Enhancing Flood Detection in Surabaya: A Comparative Study of VV and VH Polarizations with Sentinel-1 Data

Abstract

Flood mapping is critical to strengthen urban resilience, particularly in Surabaya, where flooding is a major and recurring threat. Sentinel-1 satellite data offers significant advantages for flood model calibration due to its high-resolution imagery and frequent revisits. This study utilizes Google Earth Engine to process and analyse Sentinel-1 data for mapping flood extents using two different polarizations: VV and VH. The research compares the capabilities of these polarizations in detecting flood areas. The results show that VV polarization consistently identifies a larger flood area compared to VH polarization under similar processing conditions. However, the Kappa coefficient was used to assess classification accuracy, with VV achieving a Kappa of 0.8 and VH reaching a higher Kappa of 0.92, reflecting better classification performance. These findings suggest that while VV provides a broader flood detection, VH offers more reliable flood mapping, highlighting the trade-offs between sensitivity and accuracy in flood monitoring using Sentinel-1 satellite.