# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 3 Methodology  
A total of 10 input variables--Landsat bands 1-5 and 7, NDVI, NDWI, SAR backscattering coefficients, and DEM--were used for paddy rice classification. Figure 3 shows a flowchart of this study. Classification models were constructed using training data through two machine learning approaches, RF and SVM. RF is based on classification and regression trees (CART), which is a non-parametric model and constructs rule sets from data [42; 43]. RF has two randomization processes, randomly selecting a subset of training samples (e.g., ~70%) for each tree and selecting a subset of variables at each node of a tree (e.g., squared root of the number of total variables). RF consists of many decision trees (the number  
Figure 3: Process flow chart of this study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**(a)** & **Site A** & & & & \\ \hline \multirow{2}{*}{**Original Land Cover**} & **Aggregated** & & & **\# of Training** & **\# of Test** \\  & **Original Land Cover** & & & **Sample** & **Sample** \\ \hline Urban, municipal, commercial, industrial urban landscape, residential, semi-agricultural & & Built up & 4.1 & 40 & 12 \\ \hline \multirow{4}{*}{Citrus and subtropical, deciduous fruits and nuts, field crops, grain and hay crops, idle, Pasture, truck, nursery and berry crops, vineyards} & & & & \\ \cline{2-5}  & Riparian vegetation, native vegetation & Vegetation & 23.3 & 177 & 66 \\  & Barren and wassteland & & Bare soil & 1.9 & 13 & 6 \\  & Water & Water & 1.2 & 14 & 5 \\ \hline
**(b)** & **Site B** & & & & \\ \hline \multirow{2}{*}{**Original Land Cover**} & **Aggregated** & & & **\# of training** & **\# of test** \\  & **Land Cover** & & & **sample** & **sample** \\ \hline \multirow{2}{*}{Residential, industrial urban landscape, commercial, recreational facilities, trafficked areas, municipal} & Built up & 6 & 58 & 30 \\ \hline \multirow{2}{*}{Crop field, protected cultivation, orchard, other field} & & & & & \\ \cline{2-5}  & **Crop** & & & & \\ \hline \multirow{2}{*}{D deciduous tree, coniferous tree, mixed forest} & & & & & \\ \cline{2-5}  & **Crass** & & & & \\ \cline{2-5}  & **Crass** & & & & \\ \cline{2-5}  & **Hland wetland, coastal wet land** & & & & \\ \cline{2-5}  & **Barren, wassteland** & & & & \\ \cline{2-5}  & **Hland water, coastal marine water** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The original land cover, aggregated land cover, the number of samples (1100), and the percentage of area for each class. of trees is 1000 in this study), and each tree has a decision. Finally, the class of an unknown pixel is determined through the majority voting of the results of trees. RF has been successfully conducted for various classification tasks in the literature [38; 44; 45; 46; 47; 48; 49; 50; 51]. RF also provides information about relative variable importance. Relative variable importance is based on an increased mean squared error (MSE) using out-of-bag data (i.e., unused training samples for each tree), which is calculated when randomly permuting an independent variable [52; 53]. In this study, RF was implemented by R software ([[https://www.r-project.org/](https://www.r-project.org/)]([https://www.r-project.org/](https://www.r-project.org/))) through Random forest package with default settings (i.e., the number of randomly sampled variables as candidates at each split is the square root of the number of input variables; the minimum size of the terminal node is 1), except for the number of trees. SVM is a supervised non-parametric approach and has been used in many studies in the remote sensing field [54; 55; 56; 57; 58; 59; 60]. The use of SVM has significantly increased because it works well in cases of limited training data sets and produces higher accuracy than other traditional approaches, such as Maximum Likelihood (ML) [61; 62]. SVM conducts classification by determining hyperplanes that optimally separates classes [63]. One hyperplane only separates two classes so multiple hyperplanes are used to classify multiple classes.