# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 5 Discussion
### Strengths and Limitations  
The results of our algorithm show that freezing episodes could be successfully detected using the high-resolution Sentinel-1 SAR images, with, at worst, a temporal resolution of six days. However, the implementation of the proposed method in a near real-time scenario primarily depends on the delivery time of S1 images. S1 images are usually delivered by ESA in a \"fast 24 h\" delivery mode. This mode ensures that S1 images are available for download 24 h after the satellite acquisition. Considering that the pre-processing of S1 images and applying the proposed algorithm is automatically performed with minimum human intervention, frozen plots could be detected in a few hours after the reception of S1 images based on the the size of the studied area. To detect a freezing episode, we rely on a simple but effective algorithm that requires only the calculation of two thresholds (to distinguish between unfrozen, mild-to-moderately frozen, and severely frozen) for each land cover class, and two additional datasets (land cover classes, and in situ air temperatures). The land cover classes were provided from the detailed graphical parcel registry (RPG), which is available over all France. However, RPG was only needed to derive three super land cover classes (LC), where each LC encompassed several crop types. Hence, if no land cover maps are available, the user can calculate a single set of thresholds for the entirety of the studied plots, which might lead to uncertainties in the detection of freezing in some of the land cover classes. In addition to the land cover classes, we used in situ air temperatures from the nearest weather station to (1) calculate the threshold-values, and (2) apply the temperature filter. Therefore, in remote areas, or when no in situ temperature data are available, they can be substituted by the less accurate, but globally available, ERA5-Land's air or soil temperatures. Using these three datasets (Sentinel-1 SAR, land cover classes, air temperatures), our algorithm provided near real-time results of plots' frozen states, which distinguishes between unfrozen, mild-to-moderately frozen, and severely frozen. The strength of our algorithm is multi-faceted. First, it is computationally inexpensive, and has the potential to detect freezing episodes at near real-time. In general, S1 SAR images are available 24 h after acquisition; therefore, frozen plots could be detected a day after the event. Secondly, over agricultural areas, vegetation growth, tillage, and irrigation can cause large fluctuations in the backscatter response, which has the adverse effect of causing uncertainties when detecting freezing episodes, as witnessed in previous studies [15; 16]. In this study, we do not rely on the direct value of the \(\sigma^{0}\) to detected freezing, but rather on the difference between the acquired \(\sigma^{0}\) and the mean of the previous three calculated \(max\_o^{0}\). This approach mitigated the effects mentioned previously, and allowed us to precisely detect freezing episodes over agricultural areas and pastures. There are, however, limitations in the operational large-scale mapping of freeze/thaw detection at the plot scale. First, the validation of freeze/thaw maps is strewn with pitfalls given the scarce availability of relevant data such as soil ice contents. High-resolution meteorological data are also not free, or available as open access, and the only accessible temperatures data, as mentioned in our article, are several km away from the study sites. Therefore, in this study, as many previous studies (e.g., [15; 16; 17; 28; 47]) that attempted to detect freeze/thaw episodes, we relied on proxy variables, such as air and soil temperatures from ERA5-Land, in order to assess the performance of our algorithm. For example, [PERSON] et al. [16] used ERA interim temperatures in order to derive a freeze/thaw product and thus a quantitative analysis was possible. However, such quantitative analysis was not relevant for our study due to the following reasons:  
1.