Spaceborne GNSS-Reflectometry for Surface Water Mapping in the Amazon Basin
Paulo T. Setti, Sajad Tabibi
Abstract
The Amazon basin, one of the world's largest and most vital ecosystems, presents a formidable challenge for accurately mapping its extensive surface water extent and dynamic seasonal and long-term variations. This challenge arises from the region's dense vegetation and persistent cloud coverage, limiting the applicability of conventional remote sensing technologies. Spaceborne GNSS-R offers a promising avenue to complement existing techniques, due to its distinctive sensitivity to surface water, vegetation, and cloud penetration, coupled with its high spatio-temporal resolution. In this study, we developed a track-wise method for mapping surface water using data from CYGNSS. Over a period of more than 6 years, from Apr. 2017 to May 2023, we produced monthly 3-km binary water/land maps covering the entire basin. Using a comprehensive set of metrics commonly employed in remote sensing studies, we conducted comparative analyses with datasets derived from optical, passive microwave, and radar missions. Our findings revealed that these products tend to underestimate the full extent of inundation across the Amazon basin. In contrast, CYGNSS, while exhibiting a slight tendency to overestimate surface water due to its heightened sensitivity to water and wet soils, provides valuable insights into inundation dynamics in the region. Furthermore, we compared our monthly inundated area estimates with water levels measured by four gauging stations across the basin. The results demonstrated a strong agreement between them and a maximum correlation coefficient of 0.74. Overall, the monthly water maps produced in this study hold significant promise for a wide range of hydrological investigations and practical applications.