## 4 Conclusions  
Tidal flats are very dynamic natural systems that experience short-term and long-term morphological and hydrological changes. Their highly dynamic behavior requires frequent monitoring. Ground-based measurements and UAV-based methods provide very accurate measurements, but they lack the ability to provide repeated observations. Satellite-based methods have been useful for accomplishing repeated observations for a large spatial coverage. This study presents our innovative statistical approach for intertidal classification and topography on a pixel-by-pixel basis using multitemporal SAR backscattering coefficients.  
We demonstrated the use of JNB optimization to separate tidal flats from ocean and land using multitemporal Sentinel-1 backscattering coefficients. This method uses an iterative approach to find a distinct statistical behavior of the tidal flats along with tide heights. Tidal flats show a high variability of the SAR backscattering coefficients due to the periodic in and out flux of tidal water. In this study, areas with GVF values higher than 0.2 were classified as tidal flats, which was supported by the DEM evaluation against the airborne Lidar observations. We also successfully generated Sentinel-1 DEMs with 10 m spatial resolution and a height accuracy of 0.20 m in MAE and 0.28 in RMSE from the logistic function. The Sentinel-1 DEM outperformed the waterline-based optical image from the Landsat DEM. Our logistic function relies on a pixel-based algorithm to model the temporal variation of the multitemporal SAR backscattering coefficients, where three unknown parameters (i.e., \(k1,k2,h_{t}\)) were initialized from the JNB optimization. The height accuracy of our logistic model can be improved when considering the effect of tidal flat exposure time, sedimentary facies, and textures into the temporal variability of the SAR backscattering coefficients.  
Figure 7: The topographic profiles of the airborne Lidar, pixel-based Sentinel-1, and waterline-based Landsat DEMs along the solid white line in Figure 6.  
This study can also be used to generate important data as a means of managing the marine environment and maritime activities more coherently at the marine spatial planning (MSP) [31]. These results are essential to continue making potential improvements in quickly monitoring extensive tidal flats and coastal changes through continuous and stable SAR data collection on a local and regional scale. Future development could focus on more efficient models to estimate and forecast tidal flat topography and exposure with environmental factors, which could also lead to a significant improvement in investigating the spatiotemporal evolution of tidal flat productivity and habitat.  
K.K.: Conceptualization; Investigation; Writing-original draft. H.J.: Conceptualization; Software; Validation; Formal Analysis; Visualization; Funding Acquisition. J.-K.C.: Conceptualization; Investigation; Writing--review and editing. J.-H.R.: Conceptualization; Supervision; Funding Acquisition. Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRFK) and funded by the Korean Government (2021R1A2C100578011). This research was also funded by \"Development of technology for constructing biological and environmental spatial information system of tidal flats through machine learning of remotely sensed visual data\" from the Korea Institute of Ocean Science and Technology (PE99915).  
The authors declare no conflict of interest.