# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 1 Introduction  
In the terrestrial cryosphere, freeze/thaw (FT) state transitions play an important and measurable role in climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. Traditionally, ground observations can be used to monitor the FT state, for example, in quantitative monitoring through globally scattered measurement stations from the Global Terrestrial Network for Permafrost (GTN-P) and its circumpolar active layer monitoring (CALM) program based on the continued measurements of air temperature and soil temperature of different soil layers for decades [1, 2, 3], as well as local research programs with a focus on specified regions, such as the Asian monsoon experiment on the Tibetan Plateau (GAME-TIBET), which monitors the soil water and heat processes on the Qinghai-Tibet Plateau with high precision by establishing automatic weather stations [4]. Detection based on ground observations is often very accurate and detailed; however, due to the complex environment of the cryosphere and the high cost of site establishment and maintenance for long-term measurements, the distribution of sites is relatively sparse with limited amounts. In comparison, the remote sensing approach provides the possibility of routine monitoring of land surface information on a larger scale with better efficiency. While visible andinfrared light can only indirectly infer the FT state by providing information such as terrain, snow area, and surface temperature due to their infeasibility of penetrating the land surface and instability facing various weather and atmospheric conditions, microwave remote sensing has advantages because it is not affected by weather and light, it can penetrate low to medium coverage of vegetation, snow, and ice, and it is sensitive to dielectric constant changes in the surface, which can represent soil water content changes. In view of the principal capacity of microwave remote sensing to provide effective monitoring of landscape FT dynamics, satellite radiometer and scatterometer measurements have been used to monitor the landscape FT state and its periodic transition for decades [5; 6; 7; 8], and many algorithms have been developed and evaluated over time. There are algorithms that are highly dependent on in situ data, such as the double-index algorithm [9; 10; 11; 12; 13], decision tree algorithm [14], and standard deviation algorithm [15], and algorithms that rely more on microwave remote sensing data by determining freeze and thaw state references, such as the seasonal detection method [16; 17; 18] and polarization ratio (PR)-based algorithm [19; 20], as well as their derivations, such as the modified seasonal threshold algorithm (MSTA) [21] and the discriminant function algorithms [22; 23; 24]. With the continuous development of algorithms, an increasing number of FT products based on microwave remote sensing data have emerged and been evaluated in many studies, especially L-band data, which have received much attention since the successful launch of the Soil Moisture and Ocean Salinity (SMOS) mission and the NASA Soil Moisture Active Passive (SMAP) mission [16; 18; 19; 25; 26]. Validation studies for the L-band FT detection algorithms cross many different areas globally, such as the assessment of the performance of SMAP FT products by [PERSON] et al. (2019) [27] using surface air temperature data from approximately 5000 global weather stations and the evaluation with a regional focus on the boreal forest and tundra in the Northern Hemisphere, such as Finland, Alaska, and Quebec [16; 19; 20; 28; 29; 30; 31], as well as the analysis of plateau regions, such as the evaluation of the Advanced Microwave Scanning Radiometer 2 (AMSR2) FT product, the Making Earth System Data Records for Use in Research Environments (MEASUREs) FT product, and the SMAP FT product against the soil temperature at the 5 cm depth collected in China and the air temperature collected over the Qinghai-Tibetan Plateau [32].