## 1 Introduction
Satellite remote sensing is the only practical means by which land surface biogeochemical variables can be obtained at the temporal and spatial scales required for global and regional use [30]. Several time series of remotely sensed images are available at coarse and medium spatial resolution. In particular, Normalized Difference Vegetation Index (NDVI)-based time series are fundamental to the remote sensing of vegetation phenology and to extract numerical observations related to vegetation dynamics [27]. However, NDVI has shown to saturate over forested regions, while being sensitive to canopy background variation in arid and semi-arid areas [32]. Therefore, additional information is required to complement NDVI information and compensate for this weakness [33]. Nevertheless, a few attempts have been made to integrate additional information into ecosystem monitoring, mainly including indicators of energy partitioning into sensible and latent heat fluxes and light reflection [34]. Accurate and efficient EFTs mapping via remote sensing depends largely on the selection of an appropriate classification approach [8]. Different classifiers may produce different results even when the same functional variables are used [35]. Many classifiers have been proposed to delineate EFTs such as ISODATA [23], CLARA [34] and partitioning based on thresholds [16, 36]. A promising alternative to these methods is the self-organizing map (SOM) [37]. The SOM is an efficient way to derive maps from multi-dimensional and complex data. This method allows showing the data in a more comprehensive fashion and in fewer dimensions [38]. The detection of useful knowledge (in the form of patterns, structures and relationship) within the raw data is enhanced. Furthermore, the SOM method has overcome some of the limitations of the clustering methods since (i) they are more robust and are not significantly affected by missing data, (ii) do not require any prior assumption about the underlying distribution of the data, (iii) are capable of generalizing in noisy environments and (iv) make it possible to learn complex patterns in a limited time [39] with a decreased computational cost, also being an appropriate tool when the sample size is large [40]. Although this method has been shown to be more successful than the above mentioned algorithms to monitor and classify land cover [41] and has been previously used in ecology [42, 43] and remote sensing [44], the potential of this methodology has not yet been applied to delineate EFTs. The aim of this study is to identify EFTs in Spain for the period 2000-2009 as areas exhibiting similar responses to environmental conditions and similar ecosystem processes. The remarkable landscape diversity in Spain makes this area a suitable region to identify the EFTs. This work follows preliminary investigation on the same topic [16], which revealed the interest and potential of the EFTs classification encouraging the authors to further in-depth research. The current extended study uses images of higher spatial resolution (1 km) compared with previous studies (e.g., [23]), and a robust methodology based on a two-level procedure, based on the combined use of the SOM and _k-means_ clustering method to derive a map of EFTs definition. This method also allows expanding the number of variables involved in the definition of the EFTs to the extended use of the NDVI, and evaluates its performance. Additionally we analyze, for the different EFTs seasonal patterns and the correspondence between functional structural descriptors (_i.e._, land cover) and coregions with the derived ecosystems.