Department of Environmental Health Sciences, School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, USA.
Environ Res. 2011 Nov;111(8):1046-53. doi: 10.1016/j.envres.2011.08.012. Epub 2011 Sep 14.
Because of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scale.
This study examined whether satellite-derived impervious surface and meteorological conditions from multiple sites can improve characterization of spatial variability of temperature within an urban area.
Temperature was measured at 17 outdoor sites throughout the Detroit metropolitan area during the summer of 2008. Kriging and linear regression were applied to daily temperatures and secondary information, including impervious surface and distance-to-water. Performance of models in predicting measured temperatures was evaluated by cross-validation. Variograms derived from several scenarios were compared to determine whether high-resolution impervious surface information could capture fine-scale spatial structure of temperature in the study area.
Temperatures measured at the sites were significantly different from each other, and all kriging techniques generally performed better than the two linear regression models. Impervious surface values and distance-to-water generally improved predictions slightly. Restricting models to days with lake breezes and with less cloud cover also somewhat improved the predictions. In addition, incorporating high-resolution impervious surface information into cokriging or universal kriging enhanced the ability to characterize fine-scale spatial structure of temperature.
Meteorological and satellite-derived data can better characterize spatial variability in temperature across a metropolitan region. The data sources and methods we used can be applied in epidemiological studies and public health interventions to protect vulnerable populations from extreme heat events.
由于气候变暖，城市温度模式受到了越来越多的关注。城市内部的温度会因土地覆盖、气象和其他因素而有所不同。高分辨率卫星数据可用于了解这种城市内部的变异性，尽管这些数据主要用于在更大的空间尺度上描述城市热岛。
本研究旨在探讨从多个地点获取的卫星衍生不透水面和气象条件是否可以改善对城市内部温度空间变异性的描述。
在 2008 年夏季，在底特律大都市区的 17 个户外站点测量了温度。对每日温度和次要信息（包括不透水面和距水体的距离）进行了克里金和线性回归分析。通过交叉验证评估了模型预测实测温度的性能。比较了几种情况下的变差函数，以确定高分辨率不透水面信息是否可以捕捉研究区域温度的细尺度空间结构。
各站点测量的温度差异显著，所有克里金技术的表现通常均优于两种线性回归模型。不透水面值和距水体的距离通常略有改善预测。将模型限制在有湖风的日子和云量较少的日子也稍微提高了预测效果。此外，将高分辨率不透水面信息纳入协克里金或普遍克里金可以提高描述温度细尺度空间结构的能力。
气象和卫星衍生数据可以更好地描述大都市地区温度的空间变异性。我们使用的数据源和方法可应用于流行病学研究和公共卫生干预措施，以保护弱势群体免受极端高温事件的影响。
