# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 4 Results
### Analysis of Temporal and Spectral Characteristics of Paddy Rice
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Multiclass Classification**} & \multicolumn{4}{c}{**Binary Classification**} \\ \hline  & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**SVM**} \\ \hline  & **OA (\%)** & \multicolumn{2}{c}{**Kappa**} & \multicolumn{2}{c}{**Kappa**} & \multicolumn{2}{c}{**A(\%)**} & \multicolumn{2}{c}{**Kappa**} \\  & **Coefficient** & **OA (\%)** & \multicolumn{2}{c}{**Coefficient**} & **OA (\%)** & \multicolumn{2}{c}{**Kappa**} & \multicolumn{2}{c}{**Coefficient**} & **OA (\%)** & \multicolumn{2}{c}{**Kappa**} \\  & **Oa (\%)** & \multicolumn{2}{c}{**Coefficient**} & **OA (\%)** & \multicolumn{2}{c}{**Coefficient**} & **OA (\%)** & \multicolumn{2}{c}{**Coefficient**} \\ \hline
**Scheme 1** & & & & & & & & & \\
12 February & 73.00 & 0.65 & 74.67 & 0.68 & 87.33 & 0.72 & 87.00 & 0.71 \\
13 March & 77.33 & 0.71 & 77.00 & 0.71 & 91.33 & 0.81 & 90.67 & 0.79 \\
18 March & 79.33 & 0.74 & 80.00 & 0.74 & 91.33 & 0.81 & 91.33 & 0.81 \\
29 March & 77.33 & 0.71 & 77.00 & 0.70 & 88.00 & 0.73 & 85.67 & 0.69 \\
31 March & 81.67 & 0.77 & 81.67 & 0.77 & 91.33 & 0.81 & 92.00 & 0.82 \\
30 April & 81.67 & 0.77 & 82.33 & 0.78 & 95.67 & 0.90 & 95.00 & 0.89 \\
1 June & 82.67 & 0.78 & 83.67 & 0.79 & 96.67 & 0.92 & 97.33 & 0.94 \\
3 June & 84.33 & 0.80 & 84.00 & 0.80 & **98.67** & **0.97** & 98.00 & 0.95 \\
21 September & 81.67 & 0.77 & 81.33 & 0.76 & 95.00 & 0.88 & 95.67 & 0.90 \\
9 October & 80.33 & 0.75 & 81.33 & 0.76 & 92.67 & 0.83 & 95.00 & 0.88 \\
12 December & 76.33 & 0.70 & 76.67 & 0.70 & 90.00 & 0.78 & 90.00 & 0.78 \\
**Scheme 2** & 86.33 & 0.83 & **88.67** & **0.86** & 98.00 & 0.95 & **99.33** & **0.98** \\
**Scheme 3** & 71.67 & 0.64 & 72.67 & 0.65 & 89.33 & 0.76 & 90.67 & 0.78 \\
**Scheme 4** & 84.67 & 0.80 & 85.67 & 0.82 & 94.00 & 0.86 & 94.67 & 0.88 \\
**Scheme 5** & **88.00** & **0.85** & 87.00 & 0.84 & 97.67 & 0.95 & 97.67 & 0.95 \\
**Scheme 6** & **88.00** & **0.85** & 87.67 & 0.84 & 98.00 & 0.95 & 98.67 & 0.97 \\ \hline \end{tabular}
\end{table} 
Table 5: Summary of classification accuracies (%) at site B. The highest accuracy identified by model and classification type (i.e., multiclass vs. binary) is shown in bold. and spectral patterns of the paddy rice (refer to Tables 1 and 2). Fluctuant patterns are more visible at site B, as it suffers from more severe atmospheric affects (e.g., clouds, fogs) than site A. Built-up class (refer to Table 3) generally results in a flat temporal pattern (Figure 4), but there are outliers in the built up class on 29 March, 1 June, and 3 June due to the atmospheric effect (Figure 5). SAR backscattering coefficients were not relatively affected by atmospheric conditions and the order of years with the flat temporal pattern in built-up class (Figures 4 and 5). There are no noticeable temporal characteristics in the visible bands (bands 1-3). The reflectance is low and shows typical characteristics, including the highest reflectance in built up areas, and the lowest reflectance in water. The reflectance of paddy rice and vegetation (e.g., field, forest, grass) increase in the NIR band during their growing season. In paddy rice, the reflectance of the NIR band shows a peak in August when photosynthesis is most active, and decreases in September to October when the color of paddy rice turns yellow (Figures 4 and 5). SWIR2 is sensitive to leaf water content, and the more leaf water content there is, the lower the reflectance of SWIR2 is. Paddy rice shows the lowest reflectance of SWIR2 during the growing season, including the transplanting season (May) when the paddy is flooded (Figures 4 and 5). NDVI increases during the growing season in vegetation ( paddy rice, grass, field, and forest), and paddy rice especially grows after transplanting (May). Contrary to NDVI, NDWI sensitive to water deficit during the growing season in vegetation. SAR backscattering shows the lowest roughness in water and the highest (brightest) in built up.