**1. Introduction**

With the increase in global population and the increase in food demand, the monitoring of agricultural activities has been of utmost importance. The increasing frequency and intensity of extreme weather events have also made monitoring of agricultural fields very critical. Lodging, the tilting of plant stems from their natural upright position, is a major yield limiting factor to crops such as corn, wheat, and barley [1–3]. Corn is vulnerable to lodging during its growing stages, particularly between early to late vegetative period [4]. Lodging in corn could be as a result of insufficient root growth due to soil compaction, or due to increased occurrence of heavy rain

and derechos (wind storms). According to the National Oceanic and Atmospheric Administration (NOAA), derechos are straight-line windstorms that are associated with a fast-moving group of severe thunderstorms. The winds are destructive and can be as strong as those found in tornadoes and hurricanes. Derecho lodging results in serious damage to crop growth and development as it impedes the circulation of water and nutrients in the plant which in turn suppresses photosynthesis, leading to deterioration of grain quality and total yield loss [5]. For a more comprehensive overview on the mechanics of lodging, factors affecting lodging, and crop yield response to lodging, the reader is referred to Chauhan, et al. [6]. Lodging also reduces grower's profitability and, for this reason, it is important to detect lodging quickly, map its extent, and quantitatively measure its severity. Accurate and timely mapping of lodged fields can help guide farmers during harvest operations, help crop insurance companies during crop loss assessment, and can improve crop yield forecasts [7].

Mapping of lodged fields is typically based on visual inspection (field-based approach). However, this approach is extremely laborious, time consuming, and is infeasible for large areas. In recent years, remote sensing has been used for mapping lodging since it offers a more scalable approach and is also cost effective. Multi-temporal images acquired by optical [8,9] and radar [10,11] sensors have routinely been applied for lodging identification. Since these two sensor types have their unique sensitivity and imaging characteristics, their performance in the mapping of lodging varies.

Recently, synthetic aperture radar (SAR) data have gained considerable interest in lodging applications because SAR is an active sensor, operating without regard to weather, smoke, cloud cover, or daylight [12]. SAR sensors offer a clear advantage because of their unique scattering sensitivity to crop structure and large area coverage.

In Chauhan, et al. [8], the authors used time series of SAR backscatter, SAR coherence, and spectral reflectance derived from Sentinel-1 and Sentinel-2 data to detect lodging incidence and understand the effect of lodging in wheat. The most reliable discriminators for differentiating lodged wheat from healthy wheat were Sentinel-2 red edge band (740 nm), Sentinel-2 near infrared band (865 nm), and Sentinel-1 VH backscatter. Shu, et al. [13] have used the dual-polarization of Sentinel-1A data to develop a change detection method using plant height before and after lodging in maize to calculate the lodging angle and monitor the lodging degree. The results showed that VV backscatter was sensitive to lodged maize while the ratio of VH to VV backscatter was sensitive to non-lodged maize. In a similar study, Chauhan, et al. [14], explored the advantage of multi-sensor SAR data (Sentinel-1 and RADARSAT-2) to develop a quantitative approach to detect crop lodging stages (moderate, severe, and very severe) based on the crop angle of inclination. Quantitative relationships using support vector regression (SVR) models were established between remote sensing derived metrics from Sentinel-1 and RADARSAT-2 timeseries and field measured crop angle of inclination values [14].

While several researchers have predominantly used the single and dual-polarization of Sentinel-1 to address crop lodging, others have focused on using the multi-configuration (multi-polarization and multi-incidence angle) data from RADARSAT-2. For example, Yang, et al. [15] used a time series of Radarsat-2 images and target decomposition techniques to derive a set of polarimetric features and backscattering intensity features to compare typical lodged fields and normal fields. In their study, they found that polarimetric ratios (especially those based on odd/double scattering) were sensitive in distinguishing lodged and normal fields. In a different study, Chen, et al. [16] used polarimetric features from Polarimetric SAR (PolSAR) to identify sugarcane lodging. The authors found that several polarimetric features, such as horizontal transmit and vertical receive (HV) intensity, double-bounce scattering, and volume scattering derived from RADARSAT-2 data were helpful in sugarcane lodging identification.

Despite these and other studies carried out throughout the last decade, the integration of SAR remote sensing into routine mapping of lodging and severity assessment remains difficult for the following reasons: (a) The acquisition of SAR dataset to coincide with the specific date of lodging is not always feasible; (b) The heterogeneous distribution of lodging makes it difficult to be detected with SAR and also, in particular, in order to map lodging precisely, the size of the lodged field must be larger than the spatial resolution of the SAR sensor; (c) The mapping of lodging over large spatial extent and determination of lodging rate; (d) The acquisition of ground truth data (known lodged fields) to evaluate lodging severity can itself be an overwhelming task as it is extremely labor intensive and time consuming; (e) The accuracy assessment of the lodging severity also has some shortcomings since there are no standard scales to quantify lodging into categories such as severe, moderate, or mild.

To address and overcome some of these challenges, we used the 2020 Derecho event in Midwest U.S. as a case-study to evaluate SAR for large-scale lodging detection and mapping. The main objectives and novelty of this study are to:


In this paper, the proposed change detection method for lodging utilizes the concept of ratio image generation. The generation of ratio images suppresses background information while enhancing change information [17]. The generated ratio images were filtered using a non-local means filter [18,19] and classified into different classes using the Hidden Markov Random Field (HMRF). The HMRF considers the contextual information of neighboring pixels (i.e., a neighboring pixel is expected to have similar intensities and similar class labels) during classification. Published methods differ in their approach to extract change detection map. In the work done by Kasetkasem and Varshney [20], the authors used a MRF to model noiseless images for an optimal change image using the maximum a posteriori probability computation and the simulated annealing (SA) algorithm. In Zhao, et al. [21], the authors combined Voronoi Tessellation (VT) and HMRF based Fuzzy C-Means (FCM) algorithm (VTHMRF-FCM) for texture image segmentation. Similarly, in Yang and Yi [22], a novel method based on applying HMRF and generative adversarial network (GAN) on high-resolution SAR images was used for ship detection. To our knowledge and according to Chauhan, et al. [6], the use of satellite-based change detection for crop lodging is sparse. To date, there is no method available for corn and soybean lodging over large spatial areas.
