# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 4 Results
### Detection of Frozen/Unfrozen Plots
#### 4.2.2 Freezing Detection Results over Site B  
Figure 9 shows the performance of our algorithm across three reference plots corresponding to three land cover classes over site B (near Strasbourg, France), using S1 images in VH (Figure 9a-c) and VV (Figure 9d-f) polarizations. Over the reference plot corresponding to LC1 (Figure 9a), 13 freezing episodes (morning and afternoon combined) between mid-November and mid-February were detected using VH polarization. In the same time period, the algorithm did not detect any anomalies. For the reference plot corresponding to LC2 (Figure 9b), 19 freezing episodes (morning and afternoon) were detected in VH polarization also between mid-November and mid-March. Of these 19 freezing episodes, two episodes in early February corresponded to anomalies because the temperature at the time of acquisition of S1 was above 5 \({}^{\circ}\)C for both dates, which suggests a very low to no probability of freezing. Finally, for LC3 (Figure 9c), the algorithm detected 13 freezing episodes (morning and afternoon) for the period between mid-November and mid-February, with only one anomaly in mid-November. The majority of the anomalies for the three land cover classes appear to be detected outside of the freezing period that extends from November to February; this is in accordance with the detected anomalies in site A. Moreover, the higher number of detected freezing episodes over site B in comparison to site A is mostly due to the lower yearly average temperatures in comparison to site A, which increases the occurrence of freezing events. Our algorithm detected fewer freezing episodes across the three land cover classes when used with VV polarized S1 images over the same reference plots. For LC1 (Figure 9d), only six freezing episodes were detected for the period between mid-January and mid-February, and one freezing episode was detected in early March. However, the algorithm also detected seven anomalies between mid-February and mid-March. For LC2 (Figure 9e), the algorithm detected ten freezing episodes between mid-November and mid-February in VV polarization with no anomalies. Finally, for LC3,  
Figure 9: Freezing detection over one reference plot by land cover class in site B for each S1 acquisition date, using VH and VV polarizations. (**a**) VH-LC1, (**b**) VH-LC2, (**c**) VH-LC3, (**d**) VV-LC1, (**e**) VV-LC2, (**f**) VV-LC3. the algorithm detected 12 freezing episodes between mid-November and mid-February, of which two were anomalies (temperature above 5 \({}^{\circ}\)C for both dates). Figure 10 shows the distribution of the frozen episodes and anomalies, across all the plots in each land cover class for both VH and VV acquisitions in ascending and descending modes. The period of the freezing episodes' occurrence is similar to those shown for site A, with the overwhelming majority of freezing episodes detected between mid-November and mid-February, and this was observed for both VH and VV polarization (Figure 10). The percentage of frozen plots is tightly coupled with the temperature for both VH and VV polarizations. For temperatures below 0 \({}^{\circ}\)C, and using VH polarization, the percentage of detected frozen plots for LC1 was higher than 50%, and 80% of plots were detected as frozen in four out of the five freezing episodes. For temperatures around 0 \({}^{\circ}\)C (t between 0 and 1 \({}^{\circ}\)C), at least 50% of plots in LC1 were detected as frozen in 50% percent of freezing episodes. Finally, for temperatures above 1 \({}^{\circ}\)C, the percentage of frozen plots was less than 15%. For LC2, and LC3, in VH polarization, similar patterns to LC1 were observed. However, for the lowest temperatures (t \(<-1\)\({}^{\circ}\)C), our algorithm detected fewer frozen plots in LC2, and LC3 in comparison to LC1 (Figure 10c,d).