## 4 Discussion  
This study has approached the mixed pixel effect in two different ways: a laboratory approach (greenhouse) and a full-scale approach with a field experiment. Two experiments presented consistent results, showing the same pattern of NDVI reduction. The gradual reduction in NDVI showed in Figure 5b is expected because the leaf area within the pixel is reduced and NDVI is sensitive to changes in the vegetation [15]. There is an inconsistency in the pixel 1 trend, probably because the vegetation of the adjacent vines was moved and therefore the horizontal leaf area exposed to the satellite increased, although it could also be due to a variation in the reflectivity collected by the Sentinel in that specific image, since, if an analysis is performed using the image of 6/7/2019 corresponding to the Sentinel-2B satellite, an increase in NDVI can be observed in all the pixels of the tile. The influence of humidity in the soil is disregarded [58] since there was no significant rainfall in the period.  
In the field experiment, initial NDVI values were between 0.25 and 0.32 with final values between 0.17 and 0.19. The starting NDVI values correspond to the maximum vine cover and indicate a different level of vigour in the selected pixels (Figure 5b). The final NDVI values correspond to bare soil and are consistent with the results of other authors [47]. It has been observed that the reduction in the NDVI value is proportional to the loss of TLA, finding that in the greenhouse experiment and the field experiment the slopes of the regression line were very similar. Therefore, if the vegetation within the pixel is reduced by 20%, the NDVI will be reduced proportionally by 6-7%. This also indicates that, considering the components of a mixed pixel [46], the vegetation and the associated shade effect 30-35%, so the remaining value is being influenced by the soil. These results are similar to the values reported by other authors [48], which indicate that the effect of the soil can be up to 3 times that of the vegetation.  
If the field dataset is disaggregated per pixel, the difference between pixels is clearly observed (Figure 6b). This could be due to differences in vigour within the vineyard, since the vigour of the vineyard is related to the NDVI values [39; 40]. Another important aspect to consider is the orientation of the row regarding the position of the Sentinel-2 pixels. The orientation combined with the space between rows creates an irregular grid effect. Therefore, if the orientation is not perfectly aligned with the pixel position, the number of vines per pixel is irregular. In our case, this effect was clear, with values of 38, 37 and 39 vines for the pixels 1, 2 and 3, respectively.  
The trend lines presented in the results have very high R\({}^{2}\) values, however, these lines were just made in order to observe the trend and to highlight the relationship that has been shown, not to establish a model, since they are strongly influenced by the extreme values (0% and 100% of reduction), and it would be desirable to have a greater amount of data to develop a model. It is also important to note that there may be misregistration of pixels, which are improved regularly by updating the Processing Baseline, so in future studies the algorithm might be ameliorated, allowing more accuracy in the process [59].  
Looking at the results of the \(t\)-test, although the TLA was significantly reduced between each removal step, the differences in the NDVI were not significant until the amount of vegetation was reduced with two removal steps. For example, there are differences between the first and the last removal, and between the second and the last, but there is no difference between the third and the fourth. This indicates that, although the NDVI is affected by the reduction of TLA, it is not overly sensitive to small reductions in the vegetation amount.