## 1 Introduction
### Specific Uses of S2 Bands  
The spectral bands of Sentinel-2 allow the computation of a large variety of spectral indices other than NDVI that are useful for different applications. Table 2 presents a selection of several of them. The RE bands have been proposed for chlorophyll estimation, burn severity assessment [7], LAI estimation [8] and non photosynthetic vegetation [9]. The SWIR bands have been proposed for dry mass vegetation [10] and water or moisture indices [11]. Although a thorough review of the literature is out of the scope of this paper, a bibliometric analysis shows that very few papers published after the launch of Sentinel-2 make an explicit use of the spectral particularities (RE and SWIR bands). Furthermore, a recent review about phenology monitoring using Sentinel-2 [12] shows that only one out of four published papers uses spectral information other than NDVI. Some studies, for instance [13], claim that RE and SWIR bands during vegetation senescence appear to be important for machine learning-based classification. The concept of importance has to be nuanced, since it measures the errors made when the reflectance of those bands are replaced by random values. In order to have an accurate assessment of the usefulness of those variables, the classifiers should be retrained without them. On theother hand, the same work shows that PSRI, which is computed from red, green and NIR, is also _important_, which may indicate a high correlation (and therefore redundancy) with RE bands. Another work supporting the interest of RE and SWIR bands is [16], where they are shown to be useful for gross primary productivity estimation in grasslands. Using regression approaches, the authors show that those bands are useful to predict the target variable, but do not study whether by using more complex regressors the error without those bands could be reduced. It is interesting to note that other works, for instance [17], show that NDVI is best suited to monitor grass phenology rather than more sophisticated VIs using RE and SWIR bands. Another example is [18], where it is shown that the RE bands of Sentinel-2 do not perform well for the estimation of chlorophyll content changes in certain crops. One should note that before the launch of Sentinel-2 the same community had great expectations for these bands for the same application [19]. However, at the time, the authors already suggested that using the green band in \(CI_{green}\) also seemed very promising and therefore further research was required. The apparent contradictions between these different works are likely due to the fact that different experimental settings, different data and different applications were involved. Further, we find that works on the usefulness of spectral bands are usually addressed only from the point of view of demonstrating that a particular phenomenon has a signature in a particular band. For instance, a recent publication [20] proposed additional SWIR bands in order to detect non-photosynthetic vegetation and crop residues. The study indeed shows that these objects cannot be detected with the SWIR bands of Landsat-8. However the cited work does not analyze how the complete set of Landsat-8 bands could be used to retrieve a signature of the phenomenon at hand. As of this writing and to the best of our knowledge, the most thorough study of the _usefulness_ of Sentinel-2's spectral bands is [21]. This reference is actually a detailed literature review of the use of hyperspectral imagery with the goal of proposing synergies with Sentinel-2 in order to overcome the limitations of space-borne hyperspectral sensors (spatial resolution, revisit time and signal-to-noise ratio). Interestingly, the review shows how the current set of Sentinel-2 bands constitutes in itself a very wise choice for many applications. However, the limit of such a meta-analysis is that there cannot be a holistic view of the problem, since the pertinence of each spectral range is performed in isolation in the reviewed literature.