SENTIFLEX originates from the recognition that current satellite-based photosynthesis maps (e.g., GPP maps) have inherent uncertainties with respect to space and time. With ESA's ambition to take the forefront in global SIF mapping, FLEX was recently selected as 8th Earth Explorer. FLEX will be specifically designed to measure the full broadband SIF signal. However, the responsibility of ESA stops at the delivery of L2 products, i.e. a set of SIF-derived products (e.g. peaks, total SIF emission).

The processing of SIF data into meaningful higher-level products is left to be done by the scientific community. SENTIFLEX seeks to tackle this by developing a vegetation monitoring facility to resolve some of the scientific and practical challenges related to the satellite-based quantification of vegetation productivity for the benefit of the broader land scientific community and eventually end-users in the agricultural sector and players involved with food security.

The prime goal of SENTIFLEX is to develop a prototype Vegetation Productivity Monitoring Facility focused upon inferring unbiased canopy photosynthesis from forthcoming FLEX-Sentinel-3 satellite observations.

This facility will enable processing of FLEX-Sentinel-3 data influx for retrieval of high-level vegetation products and assimilation with data-driven and machine learning models. To accomplish this, SENTIFLEX will build on a strong scientific theoretical foundation based on experimental, modelling and processing studies. Consolidated SIF-photosynthesis relationships will form the basis of the processing scheme. Although FLEX will not be launched until 2022, it is the ambition of SENTIFLEX to be fully prepared when the satellite starts transmitting data. The project can be broken down into the following sub-objectives:

  • To disentangle canopy-leaving SIF-photosynthesis relationships from vegetation and atmospheric drivers in heterogeneous conditions through experimental and modelling studies.
  • To develop a FLEX-Sentinel-3 data assimilation scheme based on Sentinel-3 and FLEX-like data in preparation for the forthcoming FLEX-Sentinel-3 Tandem Mission.
  • To exploit new data-assimilation techniques based on synergy of statistical and physically-based models, e.g., through emulation To interpolate spatiotemporal scaling by making use of RTM couplings with ensemble Kalman filter and machine learning prediction algorithms.
  • To implement these processing developments into a prototype processing chain that will be functional when FLEX data emerges.
  • To incorporate photosynthesis and its associated productivity products into high-level assimilation systems and tailored early-warning crop monitoring tools and services.


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