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.
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Lu, J.; Tang, R.; Tang, H.; Li, Z.-L.; Zhou, G.; Shao, K.; Bi, Y.; Labed, J. Daily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation. Remote Sens. 2014, 6, 4369-4390.
Lu J, Tang R, Tang H, Li Z-L, Zhou G, Shao K, Bi Y, Labed J. Daily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation. Remote Sensing. 2014; 6(5):4369-4390.
Lu, Jing; Tang, Ronglin; Tang, Huajun; Li, Zhao-Liang; Zhou, Guoqing; Shao, Kun; Bi, Yuyun; Labed, Jelila. 2014. "Daily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation." Remote Sens. 6, no. 5: 4369-4390.