**3. Methods**

A graphical overview of the framework used for this study is illustrated in Figure 4. Major steps of the framework include preprocessing, ratio image formation, and change detection classification approach. All the steps in this paper can be reproduced quickly and all analyses for the study were carried out using python programming language.

**Figure 4.** Approach for mapping the 2020 Derecho lodging disaster.

In this paper, our proposed approach requires some parameters to be set beforehand. The parameters that need to be set include (i) the neighborhood size of the non-local means filtering step; (ii) the kernel size of the majority filter; (iii) the structuring element of the morphological filter; and, finally, (iv) the maximum number of allowed change classes. Please note that while we identified optimal settings for these parameters, we found that the performance of our algorithm does not critically depend on the exact choice for these variables. This is true for the following reasons: (i) as non-local means filtering is performed very early in the workflow, the impact of changes in the neighborhood size is mitigated by subsequent processing steps such as the application of mathematical morphology. Hence, we found that varying the neighborhood size from its optimal value changed system performance only slowly; (ii) the increase or decrease in the kernel size of the majority filter slowly decreases our change detection performance, yet this reduction of performance does not become significant unless the kernel size is increased tremendously; (iii) from an analysis of a broad range of data from different change detection projects we found that (1) a 5 × 5 pixel-sized structuring element of the morphological filter led to the most consistent results; and that (2) change detection performance changed slowly with deviation from the 5 × 5 pixel setting. Hence, while 5 × 5 pixel was found to be optimal, the exact choice of the window size is not critical for change detection success; finally, (iv) the maximum number of allowable change classes is a very uncritical variable as it merely sets an upper bound for a subsequent algorithm that automatically determines the number of distinguishable classes in a data set. By presetting this variable to 3 classes we ensure that changes as a result of lodging are captured.

#### *3.1. Image Preprocessing*

Sentinel-1A preprocessing was carried out using SeNtinel Application Platform (SNAP) software version 6.0 (https://step.esa.int/main/toolboxes/snap/), an open source common architecture provided by European Space Agency (ESA). The preprocessing step includes orbit file correction, GRD border noise removal, thermal noise removal, calibration, filtering using refined Lee filter, radiometric terrain correction (RTC), and geometric terrain correction (GTC). For more details on the preprocessing step and the importance of RTC, the reader is referred to Ajadi, Meyer and Webley [17].

#### *3.2. Logarithmic Scaling and Ratio Image Formation*

While both VH polarization and VV polarization were sensitive to crop lodging assessment, we only used the VH polarization of Sentinel-1A dataset in this study because it depicts crop phenology very well and, moreover, it is very sensitive to crop canopy structure. In order to increase the detectability of lodging and to suppress background information from SAR data, ratio images (*XRis* = [*XRi*1, *XRi*2, *XRi*3]) were formed (Figures 5 and 6) using the pre-lodging event image and of the

post-lodging event image of similar geometry, respectively (Table 1 and Figure 3). Note that, due to the performed radiometric correction steps, images are not required to come from identical geometries. Also, because the pre-lodging and post-lodging event images are logarithmically scaled, the creation of ratio images is performed as a subtraction operation. Afterwards, a fast-non-local means filtering procedure was applied to all ratio images in order to filter out the speckle noise while preserving the details. The fast-non-local means uses redundant information to reduce noise and restore the original noise-free image by performing a weighted average of pixel values, considering the spatial and intensity similarities between pixels [17].

**Figure 5.** Ratio images generated between (**a**) 29 July 2020 and 22 August 2020; (**b**) 3 August 2020 and 15 August 2020; (**c**) 4 August 2020 and 16 August 2020.

**Figure 6.** Spatial mosaic of all ratio maps in Figure 5a–c.
