Aim: To develop and enable application of a practical, flexible, user-friendly platform to provide the scientific community with a tool to generate land surface products and its associated uncertainties and exploit these for data-intensive science.
|Looptijd||2016 - 2019|
|Contact||Peter van Bodegom|
|Financiering||EU Horizon 2020|
With the start of the SENTINEL era, an unprecedented amount of Earth Observation (EO) data will become available. Currently there is no consistent but extendible and adaptable framework to integrate observations from different sensors in order to obtain the best possible estimate of the land surface state. MULTIPLY proposes a solution to this challenge.
The project will develop an efficient, fully generic and fully traceable platform that uses state-of-the-art physical radiative transfer models, within advanced data assimilation (DA) concepts, to consistently acquire, interpret and produce a continuous stream of high spatial and temporal resolution estimates of land surface parameters, fully characterized. These inferences on the state of the land surface will be the result from the coherent joint interpretation of the observations from the different Sentinels, as well as other 3rd party missions (e.g. ProbaV, Landsat, MODIS).
The framework allows users to exchange components as plug-ins according to their needs and builds on the EO-LDAS concepts, which have shown the feasibility of producing estimates of the land surface parameters by combining different sets of observations through the use of radiative transfer models. The data retrieval platform will operate in an environment with advanced visualisation tools.
Users will be engaged throughout the process and trained. Moreover, user demonstrator projects include applications to crop monitoring & modelling, forestry, biodiversity and nature management. Another user demonstrator project involves providing satellite operators with an opportunity to cross-calibrate their data to the science-grade Sentinel standards.
- To provide a platform, based on data assimilation principles, that combines and integrates multisensoral data with prior information and radiative transfer models to optimally retrieve satellite information that are gap-free.
- To design the data assimilation platform such that land surface information is fused to deliver a set of internally consistent data products with quantified uncertainties.
- To deliver and validate a new suite of consistent downstream products to characterize the land surface state (e.g. albedo, faPAR, chlorophyll content, effective LAI, leaf or soil moisture, surface roughness) at different spatial (from tens through hundreds of metres up to (sub-national) and temporal (from a few days till multiple years) scales. As such, we aim to deliver 0in addition to data retrieval- a high levels interpretation of the data.
- To derive and validate, within the same platform, indicators of functional biodiversity and disturbance to enhance further development of Copernicus land surface products