Evidence map›Paper›PMID 23777856›Full record

ArticleEnvironmental health perspectives2013

Validating satellite-derived land surface temperature with in situ measurements: a public health perspective.

Jalonne L White-Newsome, Shannon J Brines, Daniel G Brown, J Timothy Dvonch, Carina J Gronlund, Kai Zhang, Evan M Oswald, Marie S O'Neill

Abstract readValidation Study
In one paragraph

Article in Environmental health perspectives, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification.IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society · 2025
    Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  12. Review
  13. Satellite remote sensing in epidemiological studies.Current opinion in pediatrics · 2016
    Review
  14. A Bicycle-Based Field Measurement System for the Study of Thermal Exposure in Cuyahoga County, Ohio, USA.International journal of environmental research and public health · 2016
    Article
  15. Article
  16. Article
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

8 authors.

Jalonne L White-NewsomeSchool of Public Health, University of Michigan, Ann Arbor, Michigan, USA. Jalonne@weact.org
Shannon J Brines
Daniel G Brown
J Timothy Dvonch
Carina J Gronlund
Kai Zhang
Evan M Oswald
Marie S O'Neill

Funding

University of Michigan ERC-supplement (TRT)T42OH008455 · OH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NEITZEL, RICHARD L · 2005 to 2025
$32.3M
Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Dana Dolinoy · 2011 to 2026
$21.3M
Interdisciplinary Research Training on Health and AgingT32AG027708 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Lindsay C Kobayashi, Briana Mezuk · 2008 to 2026
$2.7M
Air Pollution, Inflammation and Preterm Birth: A Mechanistic Study in Mexico CityR01ES016932 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI O'NEILL, MARIE SYLVIA · 2008 to 2015
$2.2M
Reducing Social Disparities of Heatwave Impacts in a Changing ClimateR18EH000348 · EH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI O'NEILL, MARIE SYLVIA · 2008 to 2010
$1.3M
NCEH CDC HHS R18 EH000348NIA NIH HHS T32 AG027708NIA NIH HHS T32AG027708NIEHS NIH HHS P30 ES017885NIEHS NIH HHS R01 ES016932NIOSH CDC HHS 2T42OH008455NIOSH CDC HHS T42 OH008455
6 · The paper itself

Abstract

backgroundLand surface temperature (LST) and percent surface imperviousness (SI), both derived from satellite imagery, have been used to characterize the urban heat island effect, a phenomenon in which urban areas are warmer than non-urban areas.

objectivesWe aimed to assess the correlations between LSTs and SI images with actual temperature readings from a ground-based network of outdoor monitors.

methodsWe evaluated the relationships among a) LST calculated from a 2009 summertime satellite image of the Detroit metropolitan region, Michigan; b) SI from the 2006 National Land Cover Data Set; and c) ground-based temperature measurements monitored during the same time period at 19 residences throughout the Detroit metropolitan region. Associations between these ground-based temperatures and the average LSTs and SI at different radii around the point of the ground-based temperature measurement were evaluated at different time intervals. Spearman correlation coefficients and corresponding p-values were calculated.

resultsSatellite-derived LST and SI values were significantly correlated with 24-hr average and August monthly average ground temperatures at all but two of the radii examined (100 m for LST and 0 m for SI). Correlations were also significant for temperatures measured between 0400 and 0500 hours for SI, except at 0 m, but not LST. Statistically significant correlations ranging from 0.49 to 0.91 were observed between LST and SI.

conclusionsBoth SI and LST could be used to better understand spatial variation in heat exposures over longer time frames but are less useful for estimating shorter-term, actual temperature exposures, which can be useful for public health preparedness during extreme heat events.

Indexed as

Geographic Information SystemsPublic HealthSpacecraftEnvironmental MonitoringTemperature

Identifiers

PMID23777856
PMCPMC3734495

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Registered trials

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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.