# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 5 Discussion  
### Freezing Detection Using Sentinel-1 \(\sigma^{\circ}\) SAR Backscattering  
As evidenced in Sections 4.1-4.3, the behavior of the S1 SAR backscatter response differed greatly according to the land cover class. Therefore, it was necessary to detect freezing episodes with different thresholds based on the investigated land cover classes and the S1 polarization. In this study, the proposed approach to calculate the threshold values for freezing detection using S1 SAR images in both polarization modes (VH and VV) appeared to perform relatively well in determining the best thresholds for each land cover class.  
The detection of freezing events was performed by calculating the difference between the investigated \(\sigma^{0}\) at date '\(d_{i}^{\prime}\)', and a mean value of the S-1 SAR signal obtained by using the value of three previously detected maximum \(\sigma^{0}\left(max\_0^{0}\right)\) from several prior days. \(max\_0^{0}\) are obtained by detecting the maximum S1 SAR backscattered coefficients in a moving window of 15 days from and up-to date '\(d_{i}^{\prime}\)'. We chose the mean of the last three \(max\_0^{0}\left(mean\_0^{0}\right)\), instead of just the latest one, for several  
Figure 14: The distribution of frozen plots in site B in comparison to ERA5-Land’s soil and air temperatures, for each S1 acquisition, and using VH (**a**) and VV (**b**) polarizations after applying the temperature filter. (**a**) VH, and (**b**) VV.  
reasons. In this study, we rely on a change detection approach (difference between the acquired \(\sigma^{0}\) at date '\(d_{i}\)' and _mean_\(\sigma^{0}\)) to determine a freezing occurrence. Thus, using only the latest _max_o\({}^{0}\)_ may make our model susceptible to making false detections due to several phenomena (vegetation growth, rainfall, irrigation, tillage, or even signal noise) that lead to an increase in the difference between _max_o\({}^{0}\)_ and \(\sigma^{0}\) at date '\(d_{i}\)'. In addition, such phenomena can also lead to uncertainties when distinguishing between mild-to-moderate and severe freezing episodes. For example, after a rainfall or irrigation event, and even in less cold temperatures (between 0\({}^{\circ}\) and 3 \({}^{\circ}\)C), relying on only the latest _max_o\({}^{0}\)_, our algorithm can sometimes falsely detect mild-to-moderate frozen episodes as severe. Therefore, to mitigate the effect of these phenomena (vegetation growth, irrigation, rainfall, and tillage), the use of _mean_o\({}^{0}\)_ would improve our prediction accuracy. Indeed, our algorithm did not detect any presence of severe frozen plots with less cold temperatures in both the morning and afternoon periods. Moreover, severely frozen plots were detected only when the temperatures were at their lowest (period between November and February), and this was observed over both study sites. For this period (between November and February), the number of falsely detected frozen plots was also greatly reduced.  
Finally, the results are in accordance with the findings obtained via simulation in the study of [PERSON] et al. [20]. In their study, they also showed a strong potential for Sentinel-1 data to map freezing episodes at the plot scale in an operational capacity. The thresholds obtained are also consistent with the findings of the simulations. Simulation results in [PERSON] et al. [20] found that a signal drop of at least 3-4 dB corresponded to a freezing episode (depending on the type soil). With actual Sentinel-1 data, we considered for LC1 that freezing occurs when the signal drops below 3.5 dB, 2.8 dB for LC2, and 2.1 dB for LC3, and a severe freezing is detected for signal drops higher than 5.3 3.5 and 2.9 for, respectively, LC1, LC2, and LC3.