Air surface temperature (Tair) is an important parameter for a wide range of applications such as vector-borne disease bionomics, hydrology and climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of Tair at both regional and global scales. Some studies have tried to derive maximum (Tmax), minimum (Tmin) and average air temperature (Tavg) using different methods, with variable estimation accuracy; errors generally fall in the 2–3 °C range while the level of precision generally considered as accurate is 1–2 °C. The main objective of this study was to accurately estimate Tmax, Tmin and Tavg for a 10 year period based on remote sensing—Land Surface Temperature (LST) data obtained from MODIS—and auxiliary data using a statistical approach. An optimization procedure with a mixed bootstrap and jackknife resampling was employed. The statistical models estimated Tavg with a MEF (Model Efficiency Index) of 0.941 and a RMSE of 1.33 °C. Regarding Tmax and Tmin, the best MEF achieved was 0.919 and 0.871, respectively, with a 1.83 and 1.74 °C RMSE. The developed datasets provided weekly 1 km estimations and accurately described both the intra and inter annual temporal and spatial patterns of Tair. Potential sources of uncertainty and error were also analyzed and identified. The most promising developments were proposed with the aim of developing accurate Tair estimations at a larger scale in the future.