Detecting Floods Caused by Tropical Cyclone Using CYGNSS Data

Abstract

As a tropical cyclone reaches inland, it causes severe flash floods. Real-time flood remote sensing can reduce the resultant damages of a flash flood due to its heavy precipitation. Considering the high temporal resolution and large constellation of the Cyclone Global Navigation Satellite System (CYGNSS), it has the potential to detect and monitor flash floods. In this study, based on CYGNSS data and the Random Under-Sampling Boosted (RUSBoost) machine learning algorithm, a flood detection method is proposed. The proposed technique is applied to the areas affected by Hurricane Harvey and Hurricane Irma, for which test results indicate that the flooded points are detected with 89.00% and 85.00% accuracies, respectively, and non-flooded land points are classified with accuracies equal to 97.20% and 71.00%, respectively.