Complimenting Optical Remote Sensing Data with Synthetic Aperture Radar Data to Investigate Severe Thunderstorm Damage to Vegetation

Severe thunderstorms produce tornadoes, damaging winds, and large hail, which can cause damage to vegetation. In certain cases the damage caused is significant enough to be seen from space using satellite remote sensors, especially during the growing season, when vegetation is dense, uniform, often green, and more prevalent. Visible, near-infrared, and thermal imaging from optical remote sensors on multiple government, international, and commercial partner satellite missions have been used to evaluate damage to surface vegetation. However, optical remote sensors are not able acquire images of the surface if clouds are present and visible or near-infrared approaches are limited to daytime observations. To increase the number of views of the surface, synthetic aperture radar (SAR) offers an opportunity to increase the number of views of the surface and potential damage, with growing numbers of observations due to increases in the number of SAR missions, increased availability of public data sets (e.g. ESA/Sentinel), forthcoming missions (NISAR), and potential for follow-on SAR measurements.

This presentation highlights the observing of vegetation damage using optical remote sensors complemented with SAR data through two components. The first component demonstrates trends in visible, near-infrared, thermal, and derived indices for damaged and undamaged areas in optical imaging, along with their trends in actively sensed and cloud penetrating SAR observations via C-band, ESA Sentinel-1 observations. Numerous case studies include damage from tornadoes and hail, including events: 27/29 June 2018 South Dakota hail damage, 18 June 2016 Iowa hail damage, 16 May 2017 EF-3 Clear Lake, Wisconsin tornado and the 21 January 2017 Hattiesburg, Mississippi Tornado. Signals evident in SAR observations and validated through visual confirmation, storm damage reports, and optical techniques are then used to create a preliminary means of automating detection for crop damage from these types of significant weather events to help evaluate economic loss, identify areas of recovery, and improve recordkeeping as the second component of the presentation.