This paper presents a method of estimating regional distributions of surface air
temperature (Ta) and surface vapor pressure (ea), which uses remotely-sensed data and
meteorological data as its inputs. The method takes into account the effects of both local
driving force and horizontal advection on Ta and ea. Good correlation coefficients (R2) and
root mean square error (RMSE) between the measurements of Ta/ea at weather stations and
Ta/ea estimates were obtained; with R^2 of 0.77, 0.82 and 0.80 and RMSE of 0.42K, 0.35K
and 0.20K for Ta and with R^2 of 0.85, 0.88, 0.88 and RMSE of 0.24hpa, 0.35hpa and
0.16hpa for ea, respectively, for the three-day results. This result is much better than that estimated from the inverse distance weighted method (IDW). The performance of Ta/ea
estimates at Dongping Lake illustrated that the method proposed in the paper also has good
accuracy for a heterogeneous surface. The absolute biases of Ta and ea estimates at Dongping
Lake from the proposed method are less than 0.5Kand 0.7hpa, respectively, while the
absolute biases of them from the IDW method are more than 2K and 3hpa, respectively.
Sensitivity analysis suggests that the Ta estimation method presented in the paper is most
sensitive to surface temperature and that the ea estimation method is most sensitive to
available energy.