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A satellite based machine learning approach for estimating high resolution daily average air temperature in a megacity in Brazil
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  • Published: 05 February 2026

A satellite based machine learning approach for estimating high resolution daily average air temperature in a megacity in Brazil

  • Aina Roca-Barceló1,
  • Rochelle Schneider2,3,4,
  • Monica Pirani1,
  • Alessandro Sebastianelli2,5,
  • Frédéric B. Piel1,6,
  • Paolo Vineis1,
  • Adelaide Cassia Nardocci7 &
  • …
  • Daniela Fecht1,6 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Climate sciences
  • Ecology
  • Engineering
  • Environmental sciences
  • Mathematics and computing

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 × 500 m 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 × 500 m 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.

Data availability

The datasets generated and analysed during the current study are available in this [Zenodo repository](https:/zenodo.org/records/15868840?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImNkM2ViZTYzLTUzZjctNDVmYy05NjZjLWViZWQxYWFlNmM4MyIsImRhdGEiOnt9LCJyYW5kb20iOiIxNDQ3ZTAwNDFmNjkwN2Y2YTViNmViYjQzYzcyYWRiYyJ9.jR0CTaNtlOunm0XQYBwjC73yFJIdsjtSXUe-F94VXx5a1vgsCEeQP5-XIPnsRa36rV-fZZuCsw4WtZeDMI9IPA)87. All the code used in the analyses is available on GitHub at (https:/github.com/AinaRB/DailyTemperature_RandomForest_SaoPaulo) . Additionally, a public-facing website providing accessible, layman-friendly information about the project and its findings can be found at the project’s website: (https://ainarb.github.io/climate_and_health/)88.

Abbreviations

BSA:

Black sky albedo

CV:

Cross-validation

d2m:

Dew temperature at 2 m

DEM:

Digital elevation model

ECMWF:

European centre for medium-range weather forecasts

ERA5:

5th generation European centre for medium-range weather forecasts (ECMWF) atmospheric reanalysis

FFS:

Feature forward selection

GEE:

Google earth engine

LMIC:

Low and middle-income countries

LST:

Land surface temperature

MLR:

Multi-linear regression

NDVI:

Normalized difference vegetation index

R2:

r-squared

RF:

Random Forest

rh:

Relative humidity

RMSE:

Root mean square error

SZA:

Solar zenith angle

t2m:

Ambient temperature at 2 m

Ta:

Ambient temperature

u10:

Northward wind component

v10:

Eastward wind component

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Acknowledgements

This work was supported by the Imperial College PhD President Scholarship awarded to Dr Aina Roca-Barcelo. The content of this article is not officially endorsed by the funder. The authors declare no competing financial interest.

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Authors and Affiliations

  1. MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 90 Wood Ln, London, W12 0BZ, UK

    Aina Roca-Barceló, Monica Pirani, Frédéric B. Piel, Paolo Vineis & Daniela Fecht

  2. Φ-lab, European Space Agency (ESA), Frascati, Italy

    Rochelle Schneider & Alessandro Sebastianelli

  3. Forecast Department, European Centre for Medium-Range Weather Forecast (ECMWF), Reading, UK

    Rochelle Schneider

  4. Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine (LSHTM), London, UK

    Rochelle Schneider

  5. CMCC Foundation - Euro-Mediterranean Center on Climate Change, Caserta, Italy

    Alessandro Sebastianelli

  6. UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

    Frédéric B. Piel & Daniela Fecht

  7. Department of Environmental Health, School of Public Health, University of São Paulo, São Paulo, Brazil

    Adelaide Cassia Nardocci

Authors
  1. Aina Roca-Barceló
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  8. Daniela Fecht
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Contributions

Dr Aina Roca-Barceló : Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Validation, Visualization, Writing – Original Draft, Project Administration, Rochelle Schneider : Supervision, Methodology, Validation, Writing – Review And Editing, Monica Pirani : Supervision, Methodology, Validation, Writing – Review And Editing, Alessandro Sebastianelli : Methodology, Writing – Review And Editing, Frédéric B. Piel Supervision, Validation, Writing – Review And Editing, Paolo Vineis Supervision, Validation, Writing – Review And Editing, Adelaide Cassia Nardocci : Resources, Writing – Review And Editing, Daniela Fecht : Supervision, Methodology, Validation, Writing – Review And Editing, Project Administration.

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Correspondence to Aina Roca-Barceló.

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Roca-Barceló, A., Schneider, R., Pirani, M. et al. A satellite based machine learning approach for estimating high resolution daily average air temperature in a megacity in Brazil. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35689-x

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  • Received: 30 June 2025

  • Accepted: 07 January 2026

  • Published: 05 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-35689-x

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Keywords

  • Random forest
  • Ambient temperature
  • Remote sensing
  • Spatial cross-validation
  • Forward feature selection
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