## 5 Conclusions  
Storms and hurricanes from the South China Sea annually land on the mainland and cause serious damage to the RRD from May to October [3], especially during the summer-autumn rice crop. We have addressed and solved the two main remaining problems in previous studies. Firstly, the rice map of all 11 provinces in the RRD area has not been noticed due to the confusion in available administrative maps. Secondly, the use of the S1A images for flood detection and damaged rice assessment by flooding is very limited in this region although the RRD is annually hit by tropical storms, which causes serious damage to rice crops. Moreover, damaged rice statistics are only reported for heavy flooded areas at the end of months. Traditional statistical measurement methods are performed intermittently and inconsistently for rice damage estimation due to extreme environmental conditions, which limited timely responses in areas of high risk. In this study, we proposed a complete procedure for rice mapping, permanent water mapping, flooding mapping and NRT submerged rice assessment from S1A data. In particular, four datasets  
Figure 12: Visual inspection of heavily affected rice areas caused by prolonged inundation after the storm Son-Tinh in Lap Thach, Vinh Phuc (Figure 11, the A area) after the Son-Tinh happened. Column (**a**) Two Google Earth image scene on 10 March 2018—(dry season), Column (**b**) Two Google Earth Image scenes on 2 November 2018, Column (**c**) A combined scene of flood frequency map and Google Earth image on 2 November 2018 (flooding season). including VV, VH, VVVH, \(\frac{VV-VH}{VV+VH}\) were evaluated with SVM classifier, VVVH was selected to obtain the rice map due to its best performance. Similarly, VV and VH were evaluated and applied with the Otsu thresholding technique for permanent water and NRT flooding extraction, respectively. By stacking all the water body map within the year, the permanent water map was acquired. The permanent water map was then excluded from the temporal water map to obtain the NRT flooding map. After that, a stacked image of the rice map and the NRT flooding map was used to assess submerged areas. Further analysis was carried by the flood frequency map which stacking all the flooded rice maps in the study area. The evaluation of our methodology is based on field data and Vietnamese government statistics. The experiment was conducted for the assessment of damaged rice areas caused by the Son-Tinh storm which arrived at the RRD around 19 July 2018. The latest RRD rice map in the winter-spring season of 2018 was produced with 90.5% OA recorded with VVVH data. The permanent water map of the RRD in 2018 was obtained from all the S1A images within the year and also achieved 90.94% of OA using VV signals. The temporal flood map derived from VH data was created in the period from 22 July to 15 August 2018, to assess the affection of the Son-Tinh storm on the study area. In comparison with the available statistics of GSO for the Son-Tinh damages, our estimation was larger than that of the statistical data. It can be explained by the fact the affected rice area estimation from GSO was not conducted simultaneously, meanwhile, our estimation will yield results earlier than any fieldwork. Based on the experiment, we can quickly report the affected rice areas at the provincial level and the flooding risk map for the Son-Tinh storm. Some of the limitations encountered during the study can be mentioned, such as the low accuracy of the rice maps of mountainous areas in the Quang Ninh province, the low frequency of S1A over RRD, that is, every 12 days. For NRT flooding detection, the misclassification of small and complex shape canals covered by vegetation and the limitation of GSO validation data. In future, the rice classification model for areas with high terrain will be investigated further. The detection of small canals could be overcome by calculating a local optimal threshold at each small-district-level scene of large images in the RRD.