# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 5 Discussion
### Strengths and Limitations
While ERA5-Land soil temperatures can give a good representation of a soil's state, temperature is not the only parameter that causes a soil surface to freeze/thaw, and this is especially true for temperatures around 0 \({}^{\circ}\)C, during the afternoon period or during spring. Therefore, deriving a freeze/thaw map from ERA5-Land soil temperatures is not straightforward. In general, for temperatures lower than 0 \({}^{\circ}\)C, there are no issues when comparing our results and the reported temperatures by ERA5-Land. As seen in the results, for low temperatures (\(\leq\) 0 \({}^{\circ}\)C), the number of detected frozen plots was the highest. In addition, for slightly less cold temperatures(\(>0^{\circ}\)C), and while the number of detected plots was low, we have no way of determining if a freezing event occurred, and if so, the extent of its severity. 2. The large difference between ERA5-Land's 9 km spatial resolution and our plots' surface area (several hundred square meters) makes it very hard to do any meaningful statistical analysis. Even if we derive a freeze/thaw map using ERA5-Land soil temperatures using, for example, a threshold of \(0^{\circ}\)C, below which a surface state is considered frozen, it is not possible to aggregate freeze/thaw detections from plot resolution to ERA5-Land's resolution. Given the presented uncertainties, in this study we opted to perform qualitative analysis instead of quantitative analysis. In fact, given the unavailability of free and open access of high-resolution temperature data, it is difficult to do otherwise. However, the use of freely available low-resolution in situ temperature data allows us to develop an operational methodology for the mapping of freezing episodes that is easily reproducible elsewhere, with the minimum amount of data. Secondly, the proposed method is capable of showing plots that are supposed to be moderately to severely frozen. What is certain, even if we have no ground truth, and this is thanks to the simulation data present in the paper of [PERSON] et al. (2018), is that if the signal drops for bare soils by at least 3 dB, frost has occurred. In contrast, the distinction between moderate and severe freezing remains arbitrary due to the chosen threshold that distinguishes between mild-to-moderate and severe freezing conditions. As a result, a small portion of the plots estimated to be severely frozen may actually be moderately frozen and a small portion of the plots mapped as moderately frozen may actually be severely frozen. What is certain, however, is the presence of multiple freezing conditions that we are capable of detecting. In any case, due to the lack of very high spatial resolution field data and using a threshold-based method, there may be a slight mixing between classes, and it is difficult to do otherwise for large-scale mapping. A third limitation is linked to the temporal resolution of Sentinel-1 images. Sentinel-1 does not provide daily images, and thus frozen episodes that occur on days when there are no acquisitions cannot be detected. Sentinel-1 revisit times depend on the region, and in worse case acquisitions can occur once every six days. Finally, the detection of freezing episodes in areas encountering seasonal freezing (frost persists for several months) could be problematic. In this study, we calculate the mean of previous \(max\_o^{0}\) by making the assumption that \(max\_o^{0}\) did not occur on a freezing date. However, in areas affected by seasonal freezing, the mean of the previous \(max\_o^{0}\) will not be guaranteed to be detected on an unfrozen date. Therefore, the difference between \(o^{0}\) and the mean of previous \(max\_o^{0}\) will be very small, and a false detection will be made.