A satellite based machine learning approach for estimating high resolution daily average air temperature in a megacity in Brazil
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Author(s)
Type
Journal Article
Abstract
Spatiotemporally resolved ambient temperature data are essential for environmental epidemiology, especially in urban areas where temperature can vary sharply over short distances, influencing population exposure. Additionally, heat distribution often reflects built environment patterns and may correlate with existing social and environmental disparities. Continuous temporal records at high spatial resolution are, however, often lacking, especially in low- and middle-income countries. We developed a generalizable tree-based machine learning approach to estimate daily mean temperatures at 500 x 500 metres resolution using São Paulo, a megacity in Brazil, as a case study, to demonstrate its utility in highly urbanized settings with a heterogeneous urban fabric and unevenly distributed temperature monitoring stations. We trained a Random Forest model using open-access remote sensing data, along with derived products, and temperature measurements from 43 ground stations. To prevent overfitting and select relevant features, we
employed a forward feature selection algorithm with target-oriented (spatial) cross-validation. Hyperparameter tuning was performed using grid search approach. The model was validated through ten-
fold station-based cross-validation and an external hold-out dataset. The model demonstrated strong performance (RMSERF = 0.80; R²RF = 0.95), with slightly reduced accuracy in rural areas (R²rural = 0.91; R²urban = 0.95). Compared to traditional multilinear approaches (RMSEMLR = 1.02; R²MLR = 0.92), the Random Forest model outperformed, likely due to its ability to better capture microclimates and complex relationships between data sources. This 500 x 500 metres daily temperature dataset is the first of its kind in South America, with the São Paulo pipeline and data freely accessible. The approach is adaptable to other regions with appropriate retraining and validation, enabling high-resolution exposure assessments.
employed a forward feature selection algorithm with target-oriented (spatial) cross-validation. Hyperparameter tuning was performed using grid search approach. The model was validated through ten-
fold station-based cross-validation and an external hold-out dataset. The model demonstrated strong performance (RMSERF = 0.80; R²RF = 0.95), with slightly reduced accuracy in rural areas (R²rural = 0.91; R²urban = 0.95). Compared to traditional multilinear approaches (RMSEMLR = 1.02; R²MLR = 0.92), the Random Forest model outperformed, likely due to its ability to better capture microclimates and complex relationships between data sources. This 500 x 500 metres daily temperature dataset is the first of its kind in South America, with the São Paulo pipeline and data freely accessible. The approach is adaptable to other regions with appropriate retraining and validation, enabling high-resolution exposure assessments.
Date Issued
2026-02-23
Date Acceptance
2026-01-07
Citation
Scientific Reports, 2026, 16
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Volume
16
Copyright Statement
© The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Publication Status
Published
Article Number
7459
Date Publish Online
2026-02-05