Artificial neural networks model based on remote sensing to retrieve evapotranspiration over the Brazilian Pampa

P.S. Kafer, N.S. Da Rocha, L.R. Diaz, E.A. Kaiser, D.C. Santos, G.P. Veeck, D.R. Roberti, S.B.A. Rolim, G.G. De Oliveira

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).Evapotranspiration (ET) quantification improves the comprehension of the water, heat, and carbon interactions and the feedback to the climate, which is essential for global change research. We aimed to model ET using artificial neural networks (ANNs) based on Landsat-8 and reanalysis data from the National Centers for Environmental Prediction over the grasslands of the Pampa biome. The output variable was the ET trained by eddy covariance (EC) measurements acquired from a flux tower located in Santa Maria, Brazil. ANN was performed using the backpropagation algorithm with four remote sensing input variables (albedo, normalized difference vegetation index, land surface temperature, and surface net radiation). In addition, four meteorological variables from the Environmental Prediction Climate Forecast System Version 2 hourly product were included in the model (air temperature, atmospheric pressure, relative humid, and wind speed). We analyzed 67 clear-sky scenes between 2014 and 2019. Results produced very robust daily ET estimates. ANN exhibited a correlation of 0.88 relative to in situ EC data, demonstrating a good linear relationship between ET estimated and measured and producing a root-mean-square error (mean absolute error) of 0.75 (0.58) mm/day. The ANN model was also compared with the widely known simplified surface energy balance index (S-SEBI) model. S-SEBI exhibited lower correlation with the ET in situ compared to the ANN model. Furthermore, the ANN model had a superior performance in summer and winter seasons in which S-SEBI was found to outperform the ET in situ. The model developed in our research is an alternative to approaches that need a great number of input variables or in situ data since it is only dependent on freely available data. Therefore, it should support future integrated strategies of water resources allocation over the natural grasslands of the Brazilian Pampa.
Original languageEnglish
Article number038504
JournalJournal of Applied Remote Sensing
Volume14
Issue number3
DOIs
Publication statusPublished - 1 Jul 2020
Externally publishedYes

Funding

This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brazil (CAPES), finance code 001, the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), and Fundacao de Amparoa Pesquisa do Estado do Rio Grande do Sul (FAPERGS). The Landsat-8 OLI/TIRS products are courtesy of the US Geological Survey Earth Resources Observation and Science Center. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil (CAPES), finance code 001, the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS). We are also grateful to the anonymous reviewers for the valuable contributions. The authors declare no conflicts of interest.

FundersFunder number
Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul

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