A remote sensing method for estimating surface air temperature and surface vapor pressure on a regional Scale

Abstract

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 T a and e a. Good correlation coefficients (R 2) and root mean square error (RMSE) between the measurements of T a /e a at weather stations and T a /e a 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 T a 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 T a /e a estimates at Dongping Lake illustrated that the method proposed in the paper also has good accuracy for a heterogeneous surface. The absolute biases of T a and e a 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 T a estimation method presented in the paper is most sensitive to surface temperature and that the e a estimation method is most sensitive to available energy.