Localized Downscaling of Urban Land Surface Temperature—A Case Study in Beijing, China

Abstract

High-resolution land surface temperature (LST) data are essential for fine-scale urban thermal environment studies. Urban LST downscaling studies mostly remain focused on only two-dimensional (2-D) data, and neglect the impact of three-dimensional (3-D) surface structure on LST. In addition, the choice of window size is also important for LST downscaling over heterogeneous surfaces. In this study, we downscaled Landsat-LST using localized and stepwise approaches in a random forest model (RF). In addition, both 2- and 3-D building morphologies were included. Our results show that: (1) The performances of a local moving window and stepwise downscaling are dependent on the extent of surface heterogeneity. For mixed surfaces, a localized window performed better than the global window, and a stepwise approach performed better than a single-step approach. However, for monotonous surfaces (e.g., urban impervious surfaces), the global window performed better than a localized window; (2) That multi-scale geographically weighted regression (MGWR) could provide a possibility for selection of the optimal moving window. 7 × 7 windows derived from MGWR by the minimum bandwidth of predictors, performed better than other windows (3 × 3, 5 × 5, and 11 × 11) in the Beijing area; (3) That the morphology of buildings has a non-negligible impact and scaling effect on urban LST. When building morphologies were included in downscaling, the performance of the RF model improved. Furthermore, the importance of the sky view factor, building height, and building density was greater at a higher resolution than at a lower resolution.

Sensitivity Analysis of Regression-Based Trend Estimates to Input Errors in Spatial Downscaling of Coarse Resolution Remote Sensing Data

“…Instead, using the magnitude of explanatory power in the TC estimation as a direct indicator to select an optimal regression model requires caution, particularly when the input data contain errors. Recently, geographically weighted regression (GWR) has often been applied in ATPRK-based spatial downscaling [35][36][37]. As regression coefficients are estimated locally, the explanatory power of GWR is likely to be higher than that of other regression models.…”

Applicability Assessment of Passive Microwave LST Downscaling over Semi–Homogeneous Desert Underlying Surface Based on Machine Learning

“…However, little research has been conducted on the applicability of this relationship to different underlying surfaces. We need to build a process-oriented evaluation method [75] to study the scale effects [76] of the relationship between downscaling factors and LST considering different underlying surfaces. This is what our next research will address.…”

Large-Scale Urban Parks on Post-Industrial Sites in Contemporary Urban Landscape Conceptions

“…As per the Master Plan of Beijing 2002-2020, the overall urban built area increased with the expansion of the ring-shaped infrastructure system from 1975 to 2002 (Stokman et al 2008). Such a plan suggests that rapid population growth and an increase in housing and economic activities have expanded the inner city's built-up area from 84 km 2 in 1949 to more than 700 km 2 within the recent decade since the foundation of the People's Republic (Li et al 2005b). One of the impacts of economic reforms in 1978 was unprecedented urban expansion.…”