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 mm and 12 mm brightness temperature (T 11mm and T 12mm ) 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.
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