# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 6 Conclusions  
In this study, we propose a new approach for mapping freezing episodes over agricultural areas, at plot scale, and in near real-time using Sentinel-1 SAR images acquired in VH and VV polarizations. Our approach relies on change detection in the S1 SAR backscattering coefficients (\(o^{0}\)) to detect frozen plots and can be divided into two major parts. The first part is responsible for calculating thresholds in order to distinguish between unfrozen, mild-to-moderately frozen, and severely frozen plots. Given that SAR backscatter fluctuations due to freezing can greatly vary between land cover classes, we opted to calculate the thresholds for each land cover class, and for each polarization (VH and VV). The second part of our algorithm is charged with the detection of frozen/unfrozen plots. The detection of a plot's state at a particular date '\(d_{i}\)' is done by calculating the difference between \(o^{0}\) at date '\(d_{i}\)' and a mean value of the S-1 SAR signal obtained by using the value of three previously detected maximum \(o^{0}\left(max\_o^{0}\right)\) from several days prior. \(max\_o^{0}\) values are obtained by detecting the maximum S1 SAR backscattered coefficients in a moving window of 15 days from and up to date '\(d_{i}\)'. Finally, due to several phenomena which can cause false freezing detection (rainfall, irrigation, tillage), a filter based on the registered air temperature at S-1 SAR's acquisition time was introduced at the end of the detection chain. Our algorithm therefore will consider a particular plot as unfrozen if the reported temperature is higher than 3 \({}^{\circ}\)C.  
Our approach was tested over two agricultural sites in France, site A (near Paris), and site B (near Strasbourg). Site A has, in general, higher mean yearly temperatures than site B, and the occurrence of freezing episodes is less common. The results show that our algorithm was capable of detecting all major freezing episodes in both sites, using VH or VV, and in both the morning and afternoon periods. The results also show that the detection of frozen plots using VH could be less ambiguous than using VV, given the higher thresholds used in VH than in VV (the radar signal decay due to freezing is greater in VH than in VV), which resulted in a lower number of false detections. The higher thresholds used for LC1, in comparison to LC2 and LC3, also showed that LC1 has, on occasion, better accuracy when detecting frozen plots over both study sites. Finally, the qualitative analysis between our results and ERA5-Land's soil and air temperatures showed high agreement. In essence, there were no freezing episodes detected in ERA5-Land that were not detected using our algorithm. However, our algorithm, which uses Sentinel-1 high resolution SAR images, is capable of detecting freezing episodes at the plot scale, in comparison to ERA5-Land which shows a coarser representation of freezing episodes (spatial resolution of 9 km).  
Conceptualization, I.F. and N.B.; Data curation, I.F.; Formal analysis, I.F., N.B. and H.B.; Investigation, I.F. and N.B.; Methodology, I.F., N.B. and H.B.; Software, I.F.; Validation, I.F., N.B. and M.Z.; Writing--original draft, I.F.; Writing--review & editing, N.B., H.B. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the French Space Study Center (CNES, TOSCA 2020 project) and the National Research Institute for Agriculture, Food and the Environment (INRAE).  
Authors wish to thank the French Space Study Center (TOSCA 2020) and the National Research Institute for Agriculture, Food and the Environment (INRAE). The authors wish also to thank the European Space Agency (ESA) for the Sentinel-1 data and ECMWF for the ERA5-Land data.  
The authors declare no conflict of interest.