However, it is challenging to quantify the contribution of the variables and identify the nonlinear relationships of the multi-dimensional indicators using SMLR model, thus weakening the application value of the research results. Improved understanding of these relationships influencing both night and day land surface temperature will assist planners undertaking climate change adaptation and heat wave mitigation. Our results also demonstrate the potential for using nonlinear statistical analysis to investigate land surface temperature and its relationships with urban characteristics. While this may be expected, this is the first study to quantify this relationship for Landsat-resolution nighttime temperature estimates. Our results suggest that vegetation and impervious surfaces are the most important urban characteristics associated with land surface temperature. We devise techniques for training and validating nonlinear statistical models on geostatistical data and use these models to assess the interdependent effects of urban characteristics on urban surface temperature.

Using the recently available night-time moderate-resolution thermal satellite imagery and employing advanced nonlinear statistical models, we seek to answer the question “What is the influence and relative importance of urban characteristics on land surface temperature, during both the day and night?” To answer this question, we analyze urban land surface temperature in four cities across the United States.

Understanding these mechanisms is necessary to design strategies that mitigate land temperatures in our cities. However, the mechanisms and complex interactions in urban areas underlying land surface temperature are still being understood. The characteristics of urban land surfaces contribute to the urban heat island, and, in turn, can exacerbate the severity of heat wave impacts.

Farsky distinguish day and night