Evaluation of CYGNSS Observations for Flood Detection and Mapping during Sistan and Baluchestan Torrential Rain in 2020

Abstract

Flood detection and produced maps play essential roles in policymaking, planning, and implementing flood management options. Remote sensing is commonly accepted as a maximum cost-effective technology to obtain detailed information over large areas of lands and oceans. We used remote sensing observations from Global Navigation Satellite System-Reflectometry (GNSS-R) to study the potential of this technique for the retrieval of flood maps over the regions affected by the recent flood in the southeastern part of Iran. The evaluation was made using spaceborne GNSS-R measurements over the Sistan and Baluchestan provinces during torrential rain in January 2020. This area has been at a high risk of flood in recent years and needs to be continuously monitored by means of timely observations. The main dataset was acquired from the level-1 data product of the Cyclone Global Navigation Satellite System (CYGNSS) spaceborne mission. The mission consisted of a constellation of eight microsatellites with GNSS-R sensors onboard to receive forward-scattered GNSS signals from the ocean and land. We first focused on data preparation and eliminating the outliers. Afterward, the reflectivity of the surface was calculated using the bistatic radar equations formula. The flooded areas were then detected based on the analysis of the derived reflectivity. Images from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used for evaluation of the results. The analysis estimated the inundated area of approximately 19,644 km2 (including Jaz-Murian depression) to be affected by the flood in the south and middle parts of the Sistan and Baluchestan province. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we showed the capability of detecting floods in the study area. The sensitivity of the spaceborne GNSS-R observations, together with the relatively short revisit time, highlight the potential of this technique to be used in flood detection. Future GNSS-R missions capable of collecting the reflected signals from all available multi-GNSS constellations would offer even more detailed information from the flood-affected areas.

Improving Flood Detection and Monitoring through Remote Sensing

“…A potentially innovative data source for flood monitoring is investigated in [9]. GNSS reflectometry is a technique that uses microwave signals emitted by global positioning system constellations and collected by suitable receivers to gain information about (bistatic) reflectivity of the Earth's surface.…”

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

“…This method has also been widely used in previous studies using CYGNSS data to retrieve the submerged state of the surface. However, owing to the different parameters, such as the topography, roughness, and vegetation of the studied area, this threshold is not certain [40][41][42]. For instance, the threshold used by [40] was 12 dB for the medium-vegetation density and typical roughness.…”

“…Studies have shown that the results of the flooding area distribution obtained by CYGNSS are in good agreement with rainfall data, SMAP, and SMOS brightness temperature data. Rajabi et al [42] studied the feasibility of using CYGNSS data to detect and map flood distributions during heavy rains in Sistan and Baluchistan in 2020. The results show that the CYGNSS signal-to-noise ratio observation can be used to detect and map the distribution of a flood disaster, and the results are in good agreement with the flood disaster distribution obtained from MODIS optical images.…”

Calibration and Validation of CYGNSS Reflectivity through Wetlands’ and Deserts’ Dielectric Permittivity

“…To compute CYGNSS reflectivity values, we only consider the strongest scattering power provided by natural land surfaces, which is received from the coherent part of the reflected signal [19,43]. In this sense, land surface reflectivity can be sensed from GNSS-R data through the bistatic radar equation for the coherent component of LHCP GNSS bistatic microwave signals [17,36,[44][45][46], which takes the following expression [20] when dealing with GNSS-R data:…”

“…The subscript lr stands for a scattering mechanism when the incident RHCP signal is scattered by the surface and inverts the polarization to LHCP at the receiver position. Γ lr is the surface reflectivity from which the SMC might be estimated, after correction of the noise floor component (N) in the DDM [31,46]. R t and R r are the transmitter and receiver range to the specular point, respectively.…”