Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China

Abstract

On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

A New Coherence Detection Method for Mapping Inland Water Bodies Using CYGNSS Data

“…Besides the investigations on the CYGNSS's ability to identify permanent inland water bodies, many studies explored the possibility of monitoring shorter-lived inland water bodies such as floods [18,19,47]. Obscured by cloud cover, optical images are often unusable for water detection during floods.…”

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

“…Thus, traditional optical remote sensing technology cannot penetrate the clouds and monitor the progress of the floods [5]. In contrast, microwave remote sensing is almost not affected by vegetation or clouds [6,7]. Consequently, it is considered to be an effective way to monitor flood disasters.…”

“…Moreover, Song [28] proposed a dual-branch neural network method for flood detection based on the CYGNSS DDM and SMAP vegetation data and verified the use of the CYGNSS data for flood monitoring. In 2021, Yang and Zhang [5,7] both studied flood inundation in Henan Province during extreme precipitation events in 2020 using CYGNSS SR and the threshold method. It should be noted that Henan Province is located in the middle of mainland China, which belongs to a temperate monsoon climate with less precipitation and less moisture.…”

Technology for Position Correction of Satellite Precipitation and Contributions to Error Reduction—A Case of the ‘720’ Rainstorm in Henan, China

“…They also discovered that, similar to Nie’s findings, the southeast airflow on the southwest side of the subtropical high pressure transported enough water vapor to Henan Province [ 14 ]. Zhang used the observations from CYSNSS to study the distribution and impact of floods in Henan [ 15 ]. Shi used observations from BeiDou/GNSS to analyze the relationship between extreme rainfall processes and the precipitation water vapor [ 16 ].…”