Iowa crop damage from Derecho thunderstorms seen from space

Many Iowa farmers suddenly saw their crops flattened after a set of strong thunderstorms battered the state this month, NASA reported while sharing satellite images of the damage.

The line of thunderstorms, called a derecho, hit the U.S. Midwest hard on Aug. 10. Tens of thousands of people in Iowa were still without power a week after a storm, while farmers tried to figure out how much of their corn and soybean harvest they could salvage, the NASA Earth Observatory website said in a statement.

"While large hail can quickly strip stalks and pulverize corn and soybean plants — leaving dark brown stripes across rural landscapes — much of the damage in August 2020 was caused by fierce winds that toppled stalks that were already heavy with maturing corn husks," NASA said.

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The Landsat 8 satellite, a collaboration between NASA and the United States Geological Survey, captured an image of the damaged fields in Marshalltown, Iowa, which is about an hour's drive northeast of Des Moines. The wind-damaged crops appear in light green, while darker areas likely represent tree breaks or lesser-damaged crops. Hail also hit some plants. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the NASA-led Terra spacecraft shows the damage across a wider field of view.

The storm likely caused almost $4 billion in damage, CBS News reported, making it one of the 30 costliest events of the past 10 years as tracked by the National Oceanic and Atmospheric Administration's (NOAA) weather and climate disaster tracker.

NASA scientists are working on a project to use remote sensing to better understand hail events such as this one that hit Iowa. The information could be used to help figure out the risk of crop losses during severe thunderstorms, NASA said.

"We are using a whole suite of sensors to detect storms and assess the damages they cause," Kristopher Bedka, the leader of that project, said in the same NASA statement. "We are doing it not just for this event, but for catastrophic events all around the world so we can learn how to best quantify damage in an automated way."

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