Actuaciones promocionales institucionales

7.4.1 Retrieval toolboxes

The retrieval toolboxes paved the path for various new mapping applications, pushing those fields into superior performances and new application opportunities. They have been made freely available to the broader remote sensing community. During the course of this work several suggestions have been identified to boost the field of vegetation properties mapping for-ward:

Spectral Indices toolbox:

 In Chapter 3, two-band vegetation indices according to SR and NDVI formulations have been optimized. However, the SI toolbox permits the development and assessment of new vegetation indices with all possible band combinations of up to 10 different bands.

A follow-up study with spectral indices on the basis of simulated Sentinel-2 data for all possible four bands has been conducted [Verrelst et al., 2013c], with optimized indices that pushed accuracies with a few pointsr²up. It is to be expected that new index formu-lations with more bands involved will further improve accuracies.

 Another path to be explored concerns the analysis of generic indices based on simulated reflectance data. In this thesis, optimized vegetation indices have been derived from the SPARC dataset. Given the absence of uncertainties, it remains however unknown how

7.4 OUTLOOK 123 general optimized models can be applied to other sites and conditions. For this reason it would be valuable to have indices analyzed on the basis of simulated reflectance data.

ARTMO has all the tools operational to undertake this research, not only with RTMs both at leaf or canopy level, but also for different vegetation types, i.e. 1D (e.g. SAIL) or 3D forest RTMs (e.g. FLIGHT).

LUT-based inversion toolbox:

 In Chapter 4, only 18 stand-alone cost functions out of more than 60 functions have been analyzed. The majority of cost functions require one or more parameters to be tuned, which in principle should lead to refined performances compared to the stand-alone cost functions. The inversion toolbox offers the possibility to loop over those parameters in or-der to seek for optimum. These features have not been explored yet, leaving opportunities for further optimizing the inversion scheme.

 LUT-based inversion relies on a large spectral database that have been generated by RTMs. Until now only PROSAIL has been applied to generate the LUT because of rel-atively few input variables involved (about 8). However, ARTMO enables the develop-ment of spectral databases originating from more advanced RTMs such as FLIGHT, SLC, SCOPE. While those models require more parametrization, once having them configured, running the subsequent inversion process is just the same.

MLRA toolbox:

 In Chapter 5, only six available nonparametric regression algorithms have been analyzed (PCR, PLSR, DT, NN, KRR, GPR). Meanwhile, several more novel MLRAs have been implemented into the toolbox, with some of them never being applied in the context of vegetation properties mapping, being bagging trees, boosting trees, extreme machine learning, relevance vector machine, variational heteroscedastic GPR. Recent results in-dicate that particularly relevance vector machine, variational heteroscedastic GPR and extreme machine learning are among the top performing regression algorithms [Verrelst et al., 2013c].

 Hybrid methods try to combine the generality of physically-based methods with the flex-ibility and computational efficiency of MLRAs. The idea is to learn the inverse mapping with a nonparametric model that is being trained using simulated data generated by RTMs.

This approach has long been restricted to training a neural net by simulated data from a RTM (e.g. PROSAIL), which has been proven successful in an operational processing scenes (e.g. GEOLAND products). However, given the recent advances made in the field of MLRAs, it can be questioned whether a neural network is really the best choice. In Chapter 5 it was demonstrated that novel MLRAs (e.g. GPR) tend to outperform neu-ral networks. A MLRA such as GPR, with the feature of providing uncertainties, holds promises to develop new generation of retrieval methods that can be implemented into operational processing chains.

7.4.2 Future work ARTMO: beyond the retrieval toolboxes

Coupling with atmospheric models

Although ARTMO is already at an advanced stage of development, currently the integration with an atmospheric model is missing. This means that only top-of-canopy (TOC) reflectance can be simulated. It also bears the consequences that an atmospheric correction has to be ap-plied when aiming to invert a RTM against remote sensing data. Alternatively, when having an atmospheric model implemented into ARTMO then canopy simulations can be upscaled to top-of-atmosphere (TOA) radiance. This holds the advantage that it would allow us to develop retrieval strategies of atmospheric and biophysical variables directly from the satellite observa-tions, i.e. without the need to correct for atmospheric effects. Potential atmospheric models to be implemented into ARTMO of a low to high complexity would be 6S and MODTRAN.

Efforts in coupling a MODTRAN-generated LUT with an ARTMO-generated LUT are already underway, e.g. as part of a scene generator module.

