Improvements to a \{MODIS\} global terrestrial evapotranspiration algorithm
Qiaozhen Mu and Maosheng Zhao and Steven W. Running

Type: Paper

title = "Improvements to a \{MODIS\} global terrestrial evapotranspiration algorithm ",
journal = "Remote Sensing of Environment ",
volume = "115",
number = "8",
pages = "1781 - 1800",
year = "2011",
note = "",
issn = "0034-4257",
doi = "",
url = "",
author = "Qiaozhen Mu and Maosheng Zhao and Steven W. Running",
keywords = "Evapotranspiration",
keywords = "Stomatal conductance",
keywords = "Soil surface evaporation",
keywords = "Vegetation cover fraction",
keywords = "MODIS ",
abstract = "\{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. "

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