## 2 Materials and Methods
### Satellite Data and Processing
#### 2.3.3 Land Cover Classification  
A supervised land cover classification with machine learning algorithm was employed in GEE. Several of those algorithms have been embedded in GEE. These classifiers are Classification and Regression Tree (CART), Random Forest (RF), NaiveBayes, and Support Vector Machine (SVM) [59]. Wetland mapping, as in mangrove swamps, is one of the most challenging areas for remote sensing. RF supervised classification is reported to have the highest accuracy among widely used machine learning algorithms, e.g., K-Nearest Neighbour (KNN), SVM, Maximum Likelihood (ML), and CART [76; 87]. RF comprises a collection of tree-structured classifiers to make prediction [88]. RF is more robust to noise and size reduction of the training set than CART [89], easier to implement than SVM [90], and is particularly suitable to handle high dimensional remote sensing data [91]. The land cover classification was conducted using the following workflow: (1) training data collection, (2) initiating classifier and adjusting parameters, (3) train the classifier with training dataset, (4) classify the image based on the trained dataset, and (5) accuracy checking. In the first step, the training dataset is provided from the very high-resolution orthomosaic created on the UAV data processing. We created three classifications (water, mangroves, and mudflat area) by plotting region of interests (ROIs) which represent these classifications. As the intertidal mudflat varies over time by tidal variation, proper consideration should be made by not only referencing the mudflat's ROI based on the orthomosaic but also considering the median composite in November 2019. The ROIs polygons were made in QGIS as a shapefile and uploaded to GEE as assets. Next, sample points were created based on supervised ROIs. Accuracy of RF classification is sensitive to the sample size and spatial distribution [92]. Within each stratum, which is based on supervised ROIs, the stratums are randomly sampled [93]. Stratified random sampling \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mission** & **Bands/Indices** & **Metric** \\ \hline Sentinel-1 & VV, VH, VV/VH & mean \\ \hline Sentinel-2 & B2-B8, B8A, B11-12, NDVI, NDMI, EVI, SAVI & median \\ \hline Landsat-7 & B1-5, B7, NDVI, NDMI, EVI, SAVI & median \\ \hline Landsat-8 & B2-8, NDVI, NDMI, EVI, SAVI & median \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets used as input for each satellite constellation product. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indices** & **Formulas** & **S-2** & **L-7** & **L-8** \\ \hline NDVI & (NIR \(-\) R) \(\div\) (NIR \(+\) R) & (B8 \(-\) B4) \(\div\) (B8 \(+\) B4) & (B4 \(-\) B3) \(\div\) (B4 \(+\) B3) & (B5 \(-\) B4) \(\div\) (B5 \(+\) B4) \\ \hline NDMI & (NIR \(-\) SWIR) \(\div\) (NIR \(+\) SWIR) & (B8 \(-\) B11) \(\div\) (B8 \(+\) B11) & (B4 \(-\) B5) \(\div\) (B4 \(+\) B5) & (B5 \(-\) B6) \(\div\) (B5 \(+\) B6) \\ \hline EVI & \(2.5\times\) ((NIR \(-\) R) \(\div\) (NIR \(+\) 6 \(\times\) R & \(2.5\times\) ((B8 \(-\) B4) \(\div\) (B8 & \(2.5\times\) ((B4 \(-\) B3) \(\div\) (B4 & \(2.5\times\) ((B5 \(-\) B4) \(\div\) (B5 \\ \(-\) \(-\) \(7.5\times\) B \(+\) 1) & \(+\) 6 \(\times\) B4 \(-\) 7.5 \(\times\) B2 \(+\) 1) & \(+\) 6 \(\times\) B3 \(-\) 7.5 \(\times\) B1 \(+\) 1) & \(+\) 6 \(\times\) B4 \(-\) 7.5 \(\times\) B2 \(+\) 1) \\ \hline SAVI & (NIR \(-\) R) \(\times\) 1.5 \(\div\) (NIR \(+\) R \(+\) 0.5) & (B8 \(-\) B4) \(\times\) 1.5 \(\div\) (B8 function in GEE was used to sample the training data. [PERSON]'s formula (1) was employed to determine sample size by assuming an unknown proportion for each class [94]. \[n_{0}=Z^{2}\frac{pq}{e^{2}} \tag{1}\]  
Here, \(n_{0}\) is the sample size per class, \(p\) is the proportion of the population which has the class in question, \(q=1-p\), \(Z\) is the z-value for the given confidence, \(e\) is the margin of error. The samples were subdivided into a 70% training set and 30% of validation [88].