Performance and validation of water surface temperature estimates from Landsat 8 of the Itaipu Reservoir, State of Paraná, Brazil.

Autor: Kramer G; Postgraduate Program in Geography, Federal University of Santa Maria, Av. Roraima, Santa Maria, Rio Grande Do Sul, 100097105-900, Brazil. gisieli@outlook.com.br., Filho WP; Department of Geosciences, Federal University of Santa Maria, Av. Roraima, Santa Maria, Rio Grande Do Sul, 100097105-900, Brazil., de Carvalho LAS; Department of Meteorology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, 21941-916, Brazil., Trindade PMP; National Institute for Space Research (INPE - CRS), Santa Maria, Rio Grande Do Sul, Brazil., da Rosa CN; Itaipu Technological Park Foundation (ITPF), Av. Presidente Tancredo Neves Edifício das Águas, Fase I, Sala 202, Foz Do Iguaçu, Paraná, 673185867-900, Brazil., Dezordi R; Itaipu Technological Park Foundation (ITPF), Av. Presidente Tancredo Neves Edifício das Águas, Fase I, Sala 202, Foz Do Iguaçu, Paraná, 673185867-900, Brazil.
Jazyk: angličtina
Zdroj: Environmental monitoring and assessment [Environ Monit Assess] 2022 Nov 22; Vol. 195 (1), pp. 137. Date of Electronic Publication: 2022 Nov 22.
DOI: 10.1007/s10661-022-10677-6
Abstrakt: Studies on water surface temperature (WST) from thermal infrared remote sensing are still incipient in Brazil, and for many water resources, they do not exist. Many algorithms have been developed to estimate surface temperature in satellite images. There are also many difficulties in implementing these algorithms due to their complexity, especially in free software, which restricts the satisfactory processing of these data by users of the technique. Thus, this work aimed to validate an algorithm used to estimate land surface temperature (LST) when applied to the surface of inland water bodies. Water surface temperature estimates (WSTe) were generated from Itaipu State of Paraná (PR) reservoir, Brazil, calculated from Landsat 8 - TIRS satellite images (WSTs) and water surface temperature data from 37 in situ stations (WSTi). A linear regression model of the WSTe was generated in 60% of the samples and its validation with the remaining 40%, subject to prior evaluation of some statistical indicators. The model was considered significant since the coefficient of determination (r 2 ) was 0.90 (95% of confidence), root mean square deviation (RMSD) 0.8 °C, Willmott Index (d) = 0.97, and Nash-Sutcliffe efficiency coefficient (NSE) = 0.89. The methodology used to extract WSTs from the Python QGIS plugin was relatively quick to apply, easy to understand, and had a better performance of the estimates than those presented in the literature review.
(© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
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