## 4 Discussion
### Radiometric Consistency  
As already stressed by many authors in the literature [28; 29; 30; 55], radiometric corrections are necessary when using sensors mounted on UAV for PA, but the ease of use and diffusion is limited. The radiometric processing available in Pix4 Dmapper software for the Sequoia camera provides most of the corrections, including vignetting, dark current, exposure time, and sunlight irradiance, but omits other possible causes of radiometric variations [23]. First, this research points out that radiometric inconsistencies due to differences in the acquisition geometry remain unsolved. Reflectance values of pixels at the borders of the blocks tend to be overestimated, as a consequence of the inclination of the point of view during the photogrammetric survey. From Figure 9, it is evident that differences are not uniformly distributed, but present a clear spatial pattern. Higher difference values (absolute values) are measured at the borders of the overlapping area, while the lowest values approach the center of the area. This demonstrates the presence of a high edge effect on the reflectance maps, which must be considered during flights planning. In practical use, it is advisable to plan UAV surveys covering an area wider than the one of interest. Enlarging the survey area should guarantee uniformity in the acquisition geometry even in the edges, otherwise characterized by non-negligible radiometric distortions. Radiometric differences are not affected by different geometric processing of the blocks, as confirmed by the similar values of the differences computed for Ig/Ir and Jg/Ir scenarios for both reflectance and VI maps (Tables 4 and 5). As can be noted from RMSE values reported in the tables, differences calculated for some VI maps are lower than values obtained for reflectance maps, meaning that some indices can decrease inconsistencies of single reflectance bands [56]. Considering the Ig/Ir scenario, the differences are moderate with respect to Ig/Ir scenario for both reflectance and VI maps, with mean values overall close to zero. The edge effect is also still evident from the spatial distributions shown in Figures 10 and 11, however with lower values, as is evident in the box plots in Figures 7 and 8. As a matter of fact, it should be recalled that the Ig/Ir scenario corresponds to the procedure recommended by Pix4 Dmapper software to process large photogrammetric blocks. For adjacent blocks acquired with separate but temporally close flights, the recommended merging option can partially correct the effect of illumination geometry and mitigate radiometric inconsistencies in the overlapping areas between blocks. There are still uncertainties regarding the obtained absolute values of reflectance and for the derived indices [56], and consequently in the quantitative use of the Sequoia data for the possible calculation of biophysical parameters. From the results reported in this study, it should be noted that in some areas, differences have values close or even larger than 0.2 (absolute value). Therefore, the different processing scenarios have an impact on the results in terms of radiometry. A difference of this magnitude cannot be neglected in the operational phase for precision agriculture applications; even more so if used for multitemporal surveys. As a matter of fact, the map that shows the most homogeneous values in all cases is NDVI, which is widely used in most agriculture applications [57; 58; 59; 60]. Regarding the comparison with S2, which is limited in this paper to NDVI, it should be mentioned that despite the analysis being affected by the different geometric resolutions of sensors and acquisition platforms, a significative correlation is found between Sequoia and S2 maps. Following the approach of [52], the [PERSON]'s correlation coefficient can be adopted as a map similarity measure.