Evidence map›Paper›PMID 42331799›Full record

ArticleNature communications2026

Estimating high-resolution albedo for urban applications.

David Fork, Elizabeth J Wesley, Salil Banerjee, Vishal Batchu, Aniruddh Chennapragada, Kevin Crossan, Bryce Cronkite-Ratcliff, Ellie Delich, Tristan Goulden, Mansi Kansal and 11 more

Abstract read
In one paragraph

Article in Nature communications, 2026. 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

21 authors.

David Fork *Google, Mountain View, CA, USA. fork@google.com.ORCID http://orcid.org/0000-0001-9559-1277
Elizabeth J Wesley *World Resources Institute, Washington, D.C., USA.ORCID http://orcid.org/0000-0001-9366-0478
Salil BanerjeeGoogle, Mountain View, CA, USA.
Vishal BatchuGoogle, Mountain View, CA, USA.ORCID http://orcid.org/0000-0003-0461-0730
Aniruddh ChennapragadaGoogle, Mountain View, CA, USA.ORCID http://orcid.org/0009-0009-4378-6991
Kevin CrossanGoogle, Mountain View, CA, USA.ORCID http://orcid.org/0009-0004-0924-1721
Bryce Cronkite-RatcliffGoogle, Mountain View, CA, USA.
Ellie DelichGoogle, Mountain View, CA, USA.
Tristan GouldenNational Ecological Observatory Network, Battelle, Boulder, CO, USA.
Mansi KansalGoogle, Mountain View, CA, USA.
Jonas KempGoogle, Mountain View, CA, USA.
Eric MackresWorld Resources Institute, Washington, D.C., USA.ORCID http://orcid.org/0009-0004-0182-3399
Yael MayerGoogle, Mountain View, CA, USA.
Rebecca MilmanGoogle, Mountain View, CA, USA.ORCID http://orcid.org/0009-0001-4338-7781
John C PlattGoogle, Mountain View, CA, USA.ORCID http://orcid.org/0000-0002-5652-5303
Shruthi PrabhakaraGoogle, Mountain View, CA, USA.
Gautam PrasadGoogle, Mountain View, CA, USA.
Shravya ShettyGoogle, Mountain View, CA, USA.ORCID http://orcid.org/0000-0003-3783-3172
Charlotte StantonGoogle, Mountain View, CA, USA.
Wayne SunGoogle, Mountain View, CA, USA.
Lucy R HutyraGoogle, Mountain View, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extreme urban heat poses a growing threat to global health, yet a critical cooling intervention-reflective cool roofs-lacks the high-resolution data needed for building-level planning. While satellite-based albedo estimates are globally available, their coarse resolution cannot resolve individual rooftops, limiting targeted climate action. Here we show that fusing freely available 10-m Sentinel-2 imagery with high-resolution satellite data enables urban albedo mapping at 30-cm resolution. This approach combines the radiometric accuracy and global coverage of Sentinel-2 with the spatial detail of commercial imagery to produce actionable urban datasets. Validated against airborne hyperspectral measurements (root mean square error = 0.04), the method resolves albedo at individual building footprints across diverse urban environments. Applied to 12 global cities, our analysis shows that prioritizing large-footprint buildings for cool roof retrofits can reduce citywide temperatures by up to 0.5 °C. This work validates Sentinel-2 for city-scale albedo estimation and enables globally deployable, building-scale rooftop albedo mapping to support targeted cool roof interventions.

Identifiers

PMID42331799
PMCPMC13287749

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LicenceCC BY
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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.