ABSTRACT
ETo24 ETo for a 24 h period EVI Enhanced vegetation index εo Broadband surface emissivity fc Vegetation cover fraction fg Green canopy fraction (Fisher et al., 2008) fM Plant moisture constraint (Fisher et al., 2008) FPAR Photosynthetically active radiation fSM Soil moisture constraint (Fisher et al., 2008) fT Temperature constraint to ET (Fisher et al., 2008) fwet Relative surface wetness (Fisher et al., 2008) Fwet Water cover fraction (Mu et al., 2011) G Ground heat ¨ux γ Psychometric constant H Sensible heat ¨ux h Vegetation height η Coe¶cient in empirical crop coe¶cient method Kc Crop coe¶cient in FAO-56 method Ks Soil moisture stress coe¶cient in FAO-56 method LAI Leaf area index LST Land surface temperature, equivalent to TR LW↑ Upwelling longwave radiation LW↓ Downwelling longwave radiation Λ Evaporative fraction Λ24 Λ for 24 h period Λd Λ for daylight hours Λop Λ at time of overpass λ Latent heat of vaporization λEI Latent heat ¨ux from evaporation from wet
canopy leaf surfaces λEs Latent heat ¨ux from evaporation from the soil
surface λESP Potential latent heat ¨ux from soil evaporation
(Mu et al., 2011) λET Latent heat ¨ux λETc Latent heat ¨ux from transpiration m(D) Multiplier limiting stomatal conductance by D
(Mu et al., 2007) m(Tmin) Multiplier limiting stomatal conductance by
minimum air temperature (Mu et al., 2007) Mo Soil moisture N* NDVI NDVI Normalized di¦erence vegetation index NDVIo NDVI for bare soil NDVIs NDVI for dense vegetation Ω Index of degree of clumping (ALEXI) σ Stefan-Boltzmann constant ra Aerodynamic surface resistance ra_s Aerodynamic resistance at the soil surface (Mu
et al., 2011) Rah Aerodynamic resistance to turbulent heat
transport between z1 and z2 RH Relative humidity RMSE Root-mean-square error Rn Net radiation
Rn24 Net radiation over 24 h period Rns Net radiation at the soil surface Rs sensible heat exchange resistance of the soil
surface rs Resistance of the land surface or plant canopy
to ET rs_c Dry canopy resistance to transpiration (Mu
et al., 2011) rs_wetC Wet canopy resistance to evaporation Rx total boundary layer resistance of the canopy ρ Air density s Slope of the saturation vapor pressure versus
temperature curve SAVI Soil-adjusted vegetation index SW↓ Incoming shortwave radiation T1 Aerodynamic temperature of the evaporating
surface at height z1 T1c Vegetation canopy temperature (ALEXI) T1s Soil temperature (ALEXI) T2 Air temperature at height z2 Tc Ÿeoretical surface temperature under cool/
moist conditions (SSEBop) Th Ÿeoretical surface temperature under hot/dry
conditions (SSEBop) TR Radiometric surface temperature, equivalent
to LST TˆRhi Predicted TR at high spatial resolution (Kustas
et al., 2003) ˆ ( )T NDVIRlow low Predicted radiometric temperature using low
resolution NDVI Tˆ NDVIR hilow ( ) Predicted radiometric temperature using high-
resolution NDVI (Kustas et al., 2003) TRmax Minimum TR over vegetation TRmin Maximum TR over bare soil Tscaled Scaled TR θ View angle VI Vegetation index VImax VI value when ET is maximum VImin VI value for bare soil z1 Height above the ground surface of the evapo-
rating surface, = d + z0m z2 Height at which air temperature is measured
(o¬en 2 or 3 m) zom Surface roughness for momentum transport,
~0.03-0.123 h
ABL Atmospheric boundary layer AGRIMET Agricultural meteorological modeling system ALEXI Atmosphere-land exchange inverse model
(Anderson et al., 1997) ASTER Advanced spaceborne thermal emission and
re¨ection radiometer
AVHRR Advanced very high resolution radiometer CERES Clouds and Earth’s Radiant Energy System CONUS Conterminous United States DAIS Digital airborne imaging spectrometer DisALEXI Disaggregation ALEXI model (Norman et al., 2003) DSTV diurnal surface temperature variation DTD Dual-temperature-di¦erence ECMWF European Centre for Medium-Range Weather
Forecasts EO Earth observation FIFE First ISLSCP (International Satellite Land
Surface Climatology Project) Field Experiment FLUXNET Global network of micrometeorological ¨ux
tower sites GDAS Global Data Assimilation System GG model Granger and Gray (GG) model (Granger and
Gray, 1989) GLDAS Global Land Data Assimilation System LSA-SAF Land Surface Analysis Satellite Applications
Facility MERRA Modern-Era Retrospective Analysis for
Research and Applications METRIC Mapping Evapotranspiration at high Reso-
lution with Internalized Calibration MMR Modular multispectral radiometer MOD16 MODIS ET product, also called PM-Mu (Mu
et al., 2011) MOD43B3 MODIS albedo product MODIS Moderate Resolution Imaging Spectroradio-
met er MSG Meteosat second generation satellite NCEP-NCAR National Centers for Environmental Prediction-
National Center for Atmospheric Research NWS-NOAH National Weather Service PoLDER Polarization and directionality of Earth re¨ec-
tance instrument PT-JPL Priestley-Taylor jet propulsion laboratory
model (Fisher et al., 2008) RMSD Root-mean-square di¦erence SEBAL Surface energy balance algorithm
(Bastiaanssen et al., 1998) SEBS Surface Energy Balance System (Su, 2002) SEVIRI Spinning enhanced visible and infrared imager SGP Southern Great Plains Sim-ReSET Simple remote sensing evapotranspiration
model SMACEX Soil Moisture-Atmosphere Coupling Experiment SRB Surface radiation budget SSEB Simpli¤ed surface energy balance S-SEBI Simpli¤ed surface energy balance index SSEBop Operational simpli¤ed surface energy balance STARFM Spatial and temporal adaptive re¨ectance
fusion model SVAT Soil vegetation atmosphere transfer model
TRMM Tropical Rainfall Measurement Mission TSM Two source model VMC Vegetation and moisture coe¶cient, equiva-
Agriculture accounted for the majority of human water use and for more than 90% of global freshwater consumption during the twentieth century (Hoekstra and Mekonnen, 2012; Shiklomanov, 2000). Stream¨ow depletion due to enhanced evapotranspiration (ET) from irrigated crops impacts freshwater ecosystems globally (Foley et al., 2005). Water scarcity limits crop production in many arid and semiarid regions, and water is likely to be a key resource limiting food production and food security in the twenty-¤rst century (Foley et al., 2011; Vorosmarty et al., 2000). Despite this, estimates of the location and temporal dynamics of ET from croplands are o¬en uncertain at a variety of spatial and temporal scales. Better information on ET can be useful in several applications at a range of spatial scales, including water resources, agronomy, and meteorology (e.g., Rivas and Caselles, 2004). At the scale of irrigation projects, maps of ET can assist with irrigation scheduling and demand assessment. Measurements of ET are required for monitoring plant water requirements, plant growth, and productivity, as well as for irrigation management and deciding when to carry out cultivation procedures (e.g., Consolli et al., 2006; Glenn et al., 2007; Yang et al., 2010).
