# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 4 Results
### Detection of Frozen/Unfrozen Plots
#### 4.2.1 Freezing Detection Results over Site A
As mentioned before, the increase in the number of anomalies in the time period from March onwards is due to rainfall or irrigation events, while the anomalies detected  
Figure 8: Percentage of plots detected as frozen in site A 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. Precipitation (P) and temperature (t) are given in the graph at the top. in October are due to the moderate rainfall which occurred on 23 September 2018 (Figure 8, graph at the top). For this same period, part of the anomalies could be due to tillage, because agricultural plots, particularly class LC1, are plowed, which increases the radar signal and sometimes leads to an anomaly (especially when plowing leads to high soil roughness). These three main causes of anomalies (rainfall, irrigation and roughness), as explained in Section 3, cause some unfrozen surfaces to be falsely detected due to the high increase in the SAR signal in the S1 time series at irrigation/rainfall/tillage time, and therefore, we will have a significant \(\Delta\sigma^{0}\) since the \(\sigma^{0}\) (\(d_{n}\)), which corresponds to a \(d_{n}\) close to a date of rain, irrigation or tillage, will most probably have a high value. However, as we will see in the upcoming sections, after the application of the temperature filter, these anomalies will be discarded. Freezing episodes in the VH ascending mode (afternoon) for the three land cover classes (Figure 8a-c), are less frequent, due to the higher temperatures during the day. Therefore, only two freezing episodes where detected for LC1, and LC2 on 23 and 24 January (temperature \(\approx\) 0 \({}^{\circ}\)C). For LC1, the percentage of frozen plots at these two dates is higher than 93% and \(\sim\)60% for LC2. LC3 detected five freezing episodes, two of which occurred on 23 and 24 January, with the percentage of frozen plots being, respectively, 77.5% and 89.1%; the other freezing episodes were detected on 12 December (t = -2.3 \({}^{\circ}\)C), with less than 60% of plots detected as frozen, and two in late January/ early February 2019 (t \(\sim\) 2.65\({}^{\circ}\) for both dates), with less than 31% of plots detected as frozen. The anomalies detected using the SAR ascending VH acquisitions for LC1 and LC2 mostly follow the same dates as those found in the morning (i.e., minimal detection of anomalies during the freezing period; Figure 8a,b) and the majority of detected anomalies outside of this period do not surpass 50% of the number of plots. The high number of detected anomalies in LC3 (Figure 8c), as we previously mentioned, is not indicative of the bad detection of our algorithm for LC3 due to the low number of plots in site A, and also due to the lower thresholds used for LC3, in comparison to LC1, and LC2 (Table 1). The freezing detection using SAR acquisitions in VV polarization often shows similar performance in comparison to VH polarization for the morning period (Figure 8d-f). For example, on 3 November and 12 February, where the reported temperatures were, respectively, \(\sim\)0 and \(\sim\)\(-\)1 \({}^{\circ}\)C, our algorithm applied over S1 images produced similar results as VH for the three land cover classes. However, VV was able to detect slightly more freezing plots on 27 March in comparison to VH (more than 70% for VH and less than 40% for VH); however, this is unlikely to be true since the reported temperature at these dates is around 3 \({}^{\circ}\)C. The higher rate of freezing detection at these dates using VV in comparison to VH is due to the lower threshold values used for VV in comparison to VH (Table 1), which causes the algorithm to detect more freezing episodes with lower thresholds. Indeed, this is also noticeable with the detection of freezing episodes in the afternoon, where, using VV, the algorithm was able to detect slightly more plots in comparison to VH. The detection of anomalies using VV polarization follows the same pattern as VH polarization.