Phenology estimation from Meteosat Second Generation data

Many studies have focused on land surface phenology as a means to characterize global climate. The Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard Meteosat Second Generation (MSG) geostationary satellite can also contribute to this task thanks to its adequate spatial and temporal resolutions. Here, four years of MSG-SEVIRI Normalized Difference Vegetation Index (NDVI) daily time series have been retrieved, which were then gap-filled with the help of an algorithm based on the iterative Interpolation for Data Reconstruction [Julien and Sobrino, 2010]. Finally, phenological parameters have been retrieved from the reconstructed time series, and compared with independent MODIS (Moderate resolution Imaging Spectrometer) data, showing differences for specific land covers although the stability of the retrieved phenophases over the year is surprisingly good for MSG data. This approach can be applied to other geostationary satellites worldwide to obtain quick remotely sensed estimates of vegetation phenology at global scale.