Time series analysis and vegetation phenology

Even short-term variations might indicate disturbances that may seriously affect production and health of the vegetation. Monitoring phenology with satellites is a way of improving our knowledge about vegetation-climate interactions.

We develop time series methods for accurate description of satellite-observed phenology (land surface phenology) over large areas. We utilize coarser (like MODIS) and finer (Sentinel-2) resolution satellite sensor data, to map variability of phenological parameters across time and space. This information is useful for understanding vegetation response to climate variations and longer-term change. Further, it provides information on vegetation productivity as input into models of gross primary productivity for estimation of vegetation’s role on the carbon cycle.

Main activity fields

• The TIMESAT software system for estimation of phenological parameters from time-series of satellite sensor data. 
More information on TIMESAT

• The Plant Phenology Index (PPI). This index provides a way to estimate photosynthetically active leaf area index from red and NIR satellite observations. It has proven superior to traditional vegetation indices for vegetation phenology estimation. 
Article on the topic 

• Development of Land Surface Phenology datasets for Europe and globally for the Copernicus Land Monitoring System (CLMS) based TIMESAT, PPI and date from Sentinel-2, PROBA-V and Sentinel-3 (Figure 1). 
Read more in the Research Portal  here and here

• Trend analysis and assessment of vegetation resources in several project and at different spatial and temporal scales, e.g. in collaboration with the European Space Agency (ESA).