# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## 3 Analyses and Discussion
### Discussion  
In general terms, the complex structure of the wetlands, makes them a challenging land cover class for classification. As SAR sensors can often penetrate through herbaceous vegetation (C-band), the stronger backscatter signal is expected from wetter surfaces then the one from a drier surface [37], thus the wetter surfaces are easier to identify through remote sensing techniques [3], which makes the detection of open water bodies without vegetation relatively simple as weak or no signal returns to the antenna. When the water level is high or the wetlands are dominated by lower vegetation, the radar signal is often reduced [40], while when the water level is low related to the vegetation, double-bounce scattering occurs [41]. In this study, the backscatter VH values from the open water class range from \(-25\) dB in the leaf-off season, and \(-23\) dB in the pick of the leaf-on season, indicating low vegetation presence. Similar results are obtained from the VV polarization, with backscatter values ranging from \(-16\) to \(-21\) dB. The backscattering values of the other classes depend on both vegetation and water level. Thus, in the leaf-off season when the water level is high, the vegetation presence is low. The swamp class in the leaf-off season has low backscatter values, indicating low or no vegetation at all. Because of the medium resolution of both optical and radar sensors used in this study, the other classes include more heterogeneous land covers, which open a wide range of backscatter values and thus a mixture of the classes can occur. While the NDVI and LST values only present the vegetation presence and the temperature in one pixel, relating them with data retrieved from a sensor with different characteristics as radar can be of great importance. As seen in Table 5, the highest relation between the investigated characteristics has been noted in the NDVI-LST relation, which was expected as the LST calculation is directly connected to the NDVI values and several studies have found a strong correlation between them [42]. The statistical results in this study also showed a strong statistically significant correlation between NDVI and LST. A strong correlation was also noted between the VH and the NDVI and LST data while there was no strong correlation between the VV polarization and the other investigated parameters, which is consistent with the report by [PERSON] and [PERSON] [37], who used data from the European Remote-Sensing Satellites (ERS-1, VV polarization, C-band). There is no strong correlation between the investigated parameters in the Sediment Bog class because of its heterogenic structure where different land cover types can be found. This is also the case in the correlation between the VV and VH data in the Land, Wetland Mixture, and Sediment Bog class, where the correlations are not supported by the significance values. The results of [PERSON] et al. [17], who used a multi-year model to analyze the effects of seasonal hydrologic patterns in wetlands, indicated relatively little impact on the variation in biomass over the variation in backscatter values. Similar to this study, [PERSON] et al. [15] used multi-temporal and multi-sensor data in order to identify the backscattering characteristics of wetland vegetation. Backscatter values of the need marshes drastically increase at the beginning of the leaf-on season, from \(-10.8\) dB to \(-2.1\) dB at the VV polarization, which is similar to the findings of this study, where the values of the marsh class increase from \(-10.6\) dB to \(-0.7\) dB at the beginning of the leaf-on season at the VV polarization. In both of the studies, the reverse situation has been observed in the leaf-off season. [PERSON] et al. [19] reported that Radarsat data can provide more accurate data than Landsat Thematic Mapper data for wetland biomass estimation. However, the best results