Waterbody Detection of Korean Reservoirs from Sentinel-1 Images and the Analysis of its Relationship with Water Level: A Deep Learning Approach

Abstract

In this study, we propose a method for monitoring the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar (SAR) images and deep learning models. This approach includes verifying the correlation between water surface area and water level, using data from both the monitored water surface area and real-time water level gauges. Leveraging the Google Earth Engine (GEE) platform, we constructed datasets for seven reservoirs, each with capacities of 700,000 tonnes, 900,000 tonnes, and 1.5 million tonnes, covering the period from 2017 to 2021. The model training was conducted on 1,283 images from four reservoirs, applying shuffling and 5-fold cross-validation techniques. The models' detection results were evaluated based on mean Intersection over Union (mIoU). Utilizing the highest-performing model, we analyzed the correlation between surface area and water level changes from 2017 to 2021. By integrating the water surface area data calculated by the model with real-time reservoir water level information from RAWRIS (Rural Agricultural Water Resource Information System), we confirmed the correlation between changes in water surface area and water levels from 2017 to 2021. This study illustrates that monitoring of water surface areas by satellite can be effectively utilized for tracking status changes in agricultural reservoirs in South Korea.