Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data

Cloud cover usually denies the generation of land surface temperature (LST) time series over large areas from thermal infrared (TIR) data sensed by satellites. If cloud cover is brief (<4 h) and only partial on the scale of a METEOSAT pixel, the discontinuous time series of cloud-free measurements still approximates a cloud-free diurnal temperature cycle (DTC). In order to interpolate the missing values and to describe the thermal behaviour of the land surface as a whole, advantage is taken of METEOSATʹs high temporal resolution of 30 min. A Levenberg–Marquardt minimisation scheme is utilised to automatically fit a model of the DTC to the time series of cloud-screened brightness temperatures (BT). Out of physical considerations and in order to stabilise the fits, a priori knowledge from solar geometry is utilised and the model function is required to be smooth and continuous. A robust estimator of the error, which is based on the median rather than the arithmetic mean, is used to lessen the impact of outliers on the fits. The thermal behaviour of the land surface is then described by the determined model parameters, and the 48 METEOSAT BTs per pixel per day are reduced to five parameters per pixel per day. Monthly maximum and median composites of cloud-screened METEOSAT BTs yield synthetic DTCs, which are less influenced by synoptic effects. Fitting the model to monthly composites reduces the 17 520 METEOSAT BTs per pixel per year to 60 parameters per pixel per year. This data reduction is advantageous to climatological studies, which require the processing of long time series. The model parameters are shown to be related to physical properties of the land surface, particularly to the normalised difference vegetation index (NDVI) and thermal inertia.