Abstract
In a previous study, a daily evaporative fraction (EF) parameterization scheme was derived based on day–night differences in surface temperature, air temperature, and net radiation. Considering the advantage that incoming solar radiation can be readily retrieved from remotely sensed data in comparison with surface net radiation, this study simplified the daily EF parameterization scheme using incoming solar radiation as an input. Daily EF estimates from the simplified scheme were nearly equivalent to the results from the original scheme. In situ measurements from six Ameriflux sites with different land covers were used to validate the new simplified EF parameterization scheme. Results showed that daily EF estimates for clear skies were consistent with the in situ EF corrected by the residual energy method, showing a coefficient of determination of 0.586 and a root mean square error of 0.152. Similar results were also obtained for partly clear sky conditions. The non-closure of the measured energy and heat fluxes and the uncertainty in determining fractional vegetation cover were likely to cause discrepancies in estimated daily EF and measured counterparts. The daily EF estimates of different land covers indicate that the constant coefficients in the simplified EF parameterization scheme are not strongly site-specific.
Highlights
Studies on surface energy and water budgets require accurate evapotranspiration (ET) information at large spatial scales [1,2]
evaporative fraction (EF) estimates are closer to the residual energy (RE)-corrected EF, showing an root mean square error (RMSE) of
Because incoming solar radiation (Rg) can be readily estimated from remotely sensed data and there is a strong relationship between Rg and Rn, this study simplified the equation for estimating daily EF using Rg
Summary
Studies on surface energy and water budgets require accurate evapotranspiration (ET) information at large spatial scales [1,2]. Physical ET models based on remotely sensed data (e.g., one-source models and two-source models [5,6,7,8,9,10,11]) require ground-based measurements This hinders the application of these models to ungauged areas with few ground-based measurements. Some simple methods can estimate surface energy fluxes by directly using these remotely sensed surface parameters/variables (e.g., empirical equations and the triangle feature space [23,24,25,26,27,28]). The dependence of site and spatial domain is the main disadvantage of these methods [29,30] These methods are mainly driven by remotely sensed surface variables from a one-time observation
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