A Machine Learning Model Integrating Remote Sensing, Ground Station, and Geospatial Data to Predict Fine-Resolution Daily Air Temperature for Tuscany, Italy - Research Summary

Aug 6, 2026

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Heat-related morbidity and mortality are rising with climate change, increasing the need to pinpoint vulnerable areas and people exposed to extreme temperatures. A replicable machine learning framework was built to estimate daily air temperature at 100 m × 100 m resolution across Tuscany, Italy by integrating remote sensing, ground-station measurements, and geospatial predictors. A two-stage pipeline first imputes missing land-surface temperature using gradient-boosted trees and spatio-temporal features, then models daily Tmax and Tmin using monitoring data, satellite products (MODIS, Landsat 8), topography, land cover, ERA5-land meteorology, and NDVI vegetation indices. The model attains strong performance (R2 0.95 Tmax, 0.92 Tmin; RMSE about 1.95–1.96 °C), capturing temporal and spatial variability and enabling high-resolution temperature mapping for urban planning, climate adaptation, and heat-health epidemiology.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages27

Reading Time1 h 8 min

AccessFree to download

Introduction

Temperature-health impacts and data limitations

Methods

Two-stage modeling framework

Imputation of missing land-surface temperature

Prediction of daily Tmax and Tmin

Results

Predictive accuracy and variability capture

Applications

High-resolution temperature maps for planning and epidemiology

Article A Machine Learning Model Integrating Remote Sensing, Ground Station, and Geospatial Data to Predict Fine-Resolution Daily Air Temperature for Tuscany, Italy Giorgio Limoncella 1, Denise Feurer 2, Dominic Roye 3,4,5, Kees de Hoogh 6,7, Arturo de la Cruz 8, Antonio Gasparrini 8, Rochelle Schneider 9, Francesco Pirotti 10,11, Dolores Catelan 2, Massimo Stafoggia 12, Francesca de’Donato 12, Giulio Biscardi 1, Chiara Marzi 1, Michela Baccini 1 and Francesco Sera 1, * 1 Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, 50134 Florence, Italy; giorgio.limoncella@unifi.it (G.L.); chiara.marzi@unifi.it (C.M.) 2 Unit of Biostatistics, Epidemiology and Public Health (UBEP), University of Padua, 35131 Padua, Italy; [denise.feurer@ubep.unipd.it](denise.feurer@ubep.unipd.it) (D.F.); [dolores.catelan@ubep.unipd.it](dolores.catelan@ubep.unipd.it) (D.C.) 3 Biological Mission of Galicia (MBG), Spanish Council for Scientific Research (CSIC), 15704 Santiago de Compostela, Spain; [droye@mbg.csic.es](droye@mbg.csic.es) 4 Climate Research Foundation (FIC), 28003 Madrid, Spain 5 Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain 6 Swiss Tropical and Public Health Institute (Swiss TPH), 4123 Allschwil, Switzerland 7 University of Basel, 4003 Basel, Switzerland 8 Environment & Health Modelling (EHM) Lab, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK 9 ϕ-Lab, European Space Agency, 00044 Frascati, Italy 10 Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padua, 35020 Padua, Italy; [francesco.pirotti@unipd.it](francesco.pirotti@unipd.it) 11 Interdepartmental Research Center of Geomatics (CIRGEO), University of Padua, 35020 Padua, Italy 12 Department of Epidemiology, Lazio Regional Health Service, 00147 Rome, Italy; [f.dedonato@deplazio.it](f.dedonato@deplazio.it) (F.d.) * [Correspondence: francesco.sera@unifi.it](Correspondence: francesco.sera@unifi.it) Academic Editor: Marianna Shepherd Received: 17 July 2025 Revised: 23 August 2025 Accepted: 27 August 2025 Published: 2 September 2025 Citation: Limoncella, G.; Feurer, D.; Roye, D.; de Hoogh, K.; de la Cruz, A.; Gasparrini, A.; Schneider, R.; Pirotti, F.; Catelan, D.; Stafoggia, M.; et al. A Machine Learning Model Integrating Remote Sensing, Ground Station, and Geospatial Data to Predict Fine-Resolution Daily Air Temperature for Tuscany, Italy. Remote Sens. 2025, 17, 3052. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/rs17173052](10.3390/rs17173052) Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . Abstract Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI) . The model achieved high predictive accuracy, with R2 values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 ◦ C and 1.96 ◦ C

FAQs

What problem does the model address?

It targets the need for fine-scale daily air temperature estimates for heat-stress impact assessment and heat-related health studies.

How is the modeling performed in this framework?

It uses a two-stage approach: first imputing missing land-surface temperature (from MODIS) with gradient-boosted trees, then predicting daily Tmax and Tmin using station observations, satellite data, and multiple geospatial and meteorological predictors.

What data sources and predictors are integrated?

The model combines monitoring station measurements, satellite-derived variables (MODIS, Landsat 8), topography, land cover, ERA5-land meteorology, and vegetation indices such as NDVI.

Aug 6, 2026

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