Climate extremes threaten global food security, and compound events, such as late spring heavy and warmer rainfall over snow and subsequent flooding, exacerbate this vulnerability. Despite frequent occurrences in recent years, a quantitative understanding of the compound weather events' impacts remains elusive. Here, we use Synthetic Aperture Radar data from Sentinel-1 and normalized difference vegetation index data from MODIS satellites to map the spring 2019 U.S. Midwest flood extent and evaluate its impact on crop loss. We find a statistically significant association between flooded counties and those with plant greenup delay, while the correlation between flood area percent and amount of green-up delay remains weak, albeit reliable. An analysis of the stream gage time series and crop loss records shows that during the past ∼70 years, ∼43% of spring large discharges are associated with widespread crop loss. We also find an increase in streams' discharge frequency and magnitude across the Midwest, indicating the possibility of a future increase in crop loss due to spring flooding. This study highlights the importance of Earth-observing satellite data for developing climate adaptation and resilience plans.

极端气候威胁着全球粮食安全，而春末大雪和随后的洪水泛滥等复合事件加剧了这种脆弱性。尽管近年来频繁发生，但对复合天气事件影响的定量理解仍然难以捉摸。在这里，我们使用来自 Sentinel-1 的合成孔径雷达数据和来自 MODIS 卫星的归一化差异植被指数数据来绘制 2019 年春季美国中西部洪水范围并评估其对作物损失的影响。我们发现洪水县与植物绿化延迟的县之间存在统计学上的显着关联，而洪水面积百分比与绿化延迟量之间的相关性仍然很弱，尽管是可靠的。对流量时间序列和作物损失记录的分析表明，在过去的 70 年中，约 43% 的春季大流量与广泛的作物损失有关。我们还发现整个中西部河流的排放频率和幅度都有所增加，这表明未来可能会因春季洪水而导致作物损失增加。这项研究强调了地球观测卫星数据对于制定气候适应和恢复计划的重要性。