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

Abstract

Floods are severe natural disasters that are harmful and frequently occur across the world. From May to July 2022, the strongest, broadest, and longest rainfall event in recent years occurred in Guangdong Province, China. The flooding caused by continuous precipitation and a typhoon resulted in severe losses to local people and property. During flood events, there is an urgent need for timely and detailed flood inundation mapping for areas that have been severely affected. However, current satellite missions cannot provide sufficient information at a high enough spatio-temporal resolution for flooding applications. In contrast, spaceborne Global Navigation Satellite System reflectometry technology can be used to observe the Earth’s surface at a high spatio-temporal resolution without being affected by clouds or surface vegetation, providing a feasible scheme for flood disaster research. In this study, Cyclone Global Navigation Satellite System (CYGNSS) L1 science data were processed to obtain the change in the delay-Doppler map and surface reflectivity (SR) during the flood event. Then, a flood inundation map of the extreme precipitation was drawn using the threshold method based on the CYGNSS SR. Additionally, the flooded areas that were calculated based on the soil moisture from the Soil Moisture Active Passive (SMAP) data were used as a reference. Furthermore, the daily Dry Wet Abrupt Alternation Index (DWAAI) was used to identify the occurrence of the flood events. The results showed good agreement between the flood inundation that was derived from the CYGNSS SR and SMAP soil moisture. Moreover, compared with the SMAP results, the CYGNSS SR can provide the daily flood inundation with higher accuracy due to its high spatio-temporal resolution. Furthermore, the DWAAI can identify the transformation from droughts to floods in a relatively short period. Consequently, the distributions of and variations in flood inundation under extreme weather conditions can be identified on a daily scale with good accuracy using the CYGNSS data.

Remote sensing and its applications using GNSS reflected signals: advances and prospects

“…Daily, 3 km flood monitoring results were obtained by interpolating CYGNSS SR through a newly proposed spatial interpolation method based on previous observations (POBI) (Chew, 2021). Wei et al (2023) combined Sentinel-2 data to classify the surface into high soil moisture areas and low vegetation cover areas and proposed a dual-threshold method based on the classification results, which was successfully applied to the detection of flooding in Guangdong Province, China in the summer of 2022. Downs et al (2023) conducted an in-depth study of the performance of spaceborne GNSS-R in detecting inland water bodies in the case of South Sudan, and quantitatively compared the water distributions obtained by CYGNSS with those obtained by MODIS, the Visible Infrared Imaging Radiometer (VIIRS), and the Sentinel-1 SAR in the C-band, respectively.…”

Insights into Very Early Afterslip Associated with the 2021 M 8.2 Chignik, Alaska Earthquake Using Subdaily GNSS Solutions

“…Based on the PPP-AR software, Precision Point Positioning (PPP) with Ambiguity Resolution (AR) is conducted for raw GNSS observations to obtain kinematic solutions. The tropospheric delay is corrected by Vienna Mapping Functions 1 (VMF1), and the high-order ionospheric error is corrected using GIM products [33][34][35]. Meanwhile, the effects of solid tide, ocean tide, and polar tide are also corrected.…”

Extraction of Information on the Flooding Extent of Agricultural Land in Henan Province Based on Multi-Source Remote Sensing Images and Google Earth Engine

“…In addition, previous studies have focused more on the extraction of flood-inundated areas [46,47]. Besides of that, flood-affected areas are also important information for cultivated land as a basis for an in-depth analysis of the growth status of crops that are affected by different levels of flooding.…”