## 4 Discussion  
### Geometric Consistency  
Geometric consistency was evaluated by computing the distance between the whole dense point clouds for the DG, Ig/Ir, and Jg/Ir scenarios. The Ig/Ir scenario was disregarded because although it is interesting for the study of radiometry, it is based on the extraction of a unique, common point cloud, and therefore there is nothing to check. Thanks to the high numerosity of the samples (the used clouds have between \(10^{5}\) and \(10^{6}\) points), we obtained very significant statistical figures. In addition, we could perform a sort of continuous evaluation of the distance between the overlapping surfaces.  
The datasets considered are quite challenging for ICP, as the terrain is quite flat and the only height variations are due to medium-sized streams. Nevertheless, our developed algorithm proved to be reliable; indeed, we performed the closure check by composing direct and inverse transformations and obtained residual parameters (shift components and rotation angles), which are limited in size  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **S2** & **3 Ig/Ir** & **4 Ig/Ir** & **Ig/Ir** \\ \hline \multirow{4}{*}{NDVI} & Min. & 0.3197 & 0.0998 & 0.0764 & 0.0221 \\  & Max. & 0.7787 & 0.8731 & 0.8926 & 0.8946 \\ \cline{1-1}  & Mean & 0.5583 & 0.6262 & 0.5945 & 0.6119 \\ \cline{1-1}  & STD & 0.0906 & 0.1323 & 0.1331 & 0.1324 \\ \cline{1-1}  & RMSE & - & 0.1140 & 0.0984 & 0.1090 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary statistics of the NDVI maps computed from S2 imagery and Sequoia imagery, in the overlapping area.  
Figure 12: Scatter plot and regression line for NDVI maps computed on S2 imagery with respect to Sequoia imagery: 3 Ig/Ir (**a**), 4 Ig/Ir (**b**), Ig/Ir (**c**). For each graph, the coefficient of determination (R\({}^{2}\)) and the Pearson’s correlation coefficients (\(\varrho\)) are reported (\(p\)-value \(<2.2\times 10^{-16}\)).  
and equivalent to the granularity of the considered point clouds, having an average distance of around 12 cm (see Section 3.1.1).  
Cross checks of overlapping point clouds can be usefully applied when no or few CPs are available. Moreover, this allows detection of fine-grained deformations. Scenario DG is related to direct georeferencing, performed through the measurements of the Sequoia's on-board GPS receiver. Results are poor, not surprisingly, as the average distance between the clouds is above 3 m. The point sets are significantly shifted and rotated. This result was largely expected, but we think it is worth mentioning to warn newer photogrammetrists. Such georeferencing precision is not acceptable, even for precision agriculture applications. In Ig/Ir and Jg/Ir scenarios, bundle block adjustment was performed by means of 4 GCPs. Similar results are shown for both scenarios. Indeed, RMSE values of the components of the point-wise shift vectors are within 10 cm. It is the order of magnitude of the granularity of point clouds and of the pixel size of the generated orthomosaics. Such figures assure us that no difference or negligible radiometric differences are induced by geometric consistency issues.  
In summary, two main findings come from geometry assessment: we have been able to reliably estimate a rigid 3D transformation by robust ICPs between almost flat point clouds; and we have demonstrated that the geometric consistency is good, so that the inconsistencies shown by radiometry have a different origin.