## 5 Discussion  
A comparison of RADARSAT-2 FQ15 HH and HV and FQ25 HH and HV images confirms the higher sensitivity of the HV polarization to the presence of surface water. Based on these results, HV has a higher capability in differentiating between land and water. The backscatter coefficient at cross-polarization is contaminated by the noise floor. This results in an inability to detect mudflat areas relative to the HH polarized imagery, as shown in the four zones identified by arrows in Figure 6. One can assume that the smooth surface combined with soil water saturation in these mudflats made HV decrease more significantly in comparison to HH. Consequently, the effect of dielectric constants on backscattered signals can be neglected in this case [20; 42; 43; 44]. Using both polarizations for the classification increases the contrast between land and water and therefore improves retrieval accuracy. Additionally, a simulated RCM comparison indicates the compact parameters RH and RR on RCM should be able to be used as successfully as the HH/HV polarizations for separating land and water, and more specifically for the detection of mudflat areas. It is preferable to acquire RADARSAT-2 data at the lowest water level possible with no ice cover; however, early winter periods generally contain the lowest water levels (Figure 3) [4], allowing submerged features to become more visible than at other times of the year. Even if the area contains ice, these images could also be valuable for extracting the low water line at the lowest water level possible (Figure 12), increasing navigation safety. For automatic extraction and interpretation purposes it is preferable to have an image with no ice, but as demonstrated in Figure 12, the image with ice can be used to manually extend the shoreline to the lowest water level. Results of the change detection analysis completed for chart 6428 illustrates the degree to which remotely sensed data can improve Canadian nautical charts in remote areas. The use of RADARSAT-2 imagery is sufficient to separate land and water with a reasonable accuracy. Consequently, this imagery can be used to better define the main Mackenzie River channel, as well as its larger tributaries. This was achieved through a simple image segmentation process, allowing the shoreline of approximately 18 km of the river to be mapped with minimal effort and at little cost. In its current form, the change detection approach could be applied for the entire length of the Mackenzie River, allowing for a  
Figure 11: Segmentation result showing extracted main Mackenzie River channel and tributaries over a Landsat image from 1972 (**a**), a Sentinel-2 image from 2018 (**b**) and over chart 6428 (**c**). Imagery © 1972, U.S. Geological Survey and © 2018, Copenicus. current representation of the shoreline to be incorporated into the river's charts. At a minimum, this would provide mariners with a more representative indication of the position of shoreline and island features within the waterway, allowing them to make more informed decisions for safe navigation. Since 2014, CHS has been using this proposed approach to update different charts on the Mackenzie River. Figure 13 is an example of another chart (6424) that was updated with Earth observation (EO) data. In this example, the advantages of using the hybrid approach of radar (low water level) and optical (mid-water level) data to chart land and drying areas are well illustrated. In order to simplify the chart update process in dynamic areas, all features are extracted as vectors and coded in the International Hydrographic Organisation (IHO) S-57 Electronic Navigation Chart (ENC) format [45]. This format ensures easy integration with CHS's existing charts and future chart updates. For a considerable number of the examined areas, the current chart cannot accurately be used for navigational purposes, requiring mariners to rely entirely on their judgement for navigating these sections of the Mackenzie River.