Generating Hourly 70-M Land Surface Temperature from GOES-R Observations: A Comparison of Statistical Downscaling and Deep Learning Methods

Fine-resolution land surface temperature (LST) data is highly desired for comprehensively assessing the ecological and societal impacts of extreme climate events, such as heat waves. However, due to technical constraints, current single satellite sensor cannot provide LST data with both high temporal and spatial resolution simultaneously. To address this limitation, various downscaling and data fusion methods have been proposed to enhance the spatial details of geostationary satellites with high temporal resolution (e.g., hourly or sub-hourly). This study compares the performance of the statistical downscaling method and deep learning model in disaggregating LST observations of the new-generation geostationary satellite, GOES-R, from a resolution of 2 km to 70 m. The findings can contribute to the advancement of downscaling techniques and have practical implications for better analysis of fine-scale LST variations to support decision-making processes related to urban sustainability, land management, and extreme heat mitigation.