## 1 Introduction
### Related Work  
Despite the significant progress in modern inpainting systems, there are still difficulties when working with high-resolution images and when large areas need to be reconstructed. In the paper [16], the authors proposed two methods, one called LaMa (Large Mask Inpainting) and the other Big LaMa, both based on fast Fourier convolutions (FFCs), with a high receptive field perceptual loss and large training masks, differing only in their dimensions and resources. These models use the fast Fourier transform to perform reconstructions of areas covered by masks in images. The results presented show good performance when using inpainting with high-resolution images and robust behavior with large masks, which represents an excellent alternative for use in the recovery of satellite images in the presence of large areas occupied by clouds.  
On the other hand, the S-NMF-EC algorithm is proposed by [1], where the information covered with clouds, and the shadows that accompany them, is retrieved using a non-negative matrix factorization (NMF) and error-correction method (S-NMF-EC). First, based on the spatio-temporal fusion model STNLFFM, the temporal difference between the cloud-free reference image and the target image contaminated with clouds is reduced. Subsequently, the cloud-free information of the target image is kept to a minimum by error correction, thus carrying out the reconstruction. The following indicators were used to evaluate the method's cloud removal performance: peak signal-to-noise ratio (\(PSNR\)), correlation coefficient (\(CC\)), mean absolute error (\(MAE\)) and mean relative error (\(MRE\)).  
Following the approach proposed in [17], with the aim of measuring the temperature and vegetation of the Earth's surface from multispectral images, ref. [18] uses the \(LST\) and \(NDVI\) indices. The authors of this paper, in [19], carry out a study to determine heat islands and their relationship with vegetation on land using satellite images as well as the \(LST\) and \(NDVI\) indices.  
Regarding the quantitative evaluation metrics, the works by [2; 4; 6; 7; 20] also adopt the indicators structural similarity (\(SSIM\)), square root of the average relative global error (\(ERGAS\)), root mean squares error (\(RMSE\)), normalized root mean squares error (\(NRMSE\)), spectral angle mapper (\(SAM\)) and universal image quality index (\(UIQI\)) to evaluate the performance of satellite image cloud removal methods. The work by [21] highlights the usefulness of the SSIM indicator in the phenoms related to the visual quality of the object's structural details.  
This article presents an approach that uses the LaMa method to recover lost information in large regions due to cloud covers in satellite images of the Amazon rainforest. As a complementary part, the Big LaMa model will also be included, as this model is a scalable version of the original model. Reconstructed images will be used to estimate the normalized difference vegetation index (\(NDVI\)) and the land surface temperature (\(LST\)). Experimental results compare the calculated indices in the original images (without the presence of masks) with those reconstructed after the introduction of synthetic clouds (masks). For this purpose, a database called Pamazon-Cloud is generated, in which the data are composed of satellite images of the city of Rio Branco in the Amazon rainforest of Brazil. In order to quantitatively validate the accuracy of the approach, the \(PSNR\), \(RMSE\) and \(SSIM\) are used, which are metrics that can comprehensively evaluate reconstructed results in terms of content and structure. For qualitative analysis of image reconstruction, its \(LST\) and \(NDVI\) scatter plots were generated. Finally, experiments were carried out to recover information in satellite images with the presence of real clouds, using the CFMask segmentation algorithm to detect clouds in the reference images.