## 4 Discussion  
In the experiments carried out, satellite images captured by Landsat 8 were considered, and these images had to undergo pre-processing as described in Section 3. To simulate the presence of clouds, synthetic masks were added to the original images in order to evaluate the performance of the image reconstruction. The size of the masks characterized the scenarios for evaluating applicability: scenario 1 with thick masks, scenario 2 with medium masks and scenario 3 with thin masks. When comparing the data from the original reference image of Figure 11 with Figures 12 and 13, the results visually prove that the reconstruction carried out by the networks is adequate, as the ground truth image and those recovered by LaMa and Big LaMa have similar temperature (_LST_) and vegetation (_NDVI_) values. In the qualitative analysis, the scatter plots shown in Figures 14 and 15 describe that the recovery carried out by the Big Lama model is more concentrated, which means a higher level of consistency and precision in the reconstruction of the missing areas, highlighting its superior performance. Also, with Big Lama there is a greater strength of relationship between the variables and a higher coefficient of determination for scenario 1, representing the greater accuracy of the regression equation. This behavior may be due to its structure having more residual blocks, which may imply better efficiency in reconstructing missing areas. Analyzing the quantitative results for the reconstructed image, shown in Tables 7 and 8, the best performance was obtained in scenario 2, which is composed of medium-sized reconstruction areas. The _PSNR_ and _SSIM_ metrics indicate that the LaMa and Big LaMA approaches work well for treating satellite images with medium cloud cover. The _RMSEs_ achieved for scenario 2 and 3 differ marginally. In relation to _LST_, when analyzing the _SSIM_ metric, the LaMa network performed better for scenario 3, which corresponds to areas of thin cloud cover. Big LaMA performed better in scenario 2 when evaluating the _PSNR_  
Figure 16: Reconstruction of satellite images with cloud coverage, using LaMa and Big Lama. Figure 17: The original image with clouds and their respective _LST_ and _NDVI_. and _RMSE_ metrics. For _NDVI_, when analyzing _PSNR_ and _RMSE_, the results are better for scenario 3 in both approaches. _SSIM_ was again better for scenario 2. As a result, the visual quality of the reconstruction of images with a medium cloud cover size is higher than that of the others, which may be due to the greater preservation of contour information. As is to be expected, in scenario 3 the reconstructions were less penalized, with lower errors, due to the smaller number of pixels to be reconstructed. It is clear that are differences between the synthetic clouds and the real clouds, so experiments were carried out on images with cloud coverage. Figure 16 shows the image reconstruction results and their respective _LST_ and _NDVI_. It was found that the two algorithms achieved visually adequate results for the reconstructed image and its _LST_, but we can see from the _NDVI_ in the region where the largest cloud was located that there is a difference between the results from the LaMa and Big LaMa network. According to Table 1, when the intervals in _NDVI_ are between 0.4 and 0.6, this indicates a medium vegetation, and between 0.6 and 0.8 this indicates a dense vegetation; therefore, while examining the cloud region, the _NDVI_ can indicate a dense vegetation for LaMa and a medium vegetation for Big LaMa. This can be harmful to remote sensing analysis. Using the image with cloud coverage, without the reconstruction process, calculating the _LST_ and _NDVI_ indices can lead to an erroneous analysis. The differences in the _LST_ and _NDVI_ of Figure 17, compared to those of Figure 16, highlight the unrealistic information.