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Article: Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States

TitleDeveloping a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States
Authors
KeywordsAir temperature
Geographically weighted regression
High spatiotemporal
Land surface temperature
MODIS
Issue Date2018
Citation
Remote Sensing of Environment, 2018, v. 215, p. 74-84 How to Cite?
AbstractHigh spatiotemporal resolution air temperature (Ta) datasets are increasingly needed for assessing the impact of temperature change on people, ecosystems, and energy system, especially in the urban domains. However, such datasets are not widely available because of the large spatiotemporal heterogeneity of Ta caused by complex biophysical and socioeconomic factors such as built infrastructure and human activities. In this study, we developed a 1 km gridded dataset of daily minimum Ta (Tmin) and maximum Ta (Tmax), and the associated uncertainties, in urban and surrounding areas in the conterminous U.S. for the 2003–2016 period. Daily geographically weighted regression (GWR) models were developed and used to interpolate Ta using 1 km daily land surface temperature and elevation as explanatory variables. The leave-one-out cross-validation approach indicates that our method performs reasonably well, with root mean square errors of 2.1 °C and 1.9 °C, mean absolute errors of 1.5 °C and 1.3 °C, and R2 of 0.95 and 0.97, for Tmin and Tmax, respectively. The resulting dataset captures reasonably the spatial heterogeneity of Ta in the urban areas, and also captures effectively the urban heat island (UHI) phenomenon that Ta rises with the increase of urban development (i.e., impervious surface area). The new dataset is valuable for studying environmental impacts of urbanization such as UHI and other related effects (e.g., on building energy consumption and human health). The proposed methodology also shows a potential to build a long-term record of Ta worldwide, to fill the data gap that currently exists for studies of urban systems.
Persistent Identifierhttp://hdl.handle.net/10722/329508
ISSN
2023 Impact Factor: 11.1
2023 SCImago Journal Rankings: 4.310
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Xiaoma-
dc.contributor.authorZhou, Yuyu-
dc.contributor.authorAsrar, Ghassem R.-
dc.contributor.authorZhu, Zhengyuan-
dc.date.accessioned2023-08-09T03:33:17Z-
dc.date.available2023-08-09T03:33:17Z-
dc.date.issued2018-
dc.identifier.citationRemote Sensing of Environment, 2018, v. 215, p. 74-84-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/329508-
dc.description.abstractHigh spatiotemporal resolution air temperature (Ta) datasets are increasingly needed for assessing the impact of temperature change on people, ecosystems, and energy system, especially in the urban domains. However, such datasets are not widely available because of the large spatiotemporal heterogeneity of Ta caused by complex biophysical and socioeconomic factors such as built infrastructure and human activities. In this study, we developed a 1 km gridded dataset of daily minimum Ta (Tmin) and maximum Ta (Tmax), and the associated uncertainties, in urban and surrounding areas in the conterminous U.S. for the 2003–2016 period. Daily geographically weighted regression (GWR) models were developed and used to interpolate Ta using 1 km daily land surface temperature and elevation as explanatory variables. The leave-one-out cross-validation approach indicates that our method performs reasonably well, with root mean square errors of 2.1 °C and 1.9 °C, mean absolute errors of 1.5 °C and 1.3 °C, and R2 of 0.95 and 0.97, for Tmin and Tmax, respectively. The resulting dataset captures reasonably the spatial heterogeneity of Ta in the urban areas, and also captures effectively the urban heat island (UHI) phenomenon that Ta rises with the increase of urban development (i.e., impervious surface area). The new dataset is valuable for studying environmental impacts of urbanization such as UHI and other related effects (e.g., on building energy consumption and human health). The proposed methodology also shows a potential to build a long-term record of Ta worldwide, to fill the data gap that currently exists for studies of urban systems.-
dc.languageeng-
dc.relation.ispartofRemote Sensing of Environment-
dc.subjectAir temperature-
dc.subjectGeographically weighted regression-
dc.subjectHigh spatiotemporal-
dc.subjectLand surface temperature-
dc.subjectMODIS-
dc.titleDeveloping a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rse.2018.05.034-
dc.identifier.scopuseid_2-s2.0-85048525885-
dc.identifier.volume215-
dc.identifier.spage74-
dc.identifier.epage84-
dc.identifier.isiWOS:000440776000007-

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