# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 4 Results
### Comparison with ERA5-Land Dataset  
The assessment of accuracy for our algorithm to detect frozen/unfrozen plot surface states is a challenging issue, as direct ground measurements of the actual plot surface states are not available. Therefore, it is necessary to resort to proxy variables, such as the air temperature [60] or soil temperature [28]. However, proxy variables might not necessarily represent the surface state and may sometimes introduce uncertainties [16]. In addition, the proxy variables (soil and air temperatures) should be acquired in the same timeframe as Sentinel-1 acquisitions, as any interpolation of temperature data to Sentinel-1 acquisition time would inevitably lead to uncertainties. In this study, hourly soil and air temperature data from ERA5-Land were used. ERA data products have been utilized in a multitude of studies for the validation of freeze/thaw products [15; 16]. A previous ERA product, namely the ERA Interim, was found to be the most accurate reanalysis data product available [49; 50]. Moreover, ERA5-Land, which reports more accurate air temperatures than ERA interim [63], has a standard deviation to in situ air temperatures of 1.4 \({}^{\circ}\)C [64]. Over both of our study sites, the reported standard deviation to the in situ air temperatures was \(\sim\)1 \({}^{\circ}\)C. Finally, due to the large difference in spatial resolutions between ERA5-Land data products (9 Km) and our results (plot scale), a qualitative analysis will therefore be made between ERA5-Land (soil and air temperatures) and the percentage of frozen/unfrozen plots at a certain date, using the three land cover classes combined (i.e., percentage of the combined detected mild-to-moderate and severe frozen plots over the entire study site). Over site A (near Paris), Figure 13a shows that when both the soil and air temperatures reported by ERA5-Land were below 0 \({}^{\circ}\)C, the highest percentage of frozen plots detected using VH occurred for both the both morning and afternoon acquisitions. For these temperatures, the reported percentage of frozen plots was between 42% and 93%, with 85% of frozen episodes having more than 75% of plots detected as frozen. For ERA5-Land temperatures (both air and soil temperatures) higher than zero (between 0 and 3 \({}^{\circ}\)C, since the temperature filter is applied for t > 3 \({}^{\circ}\)C), the reported percentage using VH was lower, and varied between a few plots detected as frozen and approximately 40%. Freezing detection results obtained from VV acquisitions (Figure 13b), as seen in the previous sections, show the same behavior in comparison to VH. The comparison of our results to ERA5-Land's soil and air temperatures for site B (Strasbourg) show that the detection results obtained using VH and VV are mostly similar (Figure 14a,b). For both  
Figure 13: The distribution of frozen plots in site A in comparison to ERA5-Land’s soil and air temperatures at each S1 acquisition, and using VH and VV polarizations after applying the temperature filter. (**a**) VH, and (**b**) VV. polarizations, as in site A, the most detected frozen plots occurred on dates when both ERA5-Land air and soil temperatures were significantly lower than 0 \({}^{\circ}\)C (between 19 and 25 January 2019 and 4 and 12 February 2019). On these dates, the percentage of frozen plots varied between 62% and 95%. For less cold ERA5-Land soil temperatures (0-1 \({}^{\circ}\)C), the percentage of frozen plots decreased; with a percentage of detected frozen plots ranging from 15% to 67% with a mean of 33% using VH, and from 11% to 53% with a mean of 59% using VV. For ERA5-Land soil temperatures between 1 and 3 \({}^{\circ}\)C, the percentage of frozen plots did not exceed 7.4% using VH with a mean of 2.5%.