## 1 Introduction
### Monitoring Rice and Flooding Using Satellite Images
Also, time-series of An Advanced Synthetic Aperture Radar (ASAR) images were used to map early rice, late rice and double-crop using the thresholding technique [26]. [PERSON] et al. also used multi-temporal ASAR data to develop a knowledge-based decision-tree to determine rice crop based on its seasonal spectral signature [27]. Some rice mapping studies have been conducted in the Red River catchment. [PERSON] et al. used RADARSAT-2 dual-polarization data with the threshold-based approach and quad-polarization data with an SVM classifier for rice mapping in the Cau River Basin [16]. A combination of data from Landsat-8, Sentinel-1A and PALSAR-2 data was utilized to produce a rice map using a Random Forest (RF) classifier for greenhouse gas emission evaluation in the RRD [28]. Due to the high frequent cloud cover in this area, [PERSON] et al. proposed a compositing method for Landsat-8 images, which is used for rice mapping with XGBoost classifier later [29]. Other studies utilized time-series SAR imagery for rice classification using ML techniques including SVM [30; 31]. Simple Linear Iterative Clustering super-pixel algorithm [32], decision tree based on phenology analysis and the thresholding approach [33]. We found that the rice map of all 11 provinces in the RRD area has not been noticed due to the confusion in available administrative maps in previous studies. The growth process of rice is strongly associated with the irrigation schedule. At the beginning of the rice crop cycle, water is poured into the paddy field, which makes the vegetation indices (NDVI, EVI) decrease and water indices (LSWI, NDWI) increase and similarly changes SAR backscatter coefficients. The flooding causes the same spectrum change as irrigated rice fields. However, the occurrence time of these phenomena is different, so this time characteristic is used to distinguish (NRT) floods and seasonal irrigation. The flood detection methods are divided into three main categories: (1) thresholding techniques [34; 35; 36; 37; 38; 39; 40; 41]; (2) complex prediction models [42; 43; 44]; and (3) ML approach [45; 46; 47; 48]. In particular, thresholding techniques are mainly applied for NRT flood detection including adaptive thresholding [36; 37; 38; 39] and fixed thresholding [34; 35; 40; 41; 49] techniques. Fixed thresholding is often used based on expert knowledge from optical satellite-derived indices (NDVI, NDWI, LSWI, NDSI). Meanwhile, the adaptive method is mainly based on the bimodal distribution histogram of the grayscale image or maximum variance between classes (or minimum variance within classes) of a grayscale image to separate flooding from others. This method is commonly applied for SAR imagery. The adaptive thresholding technique is selected in this study since its good performance and accuracy were proved for NRT flood mapping in previous studies. Recently, social media data has also been used to detect rapid flooding [50; 51]. Adaptive thresholding techniques have some drawbacks in finding an optimal threshold [52]. Several studies have been proposed to improve the performance of the thresholding approach--using unsupervised fuzzy classification [52], split-based approaches [36; 39], a combination of different methodologies and multisource data [53]. The combination of NRT flood maps and rice maps might help with the assessment of the affected rice area. In previous studies, MODIS data was also used to evaluate flooding damage to rice in the Chao Phraya Delta region in Thailand [40] and Cambodia [41; 54], although MODIS has a low spatial resolution, is affected by environmental conditions and may cause high errors in flood mapping. In addition to the above studies, NRT flood mapping services were also available and provided by the National Aeronautics and Space Administration (NASA) [55] and Copernicus Emergency Management Service--Mapping [56].