Empirical models for estimating daily maximum, minimum and mean air temperatures with MODIS land surface temperatures

Abstract

Daily air temperature is a measurement that is required by many biogeochemical models. This study compared daily maximum (T _max), minimum (T _min) and mean (T _mean) air temperature observations collected at 678 standard meteorological stations of China in 2003 with estimates derived from daytime and night-time land surface temperature (LST) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board TERRA and AQUA satellites. Correlation analysis showed that the determination coefficients (R ² > 0.81) between models using night-time LSTs and the observed air temperatures were higher than those using daytime LSTs (R ² > 0.57), but with significant seasonal variation. Though estimates derived from coupled daytime and night-time LSTs were more accurate than using night-time or daytime LSTs alone, the available pixels were substantially reduced. Four empirical models were established for T _max, T _min and T _mean with MODIS night-time LSTs alone, or with coupled daytime and night-time LSTs, respectively. Solar declination was incorporated into the models to simulate seasonal variation of the correlations. Model validation showed that percentage of residuals within –3°C to 3°C ranged approximately from 60.2% to 74.3%, 64.4% to 69.9% and 76.8% to 85.7% for T _max, T _min and T _mean, respectively. It was concluded that night-time LST was the optimum predictor for estimating daily T _min, T _mean and even T _max when considering both the performance of the models and the availability of the LST data. Moreover, there was no significant difference between LSTs of TERRA and AQUA for estimating daily air temperatures.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant No. 40675075), the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-305) and the National High Technology Research and Development Program of China (Grant No. 2006AA10Z224-1). We thank the National Meteorological Centre, China Meteorological Administration, for providing observational meteorological data and the LPDAAC for providing MODIS images. We also thank two anonymous reviewers who provided helpful comments that led to the improvement of this article.