## 5 Conclusions  
Applications that analyze satellite images of indices such as _NDVI_ and _LST_ depend fundamentally on the seasons, i.e., they are limited to carrying out investigations in specific periods due to the presence of cloud cover blocking substantial information that can cause inconsistencies in the analysis. The innovation in the approach presented in this work is that only a single image is needed to reconstruct the areas covered by clouds in the image. It also contributes to future research into satellite image analysis that faces the same problem, since when using the proposed approach it may be possible to carry out evaluations in any season of the year.  
This paper presents the reconstruction of missing parts of the Earth's surface in satellite images with cloud cover, for the subsequent determination of the region's vegetation and temperature levels (_NDVI_ and _LST_). As a result, a dataset was created containing images of the study area, selected with as few clouds as possible. The dataset was used to train and test the LaMa and Big LaMa networks, with artificial masks representing the clouds and specifying the areas to be reconstructed. The masks represent three aspects of clouds, characterizing three scenarios: synthetic clouds covering thick, medium and thin areas. For the images that underwent the reconstruction process, the metrics _PSNR_, _SSIM_ and _RMSE_ were calculated in order to analyze whether the reconstruction was successful.  
From the qualitative analysis carried out, it can be concluded that the Big LaMa model shows superior performance when compared to the results obtained by the LaMa network in the reconstruction task. On the other hand, a quantitative analysis shows minimal superiority for the metrics achieved by LaMa. From the analysis carried out, it can be seen that the reconstruction, and its respective _LST_ and _NDVI_ calculations, show an advantage in the values achieved in scenario 2.  
Due to the differences between synthetic and real clouds, experiments with real cloud coverage were carried out, and excellent visual results were obtained, even more when compared to the alternative without the use of the implemented approach. This highlights the importance of using the image reconstruction process to analyze terrestrial features for remote sensing. When comparing the results achieved in terms of _NDVI_ and _LST_ for the reconstructed images, with real cloud coverage, the visual quality with Big LaMa is visibly better.  
Conceptualization, E.B. and A.B.A.; methodology, E.B. and A.B.A.; software, E.B., S.M., D.A.U.-F. and W.I.P.-T.; validation, E.B., S.M., D.A.U.-F. and W.I.P.-T.; formal analysis, E.B. and A.B.A.; investigation, E.B.; resources, E.B., S.M., A.B.A., D.A.U.-F., W.I.P.-T. and F.P.-Q.; data curation, E.B. and S.M.; writing--original draft preparation, E.B.; writing--review and editing, E.B., S.M., A.B.A., D.A.U.-F., W.I.P.-T. and F.P.-Q.; supervision, A.B.A. and F.P.-Q.; project administration, A.B.A. and F.P.-Q. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the PAVIC Laboratory, University of Acre, Brazil.  
Not applicable.  
The database used in this paper is PAmazonCloud, more information can be found in [[https://github.com/emilibezrera/cloud_painting.git](https://github.com/emilibezrera/cloud_painting.git)]([https://github.com/emilibezrera/cloud_painting.git](https://github.com/emilibezrera/cloud_painting.git)), accessed on 13 October 2023.  
The research was product of the collaboration between PAVIC Laboratory (Pesquisa Aplicada em Visao e Inteligencia Computacional) at University of Acre, Brazil and LIECAR Laboratory (Laboratorio Institucional de Investigacion, Emprendimiento e Innovacion en Sistemas de Control Automatico, Automatizacion y Robotica) at University of San Antonio Abad del Cusco UNSAAC, Peru. The authors gratefully acknowledge financial support from the PAVIC Laboratory.  
The authors declare no conflict of interest.