Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions.

Understanding the link between greenspace patterns and land surface temperature is very important for mitigating the urban heat island (UHI) effect and is also useful for planners and decision-makers for providing a sustainable design for urban greenspace. Although coupling remote sensing data with a computational fluid dynamics (CFD) model has widely been used to examine interactions between UHI and greenspace patterns, the paper aims to examine the impact of five theoretical models of greenspace patterns on land surface temperature based on the improvement of the accuracy of CFD modeling by the combination of LiDAR data with remote sensing images to build a 3D urban model. The simulated results demonstrated that the zonal pattern always had the obvious cooling effects when there are no large buildings or terrain obstacles. For ambient environments, the building or terrain obstacles and the type of greenspace have the hugest influence on mitigating the UHI, but the greenspace area behaves as having the least cooling effect. A dotted greenspace pattern shows the best cooling effect in the central area or residential district within a city, while a radial and a wedge pattern may result in a “cold source” for the urban thermal environment.