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Abstract:
An exponentially increasing number of scientific and commercial Earth orbiting satellites are delivering global and timely sensing of the Earth from space, on its surface or from inside the Earth. The onset of climate change and its dire consequences has been exacerbating the adverse impacts on Earth’s environments and its inhabitants. Timely satellite-based Big Earth observations at adequate spatiotemporal resolution provide a means to monitor the evolutions of more frequent and abrupt climate induced and enhanced hazards. These observations could contribute towards the elucidation of their respective governing climatic processes, and enable improved hazards forecasting, water resources monitoring, and informed hazards management and response. Example satellite geodetic and other observations include satellite gravimetry, altimetry, GNSS, GNSS bistatic altimetry, SAR/InSAR, and Planet PBC's high spatiotemporal (subdaily and 3-5 m) resolution multispectral imageries. We illustrate that the use of deep machine learning analytics can effectively integrating hydrometeorological model and other data, and downscaling the satellite geodetic observations, towards enabling timely monitoring of abrupt weather episode evolutions, including floods, groundwater depletions, cyclone landfall, snowstorms, and meteotsunamis.
