The main objective of this research is to better understand and define typical behaviours of phenological plant responses by their functional traits, so that their representation and boundaries are able to be better represented in modelling, RTMs, and earth observation products.
MULTIscale SENTINEL land surface information retrieval PLatform (MULTIPLY) is a data assimilation platform for land surface products is currently being developed. As a part of the schematic framework, priors for consistent and reliable information are required as a check to bring comparable data to a variety of users. This project aims to focus on vegetation priors through RTMs, mainly focusing on plant traits and phenology. This will be conducted through large meta-analysis to create several priors to add to a data assimilation platform MULTIPLY in relation to several aspects of vegetation phenology. This includes an exploration of covariance, spatial and temporal scales, hyperspectral and multi-spectral outputs, as well as intraspecific variation.
- 1 University Leiden (UL) NL
- 2 University of Munich (LMU) Germany
- 3 University College London (UCL) UK
- 4 Brockmann Consultant Gmb (BC) Germany
- 5 Tartu Observatory (TO) Estonia
- 6 University of Alcala (UAH) Spain
- 7 Assimila UK
- 8 ADAS UK
- 9 UVSQ-LSCE France
MULTIPLY – MULTIscale SENTINEL land surface information retrieval Platform
Improving vegetation representation in Multi-sensor Earth Observation Products through phenology and trait-based priors
Time: 2016 – 2020
Contact: Amie Corbin
Project: EU Horizon 2020EU Horizon 2020