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

REMOTE SENSING OF ENVIRONMENT

卷:215

Article

Li, Xiaoma¹  Zhou, Yuyu¹  Asrar, Ghassem R.²  Zhu, Zhengyuan^3,4 

[1] Iowa State Univ, Dept Geol & Atmospher Sci, Ames, IA 50011 USA

[2] Pacific Northwest Natl Lab, Joint Global Change Res Inst, College Pk, MD 20740 USA

[3] Iowa State Univ, Dept Stat, Ames, IA 50011 USA

[4] Iowa State Univ, Stat Lab, Ames, IA 50011 USA

关键词: Air temperature;    Land surface temperature;    Geographically weighted regression;    High spatiotemporal;    MODIS;   

DOI  :  10.1016/j.rse.2018.05.034

来源: Elsevier

【 摘 要 】

High 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 degrees C and 1.9 degrees C, mean absolute errors of 1.5 degrees C and 1.3 degrees C, and R 2 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.

【 授权许可】

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