The very first multi-temporal and multi-spectral Level-2 SEVIRI processor for the simultaneous physical retrieval of surface temperature and emissivity

Abstract

The estimation of surface parameters yields important information in several applications on regional and global scale. Because of their high temporal resolution, infrared instruments on board geostationary platforms are capable to provide time sequences of observations, which fully resolve the diurnal cycle. To exploit multi-temporal information, a Kalman filter (KF) methodology has been implemented in order to retrieve simultaneously surface temperature and emissivity from SEVIRI (Spinning Enhanced Visible and Infrared Imager) infrared data. Because of its sequential nature, the Kalman filter methodology yields a very fast software implementation, which can be applied to the SEVIRI full disk for off-line analysis. The software can run in real-time at the regional scale, which makes it very attractive for different applications such as land surveillance, natural hazards, risk management, and so on. The paper will show the basic methodology and applications at regional and global scale.