## 3 Results
### Accuracy of Rice Maps  
Based on the unsupervised classification, rice areas throughout 31 ROIs across Penin-sular Malaysia were identified. Maps of the rice extent in Peninsular Malaysia from 2019-2020 are presented in Figure 5. The complete map is available at [[https://rudiyanto.users.earthegine.app/view/malaysiarice](https://rudiyanto.users.earthegine.app/view/malaysiarice)]([https://rudiyanto.users.earthegine.app/view/malaysiarice](https://rudiyanto.users.earthegine.app/view/malaysiarice)), accessed on 9 April 2022. The maps produced in this study successfully distinguished rice fields from other land covers. Figure 5: _Cont._  
Figure 5: _Cont_. We validated our map using random sampling in the ROI of rice fields to evaluate the accuracy at a pixel level. Our map had an excellent overall accuracy of 95.95% and a kappa coefficient of 0.92, as shown in Table 2. We also evaluated the accuracy of the NSEA-Rice10 product on the same random samples. NESEA-Rice10 had a lower overall accuracy of 90.27% and a kappa coefficient of 0.80 (Table 3). Omission errors occurred when rice fields were classified as non-rice fields. Commission errors occurred when non-rice fields were classified as rice fields [22]. This study's omission and commission errors of rice fields were 1.1% and 7.0%, respectively, while NESEA-Rice10 errors were much larger--19.9% and 1.1%, respectively. We note that outside the ROI, the NESEA-Rice10 had many speckles where non-rice areas were classified as rice. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} & \multicolumn{3}{c}{**Producer Accuracy**} \\ \cline{3-8}  & \multicolumn{2}{c}{**Non-Rice** (**Pixels**)} & \multicolumn{1}{c}{**Rice** (**Pixels**)} & \multicolumn{1}{c}{**Total** (**Pixels**)} & \multicolumn{1}{c}{**Percent**} & \multicolumn{1}{c}{**Omission Error (\%)**} \\ \hline \multirow{2}{*}{Reference} & Non-rice (Pixels) & \multirow{2}{*}{366} & \multirow{2}{*}{26} & \multirow{2}{*}{392} & \multirow{2}{*}{93.4} & \multirow{2}{*}{6.6} \\  & (Pixels) & & & & & \\ \hline \multirow{2}{*}{Class} & Rice (Pixels) & \multirow{2}{*}{4} & \multirow{2}{*}{344} & \multirow{2}{*}{348} & \multirow{2}{*}{98.9} & \multirow{2}{*}{1.1} \\  & Total (Pixels) & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Total \\ (Pixels) \\ \end{tabular} } & Total (Pixels) & \multirow{2}{*}{370} & \multirow{2}{*}{370} & \multirow{2}{*}{740} & \multirow{2}{*}{740} \\  & User accuracy & & & & \\ \hline \multirow{2}{*}{Percent correct} & \multirow{2}{*}{98.9} & \multirow{2}{*}{93.0} & \multirow{2}{*}{95.95} \\ \cline{2-2} \cline{5-5}  & & & & & \\ \hline \multirow{2}{*}{Compression error (\%)} & \multirow{2}{*}{1.1} & \multirow{2}{*}{7.0} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \hline Kappa & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for the rice classification in Peninsular Malaysia by this study. Figure 5: Map of rice field extent in (**a**) Selangor State, (**b**) Kedah and Perlis States, (**c**) Penang State, and (**d**) Besut district, Terengganu state. Figure 6: _Cont._  
Table 4 shows an example comparison of the cropping calendar from the published granary area schedule and this study. The cropping calendar derived from this study agreed well with the granary area schedule. Peninsular Malaysia has two cropping seasons, the main season and off season, with no cash crops in-between seasons. As an example, Selangor and Kedah states had two cropping seasons in one year. In Selangor, there were four cultivated areas with different cropping schedules. The main season in 2020 in Selangor started in July, August, September, and October for Group 2, 1, 4 and 3, respectively. Concurrently, the off season started in January, February, March and April, respectively. In Kedah, two cultivated areas had different cropping schedules; the main season of both rice groups started in October, while the off season started in April and May for Groups 2 and 1 (Table 4).