EXTRACTION OF FLOOD-AFFECTED AGRICULTURAL LANDS IN THE GOOGLE EARTH ENGINE; CASE STUDY OF KHUZESTAN, IRAN
Keywords: Google Earth Engine, Sentinel, Flood Mapping, Damage Assessment, Agriculture, Remote Sensing
Abstract. Floods are one of the most dangerous crises that cause a lot of damage in various fields, including economic and human lives. Therefore, preparation for prevention and damage assessment in order to manage this crisis is essential. In the meantime, providing methods with high speed and accuracy together can be helpful. In this study, using the Google Earth engine system and various sources of remote sensing data, the flooded areas of 2019 in Khuzestan province of Iran were extracted and the area of damaged agricultural lands was estimated. The general method was to first use the Sentinel 1 images, which are independent of the cloud, and the JRC global surface water mapping data to obtain flooded areas. After that, with the help of Sentinel 2 images and extracting various features from its bands and implementing an automated method, a map of damaged agricultural lands was also prepared. In order to approximate the affected population, WorldPop Global Project Population data has been used to take advantage of the maximum capacity of various remote sensing sources. The resulting flood map was evaluated by a ground truth map to prove the efficiency of the method. The overall accuracy of the map was 96.30 and its kappa coefficient was 80.03, which is quantitatively appropriate. The proposed method and the system used, due to their simplicity, can be generalized at high speed to other areas.