Estimation of air temperature from MODIS data in east China

Abstract

Air temperature, T _a, with high spatial and temporal resolution is desired for global change, agricultural disaster, land surface studies, and modelling applications. A statistical algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS) data is developed for daytime T _a retrievals over east China at a resolution of 0.05° × 0.05°. The approach first applies a statistical regression of the first guess, i.e. the preliminary estimate, of T _a to MODIS 11 μm and 12 μm brightness temperature (T _11μm and T _12μm) and site data (longitude, latitude and altitude) for east China. Then the first guess of T _a is further corrected with a series of bias equations for different latitude zones in east China. Further quantitative validation with measured T _a using 335 synoptic weather stations for the whole of 2006 indicates that the algorithm performs well with overall statistics of R = 0.96, RMSE = 3.23°C, and bias = −0.09°C. 75% of the estimated T _a is within 3°C of the actual T _a and 92% of the estimated T _a is within 5°C of the actual T _a. This bias correction algorithm can be applied to other geostationary and sun-synchronous satellite instruments for T _a retrieval.

Acknowledgements

The authors would like to thank CMA National Meteorological Center for sharing the surface air temperature measurements and National Satellite Meteorological Center for providing the MODIS datasets. The authors are grateful for the constructive comments from anonymous reviewers which substantially improved the quality of this paper. The first author is supported by a grant from the National Natural Science Foundation of China (Grant No. 40801129) and a MOST Project of monitoring and forecasting on high temperature and heat disaster in Middle and Lower Beaches of the Yangtze River (Grant No. 2006BAD04B04).