## 6 Discussion  
Throughout the last decade, the role of ecosystem functioning in both environmental management and biodiversity conservation has significantly increased [92]. Ecosystem characterization results in a useful tool for several tasks, such as direct evaluation of ecosystem services, selection of protected areas, planning and monitoring ecological restorations among others. Usually, the characterization of EFTs relies on estimators of the seasonal dynamics of carbon gains, such as the NDVI, EVI, fAPAR that have been proven to be useful parameters to characterize the ecosystem diversity [36]. Nevertheless, remote sensing provides additional functional variables to complement ecosystem functioning descriptors [93]. Hence, in this article we expanded the concept of ecosystems, adopting a more comprehensive method that extends the traditional concept of EFTs [23] and incorporates other variables related to energy partitioning into sensible and latent heat fluxes and light reflection that have only been tested in a local framework [34]. Moreover, we include climatic  
Figure 10: Correspondence analysis between Ecosystem Functional Types (EFTs) and (**Left**) ecoregions (SISW: Southeastern Iberian Shrub and Woodlands, SISM: Southwest Iberian Mediterranean Sclerophyllous and Mixed Forest, ISSF: Iberian Sclerophyllous and Semi-deciduous Forest, CMF: Cantabrian Mixed Forest, PCM: Pyrenees Conifer and Mixed Forests, NIMF: Northwest Iberian Montane Forest, ICF: Iberian Conifer Forest, NSMF: Northeastern Spain and Southern France Mediterranean Forest) and (**Right**) hybrid land cover (CUL: Cultivated and Managed areas, IRR: Irrigated Areas, MOS: Mosaic of cropland/natural, NEED: Needleleaved Forest, BROAD: Broadleaved Forest, MIX: Mixed Forest, SHR: Shrubland, HER: Herbaceous, SPA: Sparse Vegetation and BAR: Bare areas). variables, such as precipitation, that have previously been demonstrated to be a regional driver on the function of ecosystem [94], although they have never been included in the definition of EFTs. In fact, results from a Wilk's lambda analysis to determine which variable played the greatest role in the formation of the EFTs, revealed that climatic variables such as the Moisture Index (_i.e._, Wilk's lambda 0.25) presented the main contribution in identifying the ecosystem functioning. Our findings support and reflect the outcomes of various studies addressing water-limited ecosystems, in which rainfall determines plant water stress and affects ecosystem function, renders it vulnerable to degradation and reduces its productivity [4]. The difference between maximum and minimum NDVI (RNDVI) revealed the lowest contribution in the definition of EFTs (_i.e._, Wilk's lambda 0.79). This is in agreement with [24] that also found a low weight of this variable due to the bioclimatic control on the NDVI signal in the Mediterranean Iberia that underlies the variation in ecosystem responses observed. The identification of EFTs and the analysis of the functional attributes by a SOM and _k-means_ in this study highlights the power of this technique for interpreting highly functional variables and for identifying EFTs. Although this method has become a focus of particular interest across various ecological studies and has been applied successfully, as far as we are aware, this is the first time that SOMs have been applied to remotely sensed data to investigate ecosystem function. Therefore, this approach demonstrates an important new methodology for ecosystem characterization and analysis. The SOM was used as a visualization tool to identify patterns in the dataset, allowed us to elucidate the interplay and relationships among difficult variables such as the functional types, where numerous biological and environmental factors are involved in a complex manner. For instance, high LST encompasses a high Aridity Index (Figure 5). This method allowed us to deal with a large amount of variables demonstrating its effectiveness in the handling of a large amount of variables.