The emphasis of the ROSES-2017 NASA Land-Cover/Land-Use Change (LCLUC) call is on Multi-Source Land Imaging (MuSLI) Science with the goal of combining different satellite data in a synergistic manner in order to increase the observation frequency and/or spatial resolution to answer important science questions and applications related to the program. Quantifying the variability-forcing-response-consequences-prediction of land cover/use change in urban areas is one such research initiative. Research on links between urbanization and urban heat islands (UHI), their effects, and mitigation strategies have gained significant impetus over the past decade due to a warming planet and overall increase in the number of city dwellers during the 21st century. For example, it is projected that nearly 66% of the world's population will live in cities by 2050. The negative effects of urbanization and related UHI’s are dangerous and widespread, and result in changes in vegetation phenology/response due to urban expansion, increased risk of vector-borne diseases, increases in the intensity and frequency of heat waves, and heat related stress/mortality, increased energy demands and economic cost, poor air quality, and changes in regional climate. The urban landscape represents a complex heterogeneous surface that strongly influences the development of the UHI and cannot be adequately characterized using traditional structural based remote sensing classification techniques (i.e., land use/cover types) since they do not relate to the physical functioning of the surface energy budget. Combining albedo and thermal infrared (TIR) derived land surface temperature (LST) information provides the critical missing element for quantifying the effect of urbanization on the UHI. However, current moderate resolution TIR data from Landsat/ASTER (60-100m) are unable to resolve fine-scale urban features and provide an adequate basis for determining the social and economic benefits of heat mitigation strategies in cities - compounded by the fact that these sensors have infrequent observations of at most 16 days in clear skies, making it impossible to effectively model the daily energy balance cycle. We propose to address these challenges by developing and testing algorithms to innovatively combine Landsat-8 TIR and Sentinel-2 optical multispectral imagery with GOES-16 high frequency TIR data to produce a high spatiotemporal resolution LST product at 30m spatial resolution and in 30min timesteps optimized for urban environments. The product would add significant value to the Landsat LCLUC program and associated urban research in not only quantifying the effects of the UHI, but also the effects of urbanization on local climate and vegetation response. Incorporation with urban canopy models (e.g. WRF-urban canopy model), would provide critical information for city planners in urban planning and UHI mitigation efforts. The specific objectives include 1) implementing a thermal infrared sharpening model using training dataset from existing high spatial resolution airborne HyTES thermal and AVIRIS optical data, 2) use the sharpening model to produce a high spatial resolution (30m) urban LST product from Landsat-8 (TIRS) using existing 100m Landsat-8 LST data and optical data from Sentinel-2A/B at 10/20m, and validating the product using coincident HyTES LST data from planned flights over LA in 2018-2019. 3) In collaboration with collaborators at the National Observatory of Athens (NOA), develop a method for using high frequency GOES-16 data and the sharpened Landsat LST data in a Support Vector Regression Machine (SVM) approach to generate a high spatio-temporal resolution urban LST product (30m, 30min). Lastly, we will generate an example beta version of the high spatio-temporal LST product over LA and Athens for select times during 2019-2020, and ensure the code is ready for a production environment including all necessary quality control and metadata information.