## 3 Results and Discussion
The generic pattern observed is similar to the results when using the entire dataset, especially for the case of monodominance; nevertheless, the abundance of band combinations with R\({}^{2}\) > 0.5 are comparatively limited. A strong correlation can be observed for the combinations of the band 2 and bands of the NIR (6,7,8,8A) (\(0.71\leq\) R\({}^{2}\) \(\leq 0.78\)), with the ratio 2/7 having the highest correlation (i.e., R\({}^{2}\) = 0.78) and yielding the highest correlation for all the scenarios of this study, which is perhaps justifiable by the fact that only monodominant plots were taken into consideration in this subset of results. Figure 8 depicts the linear regression for the highest correlation of the monodominant plots scrutinized in this paragraph, as well as the highest correlation for the entire dataset; it is worth noting that there does not seem to be a specific species which performs better than others, which is perhaps attributable to assigning species-tailored allometric equations for the estimation of plot biomass. Figure 8: Linear regression of the best performing RSI and the plot biomass recorded in situ for the entire dataset (**a**) and for the monodominant plots only (**b**). The non-monodominant dataset depicts a similar spectral window for the Sentinel-2 band combinations; band 2 correlates with bands 6-8A (0.62 \(\leq\) R\({}^{2}\)\(\leq\) 0.69). Moreover, the non-monodominant dataset indicates the band candidate of Sentinel-1 VH and Sentinel-2 band 8 as a cross-satellite combination correlating to AGB (R\({}^{2}\) = 0.66), which is the exact same combination highlighted in the entire dataset scenario (Figure 5); this band combination, and actually any combination involving Sentinel-1 bands, does not appear in the cases of the monodominant samples in Figure 7, and it is a remaining scientific question whether or not the SAR information from Sentinel-1 is actually able to correlate more strongly with heterogeneous plots than homogeneous plots. In order to evaluate the relevance of our findings in the context of traditional methods, popular vegetation indices were estimated and similar statistics were extracted. Table 4 presents the results of the empirical indices and the correlations found. The best performing popular index was EVI (R\({}^{2}\) = 0.65) followed by ARVI (R\({}^{2}\) = 0.61) and mNDVI (R\({}^{2}\) = 0.60). Compared to the SI suggested in the current study, all popular vegetation indices underperformed. As a matter of fact, there were 7 band combinations identified in Figure 5 with an R\({}^{2}\) higher than the one of EVI and 19 band combinations with R\({}^{2}\) higher than the one of mNDVI. It is worth commenting on the band variation of these three best performing popular indices; EVI is based on the NIR, red, and blue bands; ARVI is based on the red edge, red, and blue bands; and mNDVI is based on two red-edge and blue bands. The only common denominator in these three indices is the use of the blue band, which was also found in the best performing RSI index that the current study suggests. Regarding the red-edge bands, the mNDVI relies heavily on two red-edge bands, and the VOG1 relies exclusively on a combination of different red-edge bands. These indices provided values of R\({}^{2}\) = 0.60 and R\({}^{2}\) = 0.55, respectively. The fact that biomass correlates sufficiently with a vegetation index that uses exclusively red-edge bands, such as the VOG1, is encouraging for the usefulness of the red-edge bands for vegetation trait retrieval. [PERSON] et al. (2019), in a study conducted at the same area, estimated AGB based on Sentinel-2 images and reported that the Normalized Difference Index 45 (NDI45), which is constructed from red and red-edge bands, correlated strongly with AGB compared to other indices (R\({}^{2}\) = 0.79). Other studies have also emphasized the importance of the red-edge band of Sentinel-2; for instance, [PERSON] et al. (2017) reported improvement of accuracy in estimating chlorophyll when including the bands in the red-edge region.