## 4 Discussion
### Forest Canopy Height  
The results shown in Figure 10(a) suggest that with a temporal span of 48 days, \(|\gamma|\) at L-band seems to suffer from large temporal decorrelation, as indicated in Section 2.1, and this applies both to Corrientes and Neuquen sites. However, when selecting shorter temporal baselines for both instruments and sites, things look quite different. Figure 10(b) shows scatter plots for 12-day interferograms in the case of Sentinel-1, and 8 to 16 days in  
Figure 16: Scatter plot of \(B_{\perp}\) and \(B_{temp}\) vs. \(|\gamma|\) for SAOCOM-1 for region C, Figure 7. The points are labeled with the temporal baseline value to illustrate the effect of \(B_{\perp}\).  
Figure 17: Histograms of \(|\gamma|\) for SAOCOM-1 at 8 and 16 days separation in region C, Figure 1(a).  
the case of SAOCOM-1. While for Sentinel-1 the variation of the coherence with respect to the forest height is limited, in the case of SAOCOM-1 the system seems to be much more sensitive to this variable. In the Neuquen site, where the average height measured by GEDI is around 16.5 m, the coherence is higher than in Corrientes, where the mean canopy height is 24.5 m. The forest canopy height does not only vary between the two sites, but also within the Corrientes one. If we observe the histograms shown in Figure 17a,b of Section 4.4, the coherence values correspond to region C zoomed in Figure 2a, which has a mean canopy height of 24 m. According to the GEDI data, the other forested area analyzed in this work, region A in Figure 2a, has a mean canopy height of 14 m, and the histograms of coherence show higher values than the C one, as can be seen on Figure 18a,b.  
Following the analysis proposed in [51], we plotted a scatter plot comprising the relation between all the observed coherence values coming from 8 and 16-day interferometric pairs, and the ratio between GEDI canopy height and the Height of Ambiguity, namely \(h/HoA\), see Figure 19. The reason for using this metric is that the variability introduced by different spatial baselines is mitigated when using this ratio rather than the height itself. On the same Figure, we have included a curve corresponding to the RVoG volumetric decorrelation model, also introduced in [51], taking the mean \(h/HoA\) value for all the pairs. When fitting this curve, we also compensated for the temporal decorrelation that affects these observations, by applying Equation (9).  
The observed result is quite interesting, since although the \(h/HoA\) values do not cover the full numeric range needed to fit a model-based curve such as the RVoG one, the scatter plot effectively accounts for a non-linear negative relation between the ratio and \(|\gamma|\). Bearing in mind that we are working with an orbital mono-static SAR system--unlike a bi-static one like the data set presented by [51], this relation deserves further analysis with a longer and more baseline-heterogeneous InSAR time series.  
These results are relevant as they indicate that SAOCOM-1 L-band coherence is related to the canopy height, which is essential for the implementation of inversion models such as the RVoG considered in PollInSAR [7; 14]. It is also important since it proves that working at L-band with short temporal baselines is critical for this type of application. Further analysis needs to be performed regarding this point, especially assessing whether the volumetric coherence estimated by the RVoG model fits the distribution of the observed data. However, this requires all data points to come from the same pair and the same interferometric configuration, and this is not the case of Figure 11b. An alternative to this constraint could be to adopt a multi-baseline approach such as the one proposed by [17], in order to exploit the variability in baselines and choose the one that best fits each target's height.  
Figure 18: Histograms of \(|\gamma|\) for SAOCOM-1 at 8 and 16 days separation in region A, Figure 2a.