## 4 Space-Based Crop Mapping  
### Scientific Background  
Spaceborne remote sensing is generally more efficient and effective than traditional methods, like field surveying, in describing the Earth surface. This is due to its ability to map and monitor the spatial distribution of land cover continuously and consistently at a variety of spatial and temporal scales, even though it may come at the expense of slightly worse thematic accuracy, depending on the approach used [6]. Several efforts have been made to map land cover at a global scale; some examples include: IGBP DISCover [7], UMD Land Cover [8], and Global Land Cover 2000 [9]. At a regional scale, the GMES, then Copernicus, initiative in Europe produced the Corine Land Cover (CLC) [10] map reporting land cover on the European continent, made publicly available on the Copernicus Land repository [11]. This is all but easy, given the absence--until recent times--of well-registered multi-temporal datasets, skills and processing power [12]. Focusing on vegetation and crop mapping, the problem gets more difficult [13] given the stronger similarity in spectral response among all vegetated species with respect to a situation where general categories of land cover are to be discriminated. Moreover, a high revisit frequency is needed in many areas, in order to maximize the chances of getting cloud-free data. In addition, the growing cycle of vegetation in agriculture needs fine temporal resolutions to be able to separate crops that are similar in spectral response but show temporal differences in their phenological cycle [14]. This concept has been around for several years now [15], but the difficulty of the problem (possibly also together with the scarcity of data) made research efforts to focus on detection of very few classes [16]. Even with the recent research progress  
Figure 1: The flowchart of the proposed methodology. The box in the top left represents the output of the satellite-based mapping of homogeneous areas. Each homogeneous area is labeled with a number, but no specific crop class. The box in the top right represents the output of agro-climatic mapping, where each agro-climatic areas is associated with several possible crops, compatible with the local environmental conditions. None of these two outputs alone can directly translate into a crop map. The blue dot in the middle represents the fusion method described in Section 6, which leads to an actual, full crop map.  
favoured by the increasing richness of spaceborne data available, the problem of discriminating several different types of crops is still regarded as a difficult one [17]. MODIS [18; 19; 20] data and, at a finer spatial resolution, SPOT [21] data have most commonly been used for this purpose, whereas very high resolution (VHR) data is practically absent from the crop mapping context probably due to a combination of large geographical scale and high per sq. km cost of data. In our case, rather than attempting to directly classify the crop type with all the consequent issues pointed out above, we took a different approach, made possible by the availability of formalized environmental constraints on crop types (see Section 5). We indeed focussed on the simpler problem of finding homogeneous areas, defined as contiguous areas displaying homogeneous behaviour in multitemporal series of multispectral spaceborne optical data.