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Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWAL8) and split-window algorithm (SWAL8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWAS3 and SWAH8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWAH8 was compared with MWAL8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day. [ABSTRACT FROM AUTHOR]

Satellite-derived rugged land surface temperature (LST) is an important parameter indicating the status of the Earth's surface energy budget and its seasonal/temporal dynamic change. However, existing LST products from rugged areas are more prone to error when supporting applications in mountainous areas and Earth surface processes that occur at high spatial and temporal resolutions. This research aimed to develop a method for generating rugged LST with a high temporal and spatial resolution by using an improved ensemble LST model combining three regressors, including a random forest, a ridge, and a support vector machine. Different combinations of high-resolution input parameters were also considered in this study. The input datasets included Moderate Resolution Imaging Spectroradiometer (MODIS) LST datasets (MxD11A1) for nighttime, temporal Sentinel-2 Multispectral Instrument (MSI) datasets, and digital elevation model (DEM) datasets. The 30 m rugged LST datasets derived were compared against an in situ LST dataset obtained at Saihanba Forest Park (SFP) sites and an ASTER-derived 90 m LST, respectively. The results with in situ measurements demonstrated significant LST details, with an R2 higher than 0.95 and RMSE around 3.00 K for both Terra/MOD- and Aqua/MYD-based LST datasets, and with slightly better results being obtained from the Aqua/MYD-based LST than that from Terra/MOD. The inter-comparison results with ASTER LST showed that over 80% of the pixels of the difference image for the two datasets were within 2 K. In light of the complex topography and distinct atmospheric conditions, these comparison results are encouraging. The 30 m LST from the method proposed in this study also depicts the seasonality of rugged surfaces. [ABSTRACT FROM AUTHOR]

Characteristics of the urban environment (e.g., building density and road network) can influence the spread and transmission of coronavirus disease 2019 (COVID-19) within cities, especially in high-density high-rise built environments. Therefore, it is necessary to identify the key attributes of high-density high-rise built environments to enhance modelling of the spread of COVID-19. To this end, case studies for testing attributes for modelling development were performed in two densely populated Chinese cities with high-rise, high-density built environments (Hong Kong and Shanghai).The investigated urban environmental features included 2D and 3D urban morphological indices (e.g., sky view factor, floor area ratio, frontal area density, height to width ratio, and building coverage ratio), socioeconomic and demographic attributes (e.g., population), and public service points-of-interest (e.g., bus stations and clinics). The modelling effects of 3D urban morphological features on the infection rate are notable in urban communities. As the spatial scale becomes larger, the modelling effect of 2D built environment factors (e.g., building coverage ratio) on the infection rate becomes more notable. The influence of several key factors (e.g., the building coverage ratio and population density) at different scales can be considered when modelling the infection risk in urban communities. The findings of this study clarify how attributes of built environments can be applied to predict the spread of infectious diseases. This knowledge can be used to develop effective planning strategies to prevent and control epidemics and ensure healthy cities.