A few different approaches are available to estimate global terrestrial evapotranspiration (ET): I) modeled as part of larger climate or land surface models; II) dedicated mechanistic models focused primarily on ET; and, III) empirical/statistical techniques that correlate ground observations of ET against associated remote sensing or other globally gridded observations (1). Of these approaches, the dedicated mechanistic models may be the most robust, containing less uncertainty than the climate and land surface models due to tighter constraints from fewer degrees of freedom, while able to represent a full array of ecosystems and climates, and their feedbacks, whereas empirical techniques are often limited in space and time to where and when ground data were collected (2). The dedicated mechanistic models can be thought of as operating in two camps: one relying on observations of land surface temperature as a response and constraint variable to moisture and/or sensible heat status (3, 4), and the other relying on calculating potential ET from the Penman-Monteith (5) and/or Priestley-Taylor (6) equations then reducing potential to actual ET based on novel formulations of stomatal and aerodynamic conductances and/or plant ecophysiological response (7-11). We focus here on describing the development and progress of two of the leading potential-to-actual ET models—by Fisher et al. (9) and Mu et al., (7)—which have been used in a wide variety of applications and intercomparisons (1, 2, 12-14). They represent different ideas of how potential ET should be reduced to actual ET. We shed insight into why one model performs better than the other under some circumstances, and vice versa, against FLUXNET data.

1. B. Mueller et al., Geophysical Research Letters 38, doi:10.1029/2010GL046230 (2011). 2. R. K. Vinukollu, E. F. Wood, C. R. Ferguson, J. B. Fisher, Remote Sensing of Environment 115, 801 (2011). 3. M. C. Anderson, J. M. Norman, G. R. Diak, W. P. Kustas, J. R. Mecikalski, Remote Sensing of Environment 60, 195 (May, 1997). 4. Z. Su, Hydrology and Earth System Sciences 6, 85 (2002). 5. J. L. Monteith, Symposium of the Society of Exploratory Biology 19, 205 (1965). 6. C. H. B. Priestley, R. J. Taylor, Monthly Weather Review 100, 81 (1972). 7. Q. Mu, M. Zhao, S. W. Running, Remote Sensing of Environment 111, 519 (2011). 8. Q. Mu, F. A. Heinsch, M. Zhao, S. W. Running, Remote Sensing of Environment 111, 519 (2007). 9. J. B. Fisher, K. Tu, D. D. Baldocchi, Remote Sensing of Environment 112, 901 (2008). 10. R. Leuning, Y. Q. Zhang, A. Rajaud, H. Cleugh, K. Tu, Water Resources Research 44, doi:10.1029/2007WR006562 (2008). 11. K. Nishida, R. R. Nemani, S. W. Running, J. M. Glassy, Journal of Geophysical Research-Atmospheres 108, 4270 (2003). 12. C. Jiménez et al., Journal of Geophysical Research 116, doi:10.1029/2010JD014545 (2011). 13. J. B. Fisher et al., Global Change Biology 15, 2694 (2009). 14. O. L. Phillips et al., Science 323, 1344 (2009).