Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Abstract

Air temperature (Ta) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (Tmax), mean (Tmean), and minimum air temperature (Tmin) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily Ta and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best Tmean and Tmax models were satisfactory (coefficient of determination (R2) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the Tmin models (R2 = 0.80–0.81 and RSE = 2.83–3.00 K). In this article Tmin models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best Tmin models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved Tmin models yield R2 = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R2 and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the Tmin models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.