# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 1 Introduction
Thus, in the event of a post-freeze episode, the freeze event will not be captured. The algorithm also considered a stable soil roughness over time. A variable roughness could also produce false negatives, as high roughness will increase the return signal [39], and the difference \(\Delta\sigma^{\circ}\) (\(\sigma^{\circ}\) with freezing and high roughness \(-\sigma^{\circ}\) with thawing and low roughness) could be well lower than the 3 dB threshold. Finally, the study was carried out with data during the winter season, and over surfaces with lower vegetation cover; therefore, it would be interesting to develop an algorithm that works over different vegetation types. In this study, we propose a refined algorithm based on [PERSON] et al. [20] that is capable of detecting frozen/unfrozen episodes in near real-time, with a temporal resolution of at least once each six days, at plot scale, and over different vegetation types. To overcome some of the limitations encountered during the previous study, two additional datasets will be used: (1) in situ temperature data using the measured temperatures from a weather station network for the refining of the detection results and (2) land cover classes. The inclusion of temperature data will allow the removal of some false positives (unfrozen surface detected as frozen) that could occur in spring, due to specific events such as irrigation [40; 41], blowing [39] or vegetation growth [42]. In effect, as the mapping of a surface state as either frozen/unfrozen on a given date '\(d_{i}\)' is most often based on the surface state from previous dates, a difference between the radar signal at date '\(d_{i}\)' and the radar signal acquired previously can be equal or less than \(-\)3 dB. This difference is observable if at previous radar acquisitions, the soil was wet (due to rainfall or an irrigation event), while the radar backscattering coefficient at date '\(d_{i}\)' was acquired when the soil was less humid (radar signal increases with humidity [43]). Moreover, the difference between a radar signal at date '\(d_{i}\)' and those acquired previously could be less than \(-\)3 dB if the radar backscatter from previous dates was acquired over rough soil, while at date '\(d_{i}\)' the soil was smoother (radar signal increases with roughness [39]). Finally, the difference between a radar signal acquired at date '\(d_{i}\)' and backscatter values acquired previously could also be less than -3 dB due to the vegetation growth cycle of some crop types, such as, for example, in the case of wheat [42]. [PERSON] et al. [42] showed that the C-band SAR backscattering signal in VV polarization decreases gradually between the germination phase of wheat, which usually occurs by the beginning of January, and the heading phase occurring between mid-March and mid-April. In the heading phase, the C-band SAR backscattering signal attains extremely low values due to extreme vegetation attenuation. Meanwhile, land cover classes will be used since the SAR backscatter does not decrease during freezing events with the same magnitude over different vegetation types [44; 45]. Finally, the generated plots' surface state maps, which describe a surface state as either frozen or unfrozen, will be compared to the fifth Generation ReAnalysis (ERA5-Land) hourly soil and air temperature data products provided by the European Center Medium range Weather Forecasts (ECMWF) [46]. Several previous studies have used soil and air temperatures as proxies for frozen/unfrozen states [16; 17; 28; 47]. In addition, ERA5-Land predecessor ERA interim has shown close agreement with in situ temperature data [48; 49; 50].