Improvements to a MODIS Global Terrestrial Evapotranspiration Algorithm

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Remote Sensing of Environment

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MODIS global evapotranspiration (ET) products by Mu et al. [Mu, Q., Heinsch, F. A., Zhao, M., Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519–536. doi: 10.1016/j.rse.2007.04.015] are the first regular 1-km2 land surface ET dataset for the 109.03 Million km2 global vegetated land areas at an 8-day interval. In this study, we have further improved the ET algorithm in Mu et al. (2007a, hereafter called old algorithm) by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface. We compared the improved algorithm with the old one both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is 62.8 × 103 km3, agrees very well with other reported estimates of 65.5 × 103 km3 over the terrestrial land surface, which is much higher than 45.8 × 103 km3 estimated with the old algorithm. For ET evaluation at eddy flux towers, the improved algorithm reduces mean absolute bias (MAE) of daily ET from 0.39 mm day−1 to 0.33 mm day−1 driven by tower meteorological data, and from 0.40 mm day−1 to 0.31 mm day−1 driven by GMAO data, a global meteorological reanalysis dataset. MAE values by the improved ET algorithm are 24.6% and 24.1% of the ET measured from towers, within the range (10–30%) of the reported uncertainties in ET measurements, implying an enhanced accuracy of the improved algorithm. Compared to the old algorithm, the improved algorithm increases the skill score with tower-driven ET estimates from 0.50 to 0.55, and from 0.46 to 0.53 with GMAO-driven ET. Based on these results, the improved ET algorithm has a better performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes.




© 2011 Elsevier

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