Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality.

Land surface characteristics, including soil type, terrain slope, and antecedent soil moisture, have significant impacts on surface runoff during heavy precipitation in highly urbanized areas. In this study, a Linear Spectral Mixture Analysis (LSMA) method is modified to extract high-precision impervious surface, vegetation, and soil fractions. In the modified LSMA method, the representative endmembers are first selected by combining a high-resolution image from Google Earth; the unmixing results of the LSMA are then post-processed to reduce errors of misclassification with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The modified LSMA is applied to the Landsat 8 Operational Land Imager (OLI) image from 18 October 2015 of the main urban area of Guangzhou city. The experimental result indicates that the modified LSMA shows improved extraction performance compared with the conventional LSMA, as it can significantly reduce the bias and root-mean-square error (RMSE). The improved impervious surface, vegetation, and soil fractions are used to calculate the composite curve number (CN) for each pixel according to the Soil Conservation Service curve number (SCS-CN) model. The composite CN is then adjusted with regional data of the terrain slope and total 5-day antecedent precipitation. Finally, the surface runoff is simulated with the SCS-CN model by combining the adjusted CN and real precipitation data at 1 p.m., 4 May 2015.

Accurate estimations of impervious surface areas are essential for urban planning development. Linear spectral mixture analysis (LSMA) is commonly adopted to extract the impervious surface (IS) fraction in a mixed pixel at the subpixel scale. However, owing to errors in the spectra of pure pixels selected from remote sensing images, incorrect fractions of different land cover types often emerge after unmixing. In this study, two Landsat 8 Operational Land Imager (OLI) images—acquired on 20 September 2019 (Path/Row: 121/44) and 14 November 2019 (Path/Row: 122/44)—of Guangzhou and Shenzhen were unmixed by LSMA using spectral indices in endmember selection. A post-processing model using the Dry Bare-soil Index (DBSI) and Normalized Difference Vegetation Index (NDVI) as thresholds was established to improve the IS fraction of the LSMA result. Comparative analysis reveals that LSMA with the post-processing model achieves better performance for IS fraction extraction (R ² = 0.910 and 0.926 and root mean square error [RMSE] = 10.08% and 10.83% for Guangzhou and Shenzhen, respectively), and the distribution of IS is basically consistent with the IS of the actual areas. The post-processing model solves the problem of overestimation of pervious surface and underestimation of impervious surface.