Fusion of Orbital and Surface Data for Air Temperature Estimation in Belém Using Machine Learning - Tamasauskas, Leonardo de O.; Negreiros, Waldemiro J. A. G.; Guimarães, Pedro H. do V.; Pereira, Williane G. S.; Dias, Jean A. C.; Costa, Gabriel B.; Seruffo, Marcos C. da R. (Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA); 2026: Anais do XVII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, 2026)
This study evaluates fusion strategies between ERA5-Land orbital data and INMET surface observations to estimate daily air temperature in Belém, using data from 1994 to 2024. Different Random Forest models were compared, along with SARIMAX, LSTM, and stacking (SARIMAX and RF). The RF model with multivariate fusion achieved the best performance (RMSE = 0.5157 °C; R 2 = 0.708; Skill = 6.19%), reducing bias relative to the orbital baseline, while chronological validation confirmed the temporal stability of RF-based models. The results indicate that, under low equatorial thermal variability, tree-based models are the most robust for satellite–surface fusion and thermal downscaling.