Description
Synthetic Aperture Radar (SAR) sensors are ideal for monitoring certain disasters or areas that are vulnerable to disasters because the signal can “see” the surface of Earth during day or night and under nearly all weather conditions. In addition, the signal can penetrate through vegetation and is sensitive to surface roughness and small displacements of the land surface. This training, led by NASA's Applied Remote Sensing Training Program (ARSET), focuses on the use of SAR to assess areas at risk from disasters due to landslides through the use of interferometric SAR (InSAR). This is accomplished by measuring small movements (on the order of centimeters) of the land surface that are caused by gradual landslide motion, and how these movements vary with time. The sessions also characterize the extent of oil spills and their impacts, and inundation extent. SAR data is sensitive to surface roughness, allowing for identifying areas where there are oil spills. The SAR signal can penetrate through vegetation and detect inundation driven by large precipitation events or by natural events. This training includes theoretical portions for each disaster as related to the SAR signal interaction with surface conditions and demonstrations using Google Earth Engine, Jupyter Notebooks, and the SNAP Toolbox, all freely and openly available tools.