## 6 Discussion
Comparing with classical techniques, that can only deal with accurate visualization of whole data sets when the number of features required is lower than three (e.g., RNDVI, INDVI, DNVI), we evaluated a high amount of variables that is a promising method of evaluating the effect of new attributes on ecosystem in the future. On the other hand, the SOM along with the _k-means_ technique made it feasible to classify behavioral patterns and organize behaviors in different classes according to functional type attributes. One of the main challenges using a SOM along with the _k-means_ technique is the definition of the number of clusters that will best represent the ecosystem. To minimize the problem associated with the random selection of the centroid in the _k-means_ algorithm, we used 100 runs. Data was patterned into 36 clusters that was gathered according to hierarchical agglomerative to facilitate summarizing results. However, this multi-level approach tolerates returning to a higher detail level if necessary. Each of the twelve clusters represented well, each individual characteristic in terms of the main functional attributes considered (_i.e._, LST, NDVI and Albedo). In compliance with Appendix, we could postulate the twelve clusters as individual functional entities. Moreover, results from other independent attributes related to climate and biomass (_i.e._, GPP and PET) also demonstrated the discrepancies among ecosystems (see results in Section 5.3) which confirms that they function differently. EFTs direct validation was a non-trivial task due to lack of ground truth or _in situ_ measurements. Moreover, the ground measures could not be not equivalent due to differences in how they quantify the functional information [95]. Hence, the assessment of the EFTs maps was carried out, as previous studies [13], by visual exploration of the map with other derived EFTs in the area and by comparing the EFTs map with land cover and ecoregions datasets. The resulting EFTs map was consistent with preceding studies presented in [29] and [36], revealing a similar spatial distribution that decreases gradually southwards and eastwards in terms of biomass production. Although, our characterization of the heterogeneity in ecosystem functioning goes a step further increasing the spatial resolution from 8 km (e.g., GIMMS, AVHRR) to 1 km (e.g., MODIS). This is a benefit, mainly in more fragmented and complex landscapes or intensively used areas, as Spain, where the coarse spatial resolution is limited for describing the spatial variability of vegetation [29]. Hence, a 1 km spatial resolution improves the identification of that phenomenon. Moreover, these studies only rely on the classification according to vegetation such as NDVI, whereas our EFTs also considers the contribution of energy balance resulting in a more comprehensive characterization of ecosystems. For instance, Galicia was classified by [36] as a mainly continuous ecosystem with high INDVI and low RNDVI (Da1). This result is coherent with a previous test obtained from our methodology using only NDVI variables. However, including additional information such as LST allowed complementing the description and variability of the ecosystem mainly in forested areas where NDVI saturates a high level. Thus, LST in this area acts as main force to characterize the ecosystems and represents their variability in this patchy area. To evaluate our EFTs we analyzed the relation between EFTs and land cover datasets and ecoregions. We therefore analyzed the relation between EFTs and land cover, with a correspondence analysis of 77.3% (two first axis), that as previous findings in a global framework [29], indicates that EFTs have potential in the monitoring of human influence on ecosystem functioning and in supporting degradation studios.