## 3 Results and Discussion  
### Statistical Analysis  
The multitemporal radar backscattering coefficients were processed to estimate the intertidal topography through JNB optimization and logistic function. In Figure 3, the mean and standard deviation of the multitemporal radar backscattering coefficients are illustrated. The order of the mean radar backscattering coefficients from highest to lowest was land, tidal flat, and ocean. Ocean exhibited low \(\gamma^{0}\) values due to the specular scattering of the water surface. Interestingly, the order of the standard deviation of \(\gamma^{0}\) from highest to lowest was tidal flat, ocean, and land. Tidal flats were characterized by high standard deviation, which represents high temporal variation over different tide heights. In contrast, the ocean and land areas had lower standard deviations due to the smaller temporal variability of the corresponding backscattering coefficients. This is related to the fact that tidal flat topography is generally located between land and ocean bottom topography, and tidal flats are exposed as land at low tide and covered by water at high tide. Figure 3: Spatial distribution of (**a**) the mean, (**b**) standard deviations, and (**c**) GVF of the Sentinel-1 SAR backscattering coefficients. (**d**) Estimated intertidal topography in areas of GVF values higher than 0.2. The labelled symbols denote examples of ocean, salt pond, land, and tidal flats used for the best fitting logistic model in Figure 4.  
For the intertidal classification, JNB optimization was applied to ocean, land, and tidal flats, considering that tidal flats tend to show a rapid change of Sentinel-1 SAR backscattering coefficients along with a steady change in tide height. Figure 3c shows the GVF map from the JNB optimization of the backscattering temporal variability. The tidal flat showed higher GVF values compared to the lower GVF values for ocean and land. This supports the fact that tidal flats show high variability in the backscattering coefficient due to the periodic in and out flux of the tide water, thus they can be separated from land and ocean using our JNB optimization. For the intertidal topography, the logistic function was implemented to characterize the temporal evolution of the SAR backscattering coefficients on a pixel-by-pixel basis with the model parameters of local terrain height, steepness, and tide height. Figure 3d shows the result of our intertidal topography, estimated from the best fitting logistic model for areas with high GVF values. Figure 4 illustrates the temporal variability of the radar backscattering coefficients of ocean, land, salt pond, and tidal flats for more detailed results of our regression models. Tidal flats were divided into high, intermediate, and low tidal flats, in terms of elevation, to illustrate each of the temporal variabilities in Figure 4. Regardless of the tide height, ocean was characterized by low backscatter coefficients of around \(-15\) to \(-20\) dB, and the mean and standard deviation values were \(-17\)\(\pm\) 1.8 dB. Land showed relatively high gamma naught values from \(-10\) to \(-5\) dB, and the mean and standard deviation values were \(-7.9\)\(\pm\) 0.9 dB. Interestingly, salt ponds were clearly divided into two groups of high and low backscatter coefficients, with high standard deviation of \(\pm 4.4\) dB. However, unlike tidal flats, this was not related to the tide heights and could not be formulated by our logistic function. This implies that the backscatter characteristics of salt ponds is closer to ocean when seawater is trapped and closer to land when seawater is left to evaporate in the sun. Figure 4d-f shows that each pixel of tidal flats is characterized by its own distinct temporal variability of the radar backscattering coefficients with different model parameters. Tidal flats show the mean and standard deviation values of \(-9.5\)\(\pm\) 3.0 dB, \(-14.3\)\(\pm\) 3.9 dB, and \(-15.6\)\(\pm\) 4.0 dB for high, intermediate, and low elevation terrains, respectively.