Crop lodging, the tilting of stems from their natural upright position, usually occurs after a heavy storm event. Since lodging of a crop seriously affects its yield, rapid assessment of crop lodging is valuable for farmers, policymakers, agronomists, insurance companies, and relief workers. Synthetic Aperture Radar (SAR) sensors have been recognized as valuable data sources for mapping lodging extent because of their good penetrating power and high-resolution remote sensing ability. Compared to other sources, SAR’s weather and illumination independence and large area coverage at fine spatial resolution (3 m to 20 m) support frequent and detailed observations. Because of these advantages, SAR has the potential in supporting near real-time monitoring of lodging in fields when combined with automated image processing. In this study, a method based on change detection using modified Hidden Markov Random Field (HMRF) and Sentinel-1A data were utilized to identify lodging and map its extent. Results obtained have shown that when lodging occurs, the VH polarization’s backscatter (σVH) increases between the pre-lodging event image and the post-lodging event image. The increase in σVH is due to the increase in volume scattering and vegetation-soil double bounce scattering resulting from the structural changes in the crop canopy. Using Sentinel-1A images and applying our proposed approach across several fields in Iowa and Illinois, we mapped the extent of the 2020 Derecho (wind storm) lodging disaster. In addition, we separated lodged regions into severely and moderately lodged areas. We estimated that approximately 2.56 million acres of corn and 1.27 million acres of soybean were lodged. Further analysis also showed the separation between un-lodged (healthy) fields and lodged fields. The observations in this study can guide future use of SAR-based information for operational crop lodging assessment.

中文翻译：

---

使用合成孔径雷达（SAR）图像在爱荷华州和伊利诺伊州进行景观尺度农作物倒伏评估

作物倒伏，即茎从自然直立位置倾斜，通常发生在暴风雨后。由于农作物的倒伏严重影响其单产，因此对农作物倒伏的快速评估对农民，政策制定者，农艺师，保险公司和救济工作者都是有价值的。合成孔径雷达（SAR）传感器具有良好的穿透力和高分辨率的遥感能力，因此被认为是用于绘制倒伏范围的有价值的数据源。与其他来源相比，SAR的天气和照度独立性以及在精细的空间分辨率（3 m至20 m）下的大面积覆盖支持了频繁而详细的观测。由于这些优点，SAR与自动图像处理相结合，有潜力支持对田间倒伏进行近实时监控。在这个研究中，该方法利用改进的隐马尔可夫随机场（HMRF）和Sentinel-1A数据进行基于变化检测的方法来识别倒伏并绘制其范围。获得的结果表明，当发生倒伏时，VH极化的后向散射（σVH）在置顶前事件图像和置顶后事件图像之间会增加。σVH的增加归因于作物冠层结构变化导致的体积散射和植被-土壤双反弹散射的增加。使用Sentinel-1A图像，并将我们建议的方法应用于爱荷华州和伊利诺伊州的多个领域，我们绘制了2020年Derecho（风暴）倒塌灾害的程度。此外，我们将寄宿区域分为严重和中等寄宿区域。我们估计约有256万英亩玉米和1。寄放了2700万英亩的大豆。进一步的分析还显示了未倒伏（健康）田地和倒伏田地之间的分离。本研究中的观察结果可指导将来基于SAR的信息用于进行农作物倒伏评估。