## 1 Introduction
### Monitoring Rice and Flooding Using Satellite Images  
Estimation of rice damaged by flooding is a difficult task in the field. With advantages from satellite technology, high-resolution satellite images have been utilized for a timely and complete assessment of flood inundation and the affected rice area. Rice mapping has been undertaken since the late 1990s, using both radar and optical satellite images and following different approaches which include thresholding based on rice phenology analysis and machine learning (ML). Paddy rice growing stages consist of seeding, transplanting, tillering, flowering and harvesting, which determine the unique rice phenology. Based on the rice phenological analysis, the thresholding approach identifies values for specular satellite signal reflections or indicators in time-series to distinguish rice from other objects. Data from the Landsat TM and Japanese Earth Resources Satellite 1 (JERS-1) and the European Remote-Sensing Satellite (ERS-1) were analyzed to create rice maps over Indramayu in Indonesia [9] and the Mekong Delta region in Vietnam [10], respectively. In other studies, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Vegetation Index (VI), Normalized Difference Water Index (NDWI), Normalized Difference Snow Index (NDSI) were used to improve the quality of the rice maps [11]. Those indicators were calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) data and applied corresponding thresholds based on rice phenological analysis to create rice maps in References [12; 13; 14; 15]. Thresholding based on phenology analysis requires solid background knowledge of rice cultivation, satellite signals and indicator characteristics and adjustment for regional environmental conditions. Recently, the ML approach has been applied widely since its performance might surpass the traditional thresholding method [16]. Both supervised and unsupervised ML models were implemented for classification and clustering, respectively. The ML approach is easy and convenient for fast implementation but it requires strong data features for training classifiers. Optical spectral reflectance [17; 18; 19; 20], indicators such as NDVI, NDWI [18], and SAR backscattering coefficients [18] have been used as features for ML models. Regarding ML techniques, SVM [16; 18; 21], Random Forest (RF) [18; 22] and K-mean [19; 20] were largely used for rice mapping. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multicolumn{6}{c}{**Production (Thousands of Tons)**} \\ \cline{2-6}  & **2013** & **2014** & **2015** & **2016** & **2017** \\ \hline Red River Delta & 6655 & 6759 & 6729 & 6545 & 6083 \\ Northern Midlands and Mountainous & 3265 & 3341 & 3336 & 3405 & 3336 \\ North Central Coast & 6599 & 7034 & 6855 & 6842 & 6997 \\ Mekong River Delta & 25,021 & 25,245 & 25,583 & 23,831 & 23,633 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Rice production of the four largest rice areas in Vietnam (General Statistics Office (GSO)) [8]. In Vietnam, rice mapping from satellite images was mostly applied in the Mekong River Delta where the biggest rice cultivation area is. [PERSON] et al. used MODIS data over 7 years for the classification of aquaculture surfaces and rice-growing areas in three seasons in Soc Trang and Bac Lieu provinces. In the study, wavelet-based filter and thresholding of EVI, Land Surface Water Index (LSWI), and Difference Value between EVI and LSWI (DVEL) indicators were applied for rice classification [23]. Another study of rice mapping was carried out using the thresholding method on EVI derived from MODIS imagery [24]. In order to capture a variety of customized cultivation by location, [PERSON] et al. proposed a mean-shift segmentation algorithm to set different thresholds of EVI estimated from Landsat data for single-, double- and triple-cropped fields [25].