Clear-air anomaly detection using modified Kalman temporal filter from geostationary multispectral data

A multispectral temporal-based remote sensing technique based on a modified Kalman filter is presented for clear-air detection by using Geostationary visible-infrared radiometric passive measurements. The Kalman estimate relies on a model of the daily measurement cycle of the considered pixel in clear-sky conditions. If the measurement significantly deviates from the predicted value, an anomaly is detected, which is interpreted as a non-clear air scenario. The add-on value of such approach is to be able to provide a-priori estimates, making the algorithm applicable in a global way. The Meteosat Second Generation satellite has been used over a large sample area in West Africa and a test period of three months. An inter-comparison with respect to the EUMETSAT cloud mask product has been carried out showing promising results in terms of detecting clear-air scenarios and percentages of matching around 90% over the entire period

Multidisciplinary approach for the safeguard of cultural landscapes and UNESCO World Heritage sites

Cultural Heritage is more and more endangered due to a wide list of factors. Climate change consequences, as sudden and heavy rains and floods, together with ground deformation and buildings deterioration, are increasingly frequent worldwide. The present work focuses on Copernicus data exploitation for multi-temporal monitoring of archeological sites and detection of archaeological features according to the stratigraphic patterns of the selected cultural heritage sites