Scene generator module (SGM)

In support of ESA’s Earth Explorer 8 candidate mission FLEX (FLuorescence EXplorer), a scene generator module (SGM) is currently under development within the ARTMO environ-ment [Rivera et al., 2014b]. Essentially, the SGM is in charge of simulating the scene to be observed by an instrument. A scene is understood as a TOA radiance map, e.g. as observed by a satellite mission. The generation of these synthetic scenes includes the distribution of bio-geophysical and atmospheric parameters over the scene map. In addition, the SGM takes also into account environmental conditions such as surface topography and observation/illumination geometry. All these parameters and environmental conditions serve as input for the genera-tion of TOA radiance maps through the use of canopy RTMs (as those provided by ARTMO) coupled with an atmospheric model (e.g. MODTRAN) or from external radiometric data (e.g.

reflectance, fluorescence and/or TOA radiance spectral databases or external image files). The SGM will ultimately become part of a complete End-to-End FLEX mission performance simu-lator software, that is being developed in collaboration with partners and industry. At the same time, a simplified but more generic version of the SGM (i.e. applicable for any optical sensor) is foreseen to be implemented as an ARTMO tool.

Global sensitivity analysis (GSA) tool

Another ongoing study within the ARTMO environment is the development of a global sensi-tivity analysis GSA tool. Sensisensi-tivity analysis evaluates the relative importance of each input parameter and can be used to identify the most (and least) influential variables in determining the variability of model outputs. In contrast to a local sensitivity analysis that evaluates one factor at a time, a GSA explores the full input parameter space, i.e., all input parameters are tested together. In support of FLEX an advanced RTM that also provides fluorescence outputs,

7.4 OUTLOOK 125 i.e. SCOPE, has recently been analyzed using GSA [Verrelst et al., 2014a]. In this way the driving variables determining variations in reflectance and fluorescence outputs have been de-rived. Apart from these FLEX-related activities, it is foreseen that in the near future a fully operational GSA toolbox will be implemented into ARTMO that is able analyzing all available RTMs, as well enabling sensitivity analysis of coupled RTMs, e.g. leading to the unraveling the radiative transfer of a complete soil-leaf-canopy-atmosphere system.

Remaining forthcoming activities and final considerations

Finally, apart from the above-mentioned ongoing activities, the modular design of ARTMO opens opportunities to implement new RTMs, tools and toolboxes as plug-ins into the existing framework. To stay within the context of vegetation properties mapping – but in principle ap-plications can go in any direction – the following activities are planned: (1) a classifier toolbox, (2) time series toolbox, (3) unmixing toolbox, and (4) data assimilation toolbox.

Ultimately these activities will not only facilitate the use of advanced remote sensing tech-niques to a broader community, the developed tools and toolboxes will also boost progress towards: (1) an improved understanding in the interactions between light and vegetation proper-ties, (2) improved retrieval algorithms serving forthcoming optical missions such as Sentinel-2 -3, EnMAP, FLEX, (3) new mapping applications to understand better our changing Earth.

For instance, currently no operational global LCC mapping algorithm exists, which prevents this variable to be considered as an essential climate variable (ECV). Currently only absorbed photosynthetically active radiation (FAPAR) is considered as an ECV [GCOS, 2014], and often used as a substitute of LCC because of closely related with LCC [Gitelson et al., 2003] and carbon assimilation [Sellers, 1987; Sellers et al., 1992]. However, FAPAR is a component of the land-surface radiation budget and also influenced by recording time and direct versus indirect/diffuse solar radiation. The robustness of FAPAR as an indicator of LCC may therefore be questioned and more robust retrieval schemes are desirable. An operational LCC retrieval algorithm can be developed within ARTMO, e.g. based on the one presented in Chapter 6.

A precise knowledge of LCC and LAI is also indispensable in view of the FLEX mission in order to properly calculate and interpret solar-induced chlorophyll fluorescence and relate it to photosynthesis activity of vegetation [Verrelst et al., 2013a]. Furthermore, ARTMO can serve improved or new vegetation products, which in turn can serve as input into assimilation procedures for higher level products such as plant traits, photosynthesis, plant diversity; e.g. by coupling with Dynamic Global Vegetation Models or General Circulation Models. Altogether, these activities will eventually open new avenues to improved local-to-global (agro)ecosystem processes monitoring.