# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 4 Discussion  
### Accuracy Aspect  
In the previous section, it was shown that all rounds of testing showed good accuracy overall and there were differences between sites and between data from different preprocessing methods. To better understand these differences, we included statistics on the types and heterogeneity of land cover around the sites in our analysis. The land cover around the test sites is classified into the following four categories according to the original site description and satellite data observations: one infrastructure-dominated category (Infra) and three natural environment-dominated categories: pure vegetation covered area (N-Vege), mixed environment (N-Mix), and area barely covered by vegetation (N-nVege). The heterogeneity around the test sites was calculated on the basis of cloudless Sentinel-2 data in the summer of 2020. For each test site, the heterogeneity was calculated twice using two different buffers, with a radius of 17 m and a radius of 25 m, corresponding to the test rounds with a 3 \(\times\) 3 window filter and a 5 \(\times\) 5 window filter, respectively. For each test site, four values of heterogeneity were calculated, three based on three original bands of Sentinel-2 data (B2 blue, B3 green, and B4 red) and one based on NDVI data estimated using the red and NIR bands. Considering that the results from the 5 \(\times\) 5 window filter were obviously better than those of gamma- and non-filtering and were close to the results from the 3 \(\times\) 3 window filter, while each had better- and worse-performing sites, the impact of heterogeneity on the accuracy is discussed by comparing the results from the 5 \(\times\) 5 and 3 \(\times\) 3 window filters. By comparing the multiband heterogeneity of all test sites, a different level of correlation was found between the heterogeneity of the surrounding environment of the sites and the accuracy of the estimation. As shown in Figure 8, in most cases, the accuracy fluctuated in the opposite direction with the change in heterogeneity. Sites with large heterogeneity had relatively low accuracy. Conversely, sites with small heterogeneity had relatively high accuracy. Statistically, the overall correlation was not significant due to the different magnitude of the heterogeneity of the sites. When the land-cover information was  
Figure 7: Comparison of the results from the two different polarizations. taken into account, high correlation was found at the relatively bare sites, which were not dominated by vegetation (N-nVgee and Infra), especially when a larger buffer was used. When we further compared the performance of using two different buffers, we can see that, generally, the sites where the spatial heterogeneity was higher in the \(5\times 5\) buffer than in the \(3\times 3\) buffer tended to show better accuracy with the \(3\times 3\) window filtering process; conversely, when the heterogeneity around the station could be reduced by enlarging the buffer from \(3\times 3\) to \(5\times 5\), better accuracy was then reached with the \(5\times 5\) window filtering process. Additionally, a smaller spatial heterogeneity typically led to a smaller difference between the results from both filters. Generally, the results of point-based testing reflect that the accuracy of the estimation was relatively affected by the heterogeneity of the surrounding environment. More homogeneous surroundings, which usually resulted in greater matching of the available signal to the exact land cover on the test site, enabled better accuracy. The spatial filter in the preprocessing, therefore, played an important role. For highly heterogeneous sites, a filter with a larger buffer could help to alleviate the influence of redundant signals, whereas using a coarser filter would also bring a greater risk of deviating from the original site environment. Therefore, in the use of the Sentinel-1 data, it is an important step to choose a suitable spatial filter that can effectively improve the quality of the data for extracting useful environmental information.