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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - SENTIFLEX (Fluorescence-based photosynthesis estimates for vegetation productivity monitoring from space)

Teaser

Summary

Agricultural production is under increasing pressure by global anthropogenic changes, including rising population, diversion of cereals to biofuels, increased protein demands and climatic extremes. Through a fleet of Earth observation (EO) satellites, Europe is dedicated to keep fingers on the pulse of its agricultural lands. Among the most innovative vegetation monitoring concepts involves FLEX (FLuorescence EXplorer), which was recently selected as ESA\'s 8th Earth Explorer and is by design timely and ground-breaking: it is the first mission concept specifically dedicated to monitoring the `breathing\' of terrestrial vegetation. FLEX will fly in tandem with Sentinel-3 and will globally measure Sun-Induced chlorophyll Fluorescence (SIF) spectral emission from terrestrial vegetation. Together with Sentinel-3, these two new-generation European EO missions offer huge possibilities to increase our knowledge on the basic functioning of the Earth\'s vegetation, i.e. the photosynthetic process of plants resulting in carbon uptake. With the selection of FLEX, Europe reinforces a forefront role in fostering SIF research. However, for now ESA\'s commitment stops at Level-2c, i.e. delivering geometrically corrected SIF data. Hence, it is to the responsibility of the scientists to interpret and process these data into relevant products that gain insights into terrestrial vegetation dynamics. With this project named SENTIFLEX, we aim to unravel satellite-based SIF-photosynthesis relationships given heterogeneous spatiotemporal conditions on a per-pixel basis. Consolidated relationships will serve the development of a European vegetation productivity monitoring facility that eventually will be based on assimilation of Sentinel-3 surface reflectance with FLEX SIF data.

Motivation:
FLEX is the first mission specifically designed to capture the complete broadband SIF signal as well as complementary reflectance spectra in the green to NIR region (500-780 nm). The SIF emission is characterized by two peaks, one in the red (SIFred) and the other in the NIR (SIFNIR). FLEX will acquire imagery at a spatial resolution of 300 m, which is suitable for discriminating individual plots and stands. Moreover, FLEX is planned to fly in tandem with Sentinel-3, which enables parallel retrieval of relevant atmospheric and vegetation properties (e.g., canopy structure and temperature, non-photochemical energy dissipation). Given the availability of Sentinel-3 data and already-acquired or simulated FLEX-like data from FLEX science studies, the aim of SENTIFLEX is to go beyond the current state of the art in satellite-based SIF-photosynthesis research and overcome current limitations. This will be accomplished by: (1) development of a theoretical SIF-photosynthesis relationship no longer exclusively relying on indirect spectral proxies (e.g., FAPAR, APAR) but instead incorporating quantifiable biophysical variables and associated uncertainties that allow uncoupling vegetation and atmospheric dynamics from the SIF signal on a per-pixel basis; and, (2) implementation of the consolidated theoretical basis into a prototype FLEX-SENTINEL-3 data processing chain for vegetation photosynthesis and productivity monitoring.

Work performed

With the SENTIFLEX team the following objectives have been achieved during the first period:

Task 1: Data generation. Both simulated FLEX/Sentinel data with associated variables, as well real data from campaigns have been collected. Regarding simulated data, physical models (radiative transfer models: RTMs) have been brought together within the ARTMO software framework (https://artmotoolbox.com/). Now both leaf, canopy and atmosphere RTMs can be coupled. For simulation of sun-induced fluorescence (SIF) data, the SCOPE model has been extended. All RTMs can be used by the developed post-processing toolboxes: 1) global sensitivity analysis (GSA); 2) scene generation module (SGM); and retrieval toolboxes. Latest version of ARTMO has been made freely available to the public.
Task 2: SIF & vegetation properties retrieval. Retrieval algorithms have been developed for both FLEX-FLORIS and Sentinel-3 (S3) OLCI data, and their synergy. The following algorithms have been successfully validated: leaf area index (LAI), leaf chlorophyll content (LCC), fraction of absorbed photosynthetically active radiation (FAPAR) and fractional vegetation cover (FVC). Also a SIF retrieval toolbox is under development.
Task 3: assimilation to GPP. A post-doc has been hired for this task. SIF data is being explored and exploited towards GPP using machine learning techniques.
Task 4: FLEX-S3 prototype vegetation productivity monitoring facility. This task has not been started yet. Nevertheless, we work closely together with the FLEX End-to-End (E2E) processing chain. Elements form E2E will be used to develop the monitoring facility.
Task 5: Integrating spatiotemporal methods. A time series toolbox has been developed with conventional and latest machine learning algorithms. Also a data fusion tool option has been added, e.g. for fusing FLEX and S3 data. This task is almost completely finished and applications and publications are underway.
Task 6: developments of applications. Within the ARTMO framework existing toolboxes have been improved and multiple new toolboxes have been developed: TOC2TOA, BRDFplot, LabelMe, DATimeS, FLUORT, ALG. See also https://artmotoolbox.com/ for details.
Based on these tasks multiple scientific publications have been published and several other papers are in the pipeline. See also: https://ipl.uv.es/sentiflex/index.php/publication

Final results

The following progess beyond the state of the art has been achieved:

1) Development of retrieval algorithms of vegetation properties for FLEX and Sentinel-3 OLCI. The added value of the sensor synergy has been demonstrated. A manuscript has been submitted to Remote Sensing of Environment
2) Development of a time series toolbox that includes latest machine learning methods. The toolbox further calculates phenological indicators. A manuscript has been submitted to Environmental Modelling and Software.
3) Devolopment of a remote sensing synergy toolbox where multiple time series data sources can be fused for an improved capability of filling up gaps. A manuscript has been submitted to Remote Sensing of Environment.
4) Development of toolboxes within the ARTMO software framework that has been provided to the community. Toolboxes include: retrieval of vegetation properties, scene generation, global sensitivity analysis, fluorescence retrieval.