MOD16 algorithm. 1) The canopy was taken into wet and dry surface. The water lost from canopy includes canopy evaporation from the wet canopy surface and the canopy transpiration from dry surface. 2) The ground surface was taken into saturate wet surface and moisture surface. The ground surface evaporation includes potential evaporation from the saturate wet surface and actual evaporation from the moisture surface. 3) The ET includes daytime and nighttime parts. 4) The amount of soil heat flux is estimated and now only occurs to the radiation partitioned on the ground surface. 5) The methods to estimate stomatal conductance, aerodynamic resistance, boundary layer resistance and vegetation cover fraction have been improved. We evaluate version 1 algorithm using ET observations at 46 AmeriFlux eddy covariance flux towers. We calculated ET with both version 1 algorithm and beta version algorithm using Global Modeling and Assimilation Office (GMAO v. 4.0.0) meteorological data and compared the resulting ET estimates with observations. Version 1 algorithm reduces average daily ET bias over the 46 towers from -0.29 mm/day to -0.11 mm/day with tower meteorological data, and from -0.24 mm/day to -0.02 mm/day with GMAO meteorological data. The version 1 MOD16 ET algorithm increased the correlation coefficients of the ET observations with tower-driven ET estimates from 0.83 to 0.86, and from 0.81 to 0.86 driven by GMAO meteorology. We then applied both the beta and version 1 MODIS ET algorithms globally to get the 1km MOD16 ET estimates with remote sensing data and reanalysis meteorological data to obtain the annual global ET (MODIS ET) for over 2000-2006. Results indicate beta version underestimates the ET and version 1 ET estimates at the arid region have been increased a lot.