Evidence map›Paper›PMID 40951869›Full record

ArticleRemote sensing2025

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

Giorgio Limoncella, Denise Feurer, Dominic Roye, Kees de Hoogh, Arturo de la Cruz, Antonio Gasparrini, Rochelle Schneider, Francesco Pirotti, Dolores Catelan, Massimo Stafoggia and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Remote sensing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Giorgio LimoncellaDepartment of Statistics, Computer Science, Applications "G. Parenti", University of Florence, 50134 Florence, Italy.
Denise FeurerUnit of Biostatistics, Epidemiology and Public Health (UBEP), University of Padua, 35131 Padua, Italy.ORCID https://orcid.org/0009-0001-4573-4302
Dominic RoyeBiological Mission of Galicia (MBG), Spanish Council for Scientific Research (CSIC), 15704 Santiago de Compostela, Spain.ORCID https://orcid.org/0000-0002-5516-6396
Kees de HooghSwiss Tropical and Public Health Institute (Swiss TPH), 4123 Allschwil, Switzerland.
Arturo de la CruzEnvironment & Health Modelling (EHM) Lab, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.
Antonio GasparriniEnvironment & Health Modelling (EHM) Lab, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.ORCID https://orcid.org/0000-0002-2271-3568
Rochelle Schneiderϕ-Lab, European Space Agency, 00044 Frascati, Italy.ORCID https://orcid.org/0000-0002-2905-0154
Francesco PirottiDepartment of Land, Environment, Agriculture and Forestry (TESAF), University of Padua, 35020 Padua, Italy.ORCID https://orcid.org/0000-0002-4796-6406
Dolores CatelanUnit of Biostatistics, Epidemiology and Public Health (UBEP), University of Padua, 35131 Padua, Italy.
Massimo StafoggiaDepartment of Epidemiology, Lazio Regional Health Service, 00147 Rome, Italy.ORCID https://orcid.org/0000-0003-2843-2908
Francesca de'DonatoDepartment of Epidemiology, Lazio Regional Health Service, 00147 Rome, Italy.ORCID https://orcid.org/0000-0002-2225-9457
Giulio BiscardiDepartment of Statistics, Computer Science, Applications "G. Parenti", University of Florence, 50134 Florence, Italy.ORCID https://orcid.org/0009-0000-2702-4238
Chiara MarziDepartment of Statistics, Computer Science, Applications "G. Parenti", University of Florence, 50134 Florence, Italy.ORCID https://orcid.org/0000-0002-1791-3573
Michela BacciniDepartment of Statistics, Computer Science, Applications "G. Parenti", University of Florence, 50134 Florence, Italy.ORCID https://orcid.org/0000-0002-2619-2130
Francesco SeraDepartment of Statistics, Computer Science, Applications "G. Parenti", University of Florence, 50134 Florence, Italy.ORCID https://orcid.org/0000-0002-8890-6848

Funding

Medical Research Council MR/Y003330/1
6 · The paper itself

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

Indexed as

air temperatureLandsat 8machine learningMODISremote sensingurban heat island

Identifiers

PMID40951869
PMCPMC7618111

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.