Assessment of Landsat and Sentinel images integration for surface water extent mapping
The presence of clouds and shadows and the low temporal resolution of the sensors can be limiting factors for several analyses using satellite images. Considering this, the present study aimed to verify if the integration of the MSI (Sentinel/2) and OLI (Landsat/8) images, aiming at the expansion of...
| Principais autores: | Queiroz, Heithor Alexandre de Araújo, Brito, Camylle Vitoria Oliveira |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Universidade Tecnológica Federal do Paraná (UTFPR)
2022
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| Acesso em linha: |
http://periodicos.utfpr.edu.br/rbgeo/article/view/14590 |
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| Resumo: |
The presence of clouds and shadows and the low temporal resolution of the sensors can be limiting factors for several analyses using satellite images. Considering this, the present study aimed to verify if the integration of the MSI (Sentinel/2) and OLI (Landsat/8) images, aiming at the expansion of the data set, can improve the mapping of the surface water extents of the artificial reservoirs. For this, spectral indices were calculated from images of the OLI and MSI and compared with useful water volume and water depth data collected in situ in the Ceraíma reservoir (BA). The correlation (r) between the surface water extent values obtained by MSI images and the in situ variables useful water volume and relative water depth are 0.80 and 0.78. While, between OLI and the useful water volume and relative water depth variables, the correlations values are 0.59 and 0.58. And, between MSI and OLI, the correlation value is 0.89 and the index of agreement is 0.88. This study concluded that differences in spatial and temporal resolutions have a relevant influence on the ability to integrate images from different satellites for quick and simple results. The low spatial resolution makes it difficult to accurately extract the reservoir contours, while the temporal one limits the number of images for extracting clouds and shadows. |
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