Speaker
Description
Human society increasingly depends on information derived from Earth Observation (EO) data. In particular, there is an growing demand for information facilitating agriculture, forestry, soil moisture and hydrology. To this purpose the number of space-borne satellites is projected to increase dramatically over the next years, in the form of ESA/NASA scientific missions but also small cube-sat constellations. This increase in available satellite observations also provides enormous challenges in terms of retrieving actual information from this big data. The general trend currently is to create individual land surface products for each satellite mission. In that regard, a range of individual products are created using a single-sensor approach for similar land surface parameters. Such single-sensor approaches do not take into account synergies between different sensors. Additionally the diversity of single-sensor processing chains cause an inconsistency between the different produced land surface products. In order to facilitate actual usage of earth observation data, a novel multi-sensor framework (together with a new mindset) needs to be created that circumvents these limitations.
Currently, there is no consistent framework to integrate observations from different sensors in order to obtain the best possible estimate of the land surface state, as it is hard to account for the different characteristics of different sensors and to coordinate the data streams. MULTIPLY proposes a solution to this challenge. Specifically: MULTIPLY aims to:
1) Combine data from SAR observations flexibly with optical remote sensing data using compatible radiative transfer models.
2) Design a data assimilation platform incorporating as many information sources (observational, prior, multi-temporal) to optimally retrieve satellite information that are gap-free.
3) Deliver a set of internally consistent data products at different resolutions (coarse and high) with quantified uncertainties.
4) Explore potential applications that demand consistent land surface products, such as agriculture, forestry and soil moisture.
Within this presentation the operational MULTIPLY data assimilation framework will be discussed in detail. The presentation will specifically focus on a) the coupling of the different SAR observations together with optical observations in order to jointly and consistently retrieve soil parameters as well as vegetation traits and b) elaborate on the results of the fieldwork validation efforts.
