Evaluating the Impacts of 2020 Iowa Derecho Over Agricultural Fields Using Synthetic Aperture Radar
Abstract
On August 10, 2020 a series of intense and fast-moving windstorms, known as a Derecho, caused widespread damage across Iowas agricultural regions. This severe weather event bent and flattened crops over approximately one-third of the state. Immediate evaluation of the disaster and generating maps showing the damaged fields was critical to enabling rapid-response actions of government agencies and insurance companies. Given the very large area impacted by the disaster, using satellite imagery stands out as the most efficient way for monitoring and evaluating the area. In this study, due to cloudy weather conditions before and after the derecho and also high sensitivity of Synthetic Aperture Radar (SAR) signals to geometry and structure of the crops, we used Sentinel-1 data for crop damage assessment. The main objective of this work was to assess the damage caused by the 2020 Iowa derecho using remote sensing data. We have evaluated the potential of using SAR data for rapidly assessing crop damage resulting from the 2020 Iowa derecho when little to no in-situ data are available. We produced damaged area and damage severity maps based on SAR data before and after the derecho and estimated the damaged area of corn and soybean crops. In total, 2.59 million acres were analyzed, concluding that 1.99 million acres of corn and 0.6 million acres of soybean were impacted by the storm. We compared our results to two other studies published by private companies McKinsey and Company Inc., and Indigo Ag Inc. and also the report published by United States Department of Agriculture (USDA) in January 2021. Overall, our estimates lie in between those reported by USDA, McKinsey, and Indigo. Our estimates for corn and soybean acres are both higher than the USDA reductions in forecasted harvested acres, which is expected since our estimates include varying degrees of damage while the USDA acres likely accounts only for completely destroyed (unharvestable) crops. The difference between our estimates and those from Indigo and McKinsey can be attributed to differences in methods, particularly that the Indigo and McKinsey studies used VV polarization for change detection rather than VH which we used in this study.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMGC43C..02H