Universiteit Leiden Universiteit Leiden

Nederlands English


Improving vegetation representation in Multi-sensor Earth Observation Products through phenology and trait-based priors

How can various aspects of plant trait phenology (such as trait-trait covariance, spatial and temporal variances, andintraspecific variances) be analyzed through remote sensing to create priors for robust vegetation products and analysis?

Looptijd 2016  -  2020
Contact Amie Corbin
Financiering EU Horizon 2020EU Horizon 2020
Participant No Participant organisation name Country
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
9 UVSQ-LSCE France

Short abstract

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.

Photo Sentinel-2: (c) ESA/ATG medialab 2015

Project description

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.