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Parenti”, University of Florence,  \n50134 Florence, Italy; giorgio.limoncella@unifi.it (G.L.); chiara.marzi@unifi.it (C.M.)  \n2 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.)  \n3 Biological Mission of Galicia (MBG), Spanish Council for Scientific Research (CSIC),  \n15704 Santiago de Compostela, Spain; [droye@mbg.csic.es](droye@mbg.csic.es)  \n4 Climate Research Foundation (FIC), 28003 Madrid, Spain  \n5 Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP),  \n28029 Madrid, Spain  \n6 Swiss Tropical and Public Health Institute (Swiss TPH), 4123 Allschwil, Switzerland  \n7 University of Basel, 4003 Basel, Switzerland  \n8 Environment & Health Modelling (EHM) Lab, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK  \n9 ϕ-Lab, European Space Agency, 00044 Frascati, Italy  \n10 Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padua,  \n35020 Padua, Italy; [francesco.pirotti@unipd.it](francesco.pirotti@unipd.it)  \n11 Interdepartmental Research Center of Geomatics (CIRGEO), University of Padua, 35020 Padua, Italy  \n12 Department of Epidemiology, Lazio Regional Health Service, 00147 Rome, Italy; [f.dedonato@deplazio.it](f.dedonato@deplazio.it) (F.d.)  \n* [Correspondence: francesco.sera@unifi.it](Correspondence: francesco.sera@unifi.it)  \nAcademic Editor: Marianna Shepherd  \nReceived: 17 July 2025  \nRevised: 23 August 2025  \nAccepted: 27 August 2025  \nPublished: 2 September 2025  \nCitation: 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  \nFine-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)  \nCopyright: © 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/)) .  \nAbstract  \nHeat-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","cbCain0TPMk44pkQ","https://ap.wps.com/l/cbCain0TPMk44pkQ","pdf",14983126,27,"English","# Introduction\n## Temperature-health impacts and data limitations\n# Methods\n## Two-stage modeling framework\n## Imputation of missing land-surface temperature\n## Prediction of daily Tmax and Tmin\n# Results\n## Predictive accuracy and variability capture\n# Applications\n## High-resolution temperature maps for planning and epidemiology","[{\"question\":\"What problem does the model address?\",\"answer\":\"It targets the need for fine-scale daily air temperature estimates for heat-stress impact assessment and heat-related health studies.\"},{\"question\":\"How is the modeling performed in this framework?\",\"answer\":\"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.\"},{\"question\":\"What data sources and predictors are integrated?\",\"answer\":\"The model combines monitoring station measurements, satellite-derived variables (MODIS, Landsat 8), topography, land cover, ERA5-land meteorology, and vegetation indices such as NDVI.\"}]","A Machine Learning Model Integrating Remote Sensing, Ground Station, and Geospatial Data to Predict Fine-Resolution Daily Air Temperature for Tuscany, Italy - Research Summary | PDF",68]