# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 5 Conclusions  
Throughout the study, the seasonal threshold approach can be adequately used on C-band Sentinel\(-\)1 data. There is still space for accuracy improvement, for example, by improving preprocessing, such as filter adjustment, and further improving algorithms, such as threshold optimization. As a next step, it will be interesting to extend the recent point-based experiment to a larger area. The experiences collected in this study can be further used for testing and improving the functionality of the algorithm at the regional scale and for further exploration and discussion of the contribution of related variables such as land cover in the FT transition process.  
Compared with L-band passive data, C-band SAR data have weaknesses in penetration depth and temporal coverage, but they can fill in the gaps of existing L-band products in spatial resolution. For more specific regional research, especially in permafrost landscapes with ever-changing environments, which have increasingly drastic changes in today's climate change environment, the appropriate use of C-band data can provide more possibilities for detection and monitoring.  
Conceptualization, Y.C. and L.W.; methodology, Y.C. and L.W.; software, Y.C. and L.W.; validation, Y.C.; formal analysis, Y.C.; investigation, Y.C.; resources, M.B. and R.L.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, L.W. and R.L.; visualization, Y.C.; supervision, L.W., M.B. and R.L.; project administration, Y.C.; funding acquisition, L.W., M.B. and R.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The remote sensing data used in this work, including the Sentinel-1 and Sentinel-2 data, are provided by European Space Agency (ESA). These data can also be found and pre-processed on the platform Google Earth Engine ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)), accessed on 10 December 2021). The in situ data were collected based on the online data report series Nordicana D, published by the Centre d'etudes Nordiques (CEN) for Northern Studies since 1964 ([[https://www.cen.ulaval.ca/nordicanad/en_index.aspx](https://www.cen.ulaval.ca/nordicanad/en_index.aspx)]([https://www.cen.ulaval.ca/nordicanad/en_index.aspx](https://www.cen.ulaval.ca/nordicanad/en_index.aspx)), accessed on 10 December 2021).  
The authors kindly acknowledge the support of the Canadian Space Agency through the Canadian SMAP Program led by Environment Canada. We acknowledge the contribution made by ESA in making Sentinel data easily available and at no cost. We also thank the Centre d'Etudes Nordiques for logistics support and all of our INRS and LMU colleagues, who were involved in the field campaigns. [PERSON] especially thanks the Chinese Scholarship Council for generous support throughout the study. This work was also supported by research grants from the Natural Science Foundation of the Jiangsu Province (BK20200828).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Landcover** & \multicolumn{2}{c}{**Infra**} & \multicolumn{2}{c}{**N-Vegeg**} & \multicolumn{2}{c}{**N-Mix**} & \multicolumn{2}{c}{**N-nVege**} \\ \hline Filter & W 3°3 & W 5°5 & W 3°3 & W 5°5 & W 3°3 & W 5°5 & W 3°5 & W 5°5 \\ \hline Mean & 0.46 & 0.47 & 0.63 & 0.63 & 0.59 & 0.59 & 0.53 & 0.61 \\ StD. & 0.06 & 0.08 & 0.11 & 0.11 & 0.10 & 0.08 & 0.03 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average and standard deviation of each land-cover cluster.