A novel spatial and temporal data fusion model based on regularized spatial unmixing was developed to generate Landsat-like synthetic data with the fine spatial resolution of Landsat Enhanced Thematic Mapper Plus (Landsat ETM $+$) data and the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The proposed approach is based on the conventional spatial unmixing technique, but modified to include prior class spectra, which are estimated from pairs of MODIS and Landsat data using the spatial and temporal adaptive reflectance data fusion model. The method requires the optimization of the following three parameters: the number of classes of Landsat data, the neighborhood size of the MODIS data for spatial unmixing, and a regularization parameter added to the cost function to reduce unmixing error. Indexes of relative dimensionless global error in synthesis (ERGAS) were used to determine the best combination of the three parameters by evaluating the quality of the fused result at both Landsat and MODIS spatial resolutions. The experimental results with observed satellite data showed that the proposed approach performs better than conventional unmixing-based fusion approaches with the same parameters.

Health and disaster risk reduction are important and necessary components in building a smart city, especially when climate change may increase the frequency of extreme temperatures and the health risks of urban dwellers. However, limited knowledge is available about the best way to disseminate weather warnings and health protection information. This study explores the weather information acquisition patterns of the Hong Kong public and examines the sociodemographic predictors of these patterns to establish the potential public health implications of smart city development. A population-based, stratified cross-sectional, random digit dialing telephone survey was conducted among the Cantonese-speaking population aged over 15 years in Hong Kong in early 2016. Analyses were conducted based on 1017 valid samples, with a response rate of 63.6%. Cold Weather Warnings were well disseminated in Hong Kong, with 95.7% of the respondents reporting awareness of the public warnings. Television and smartphone apps were the two most important channels for weather information acquisition. Age and education level are the main social-demographic variables associated with the current utilization and future preference of smartphone technology. Among those who were not using a preferred channel to acquire weather information, 61.3% considered switching to a smartphone app. Moreover, the patterns of individual health protection measures and self-reported health impacts were significantly different between smartphone app users and non-users. Weather information dissemination should be tailored to the sociodemographic characteristics of the users.