Land Surface Phenology (LSP) is defined as theseasonal and inter-annual variation in land surface vegetation photosynthetic activity,as measured by satellite vegetation indices. LSP is a key indicator for understanding the dynamics (e.g., responses and feedbacks) of ecosystems to changing climate system and environmentalstresses, as well as for representing these in terrestrial biosphere models. Byallowing the quantification of vegetation phenological trends at various scales, LSP fills the gap between traditional phenological (field) observations and the large-scale view of global models.
The important role of LSP is recognized by its contribution to the Remote Sensing enabled Essential Biodiversity Variables (RS-EBVs)projectinitiated by the European Space Agency (ESA). RS-EBVs are defined as the measurements required to study, report, and manage biodiversity changesusing satellite data. They provide information on the status and trendsof biodiversity, and have the potential to act as brokers between monitoring initiatives and decision makers.
The Swiss Data Cube (SDC – http://www.swissdatacube.ch) is an innovative analytical cloud-computing platformallowing usersthe access, analysisand visualizationof35 years of optical (e.g., Sentinel-2; Landsat 5, 7, 8) and radar (e.g., Sentinel-1) satelliteEarth Observation (EO) Analysis Ready Data. Importantly, the SDCminimizes the time and scientific knowledge required for national-scale analyses of large volumesof consistentlycalibrated and spatially aligned satellite observations.
The objective of the SDC is to support the Swiss government for environmental monitoring and reporting, as well as enabling Swiss scientific institutions to benefit from EO data for research and innovation. Additionally, the SDC allows for highspatial and temporal resolution LSP monitoring, thereby facilitating the study of seasonal dynamics of vegetated land surfacesin response to climate and environmental change.
In the frameworkof theESA-fundedGlobDiversityproject (https://www.globdiversity.net),UZH has develop a new algorithm for monitoring LSP. It has been tested and validated in the Laegern region (10 km2) and shows promising results. In order to provide national information on the biodiversity of terrestrial ecosystems and simultaneously generate a decision-ready product,the LSP monitoring algorithm now requires to be scaled up from the development stage to the operational level, encompassing entire Switzerland. Such a product could be readily used as a basis for the design, implementation and evaluation of policies, as well as developing policy advice, programs and regulation.
Consequently, the aims of the project are touse the SDC platform to:
- Implement the LSP algorithm developed by UZH and consider improvements;
- Generate an LSP product retrieval from EO data for the entire Switzerland to contribute to provide baseline data for monitoring biodiversity;
- Demonstrate the use and capability of processing open and freely available Big EO data on a cloud-computing platform;
- Review and evaluate the variability and evolution of satellite-derived growing season length (GSL) nationwide;
- Test start- and end-of-season metrics (SOS and EOS, respectively) for linear trends as well as for significant trend shifts over the study period.
Dr. Gregory Giuliani, University of Geneva
Dr. Claudia Röösli, University of Zurich
Dr. Vladimir Wingate, University of Zurich
Dr. David Small, University of Zurich
Prof. Michael Schaepman, University of Zurich
Bruno Chatenoux, University of Geneva
Charlotte Poussin, University of Geneva
Prof. Pascal Peduzzi, University of Geneva
Prof. Anthony Lehmann, University of Geneva