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Cited by 188

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data

Kaufmann, R. K., Zhou, L., Myneni, R. B., Tucker, C. J., Slayback, D., Shabanov, N. V., Pinzon, Jorge·2003·Geophysical Research Letters

Abstract

We use statistical techniques to quantify the effect of interannual variations in vegetation within land covers on surface temperature in North America and Eurasia from satellite measures of surface greenness and ground based meteorological observations. During the winter, reductions in the extent of snow cover cause (in a statistical sense) temperature to rise. During the summer, increases in terrestrial vegetation within land covers cause (in a statistical sense) temperature to fall. Temperature‐induced increases in vegetation have slowed increases in surface temperature, but this feedback may be limited by the range over which temperature has a positive effect on vegetation.

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Crossref · 10.1029/2003gl018251

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Metadata

Authors
Kaufmann, R. K., Zhou, L., Myneni, R. B., Tucker, C. J., Slayback, D., Shabanov, N. V., & Pinzon, J.
Venue
Geophysical Research Letters
Published
2003-11
Volume
30
Issue
22
ISSN
  • 0094-8276 (ISSN-L)
  • 1944-8007
OpenAlex
W2110878578
Cited by
188
References
3
FWCI
3.81
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data. https://app.cochat.ai/papers/10.1029/2003gl018251
The paper
Kaufmann, R. K., Zhou, L., Myneni, R. B., Tucker, C. J., Slayback, D., Shabanov, N. V., & Pinzon, J. (2003). The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data. Geophysical Research Letters. https://doi.org/10.1029/2003gl018251

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.