Estimating accurate water levels for rivers and reservoirs by using SAR products: A multitemporal analysis

Remote sensing applied to flooding and natural disasters have been field of study for several research papers, generally aiming at detecting water masses, its depth measurements, and even determining its dynamics over time. These are important information for monitoring, warning about, and preventing the occurrence of hazardous situations. Through image products acquired from sensors, it is possible to automatically extract information, replacing fieldwork in areas of difficult access. A well-known acquisition technique is obtained by airborne SAR/InSAR (Interferometric Synthetic Aperture Radar) resulting in images of phase measurements (height), digital elevation models, signal strength (amplitude), and coherence for a given area. As new technologies are developed, it is possible to capture higher resolution images, which require more sophisticated image processing tools. In this paper, we present a novel approach for estimating water levels from SAR/InSAR products. The proposed method is based on a graph framework, known as Image Foresting Transform (IFT). Here, we adapted this framework for detecting margins of rivers and reservoirs, enabling us to accurately estimate their water levels over time. A rigorous analysis with real world data is conducted and discussed. Experimental results show that our approach is able to reliably predict the water levels using SAR/InSAR products and provide estimates which correspond precisely to fieldwork measurements.