**5. Discussion**

#### *5.1. Spatial-Extent of Lodging and Lodging Rate*

Based on the reports by USDA Risk Management Agency (RMA), 57 counties in Iowa and Illinois were in the path of the storm which led to the Derecho lodging disaster. In the report by RMA, there are roughly 14 million acres of insured crops within those 57 counties. Also, based on the Storm Prediction Center preliminary storm reports and assessment from MODIS satellite imagery, the Iowa Department of Agriculture and Land Stewardship estimated that about 3.57 million acres of corn and 2.5 million acres of soybeans were likely to have been impacted by the severe wind on 10 August 2020. Based on our findings using Sentinel-1A data across 52 counties, we estimated that a total of approximately 2.56 million acres of corn and approximately 1.27 million acres of soybean were impacted during the Derecho lodging disaster (Figure 8 and Table 2). Furthermore, we observed that out of the 52 counties, only 32 counties have more than 10 thousand acres per county impacted by the storm. Within these 32 impacted counties, we leveraged our proprietary yield prediction capability to quantify the yield potential prior to the storm, and we were able to quantify the total bushels lost following the windstorm. We observed the following in our findings:


As shown above and in Table 2, fields with moderate lodging were more frequent than fields with severe lodging and corn fields lodged more than soybean fields. We believe the reason for this difference is due to relatively more resistance of soybean to wind than corn.

#### *5.2. Temporal Behavior of Un-Lodged (Healthy) and Lodged Fields throughout the Observation Period*

The main difference in a field before and after lodging is the reduction of plant height. Therefore, the backscattering coefficients for lodged and un-lodged field will be different because for a lodged field, either one of the two conditions stated in Section 4.1 will hold. In this study, we compared nine randomly selected un-lodged fields to nine randomly selected lodged fields (Figure 13) using both the VH mean polarization backscatter and VV mean polarization backscatter. While observing Figure 13, on the one hand, un-lodged fields showed no change at all in both the VH and VV mean polarization backscatter. On the other hand, we saw an increase of approximately 6 dB in the VH mean polarization backscatter and 5 dB in the VV mean polarization backscatter for all the fields between the pre-lodging event date and the post-lodging event date respectively. Even though there is a slightly higher difference in the VH mean polarization backscatter, the sensitivity of VH polarization to lodging is similar to the sensitivity of VV polarization to lodging. In this study, the increase in both the VH and VV mean polarization backscatters follows the reported observations in [8]. In Chauhan, et al. [8], the authors observed a clear linear trend of increasing VH polarization backscatter and VV polarization backscatter with the increase in the lodging severity. It is worth mentioning that corn and soybean in all the fields are in the grain-filling period or plateau stage. At this stage, the height and greenness of the crop should be fairly constant, and this is why we see no change in the un-lodged fields. To further show how lodged fields and un-lodged fields differ on a large scale, we generated the VH and VV mean polarization backscatter over Dallas county in Iowa (Figure 14). By qualitatively observing the mean backscatter between the pre-lodging event date and the post-lodging event date in Figure 14, we saw an increase of approximately 3 dB in the VH mean polarization backscatter and 1 dB in the VV mean polarization backscatter. Based on the difference observed between the lodged fields and un-lodged fields in Dallas county, it is quite evident that our approach is sensitive enough to detect lodging.

**Figure 13.** Time series of lodged and un-lodged fields.

**Figure 14.** Time series of lodged and un-lodged fields in Dallas county, Iowa.

#### **6. Conclusions**

The potential and feasibility of using SAR data for monitoring the Derecho lodging disaster was demonstrated in this study. Though crop lodging assessment using SAR data have been shown in a few studies earlier, studies that have assessed crop lodging using satellite-based data at large spatial scale are still sparse and knowledge relating to the changes in SAR signatures was lacking in literature. With the advent of dense SAR time series from Sentinel-1 and proposed missions like NASA ISRO Synthetic Aperture Radar (NISAR), there is an increased interest in exploring SAR for lodging identification. The main conclusions of this study are summarized below.


Taken together, the results and the insights from this study demonstrate the practicability of using high resolution SAR remote sensing data for large scale identification of crop lodging. The approach employed is simple, effective, and can help decision-makers obtain critical information on lodging identification to support precision management in order to provide effective emergency relief. Future studies will evaluate if there is any improvement in the lodging detection performance when SAR and multispectral datasets are fused. We will also undertake quantitative measures to further validate our results. Using the validated crop lodging area in this study as a reference, we will investigate the use of deep learning models to generate a standard near-real time framework for crop lodging assessment. We will also explore the use of Gamma distribution for fitting the EM algorithm in the HMRF rather than Gaussian distribution. With adequate ground truth information and further validation, large-scale crop lodging assessment can improve crop yield forecasting and crop insurance, ultimately benefitting food production and food security initiatives.

**Author Contributions:** Conceptualization, O.A.A.; methodology, O.A.A.; software, O.A.A.; validation, O.A.A. and H.L.; formal analysis, O.A.A.; investigation, O.A.A. and H.L.; data curation, O.A.A. and A.D.S.; writing—original draft preparation, O.A.A.; writing—review and editing, O.A.A., H.L., J.J., R.P., A.S., and S.P.K.; visualization, O.A.A.; supervision, A.S.; project administration, R.P. and A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Corteva Agriscience™.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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