Task 1: Data generation. Both simulated FLEX/S3 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/(odnośnik otworzy się w nowym oknie)). For simulation of 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. As such, the ARTMO toolbox was used for: (1) generation of training data for development retrieval models, (2) generation of test synthetic scenes, and (3) emulation of SIF for fast production of SIF images.
Task 2: SIF & vegetation properties retrieval. Retrieval algorithms have been developed for both FLEX-FLORIS and 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. It led to several publications. Currently, retrieval models have been implemented into Google Earth Engine (GEE) for S3 data processing at the European scale. First European maps have been generated.
Task 3: assimilation to GPP. A post-doc has been hired for this task and he was preparing a manuscript about the topic. Unfortunately, he left the team within one year. However, he continues to work on the manuscript in his free time. Alternatively, as outlined in the proposal, recently we have teamed up with the groups working with the dynamic vegetation models BETHY and CCDAS for achieving assimilation towards GPP.
Task 4: FLEX-S3 prototype vegetation productivity monitoring facility. This task is slowly getting shape. Although not explicitly stated in the proposal, based on positive experience, we decided to develop this prototype vegetation productivity monitoring facility into GEE. A PhD student is dedicated to this task. The running of vegetation properties retrieval is already in place. We currently work on gap filling of S2 and S3 time series, which will enable to calculate phenology indicators (see also next task). A final step is to develop a regression model to enable productivity estimation.
Task 5: Integrating spatiotemporal methods. A time series toolbox called DATimeS has been developed with conventional and latest machine learning algorithms. It enables gap-filling and phenology indicators calculation. Also a data fusion tool option has been added, e.g. for fusing FLEX and S3 data. This research line led to several publications. We are now working on integrating the time series processing models into GEE to enable operational and larger-scale processing. Eventually we aim to be prepared for vegetation productivity processing at the European scale.
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, FLUOR, ALG, MLCA. See also
https://artmotoolbox.com/(odnośnik otworzy się w nowym oknie).
Each of these tasks led to several papers and several more manuscripts are in preparation.
