Using CYGNSS Data to Monitor China’s Flood Inundation during Typhoon and Extreme Precipitation Events in 2017

Wan, Wei; Liu, B.; Zeng, Ziyue; Chen, Xi; Wu, Guojun; Xu, Li‐Wen; Chen, Xiuwan; Hong, Yang

doi:10.3390/rs11070854

Cited by 56 publications

(50 citation statements)

References 21 publications

(30 reference statements)

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“…Earth Observation (EO) datasets (e.g., space borne, aerial images, and satellite images) together with geographic information systems (GIS) can be used to determine the extent of flood areas and for the production of flood hazard and risk maps [43]. Combined with the use of remote sensing (RS) and GIS, EO datasets provide a favorable environment for relevant information processing in order to obtain the spatial extent of flood hazard areas and flood mapping [44][45][46][47]. Even if the benefits are high, this technique offers valuable information only for specific flood events, and this aspect is perceived as a disadvantage because future flood details and impact cannot be investigated [42,45].…”

Section: State-of-the-artmentioning

confidence: 99%

Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach

Mihu-Pintilie

Cimpianu

Stoleriu

et al. 2019

Water

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The ability to extract streamflow hydraulic settings using geoinformatic techniques, especially in high populated territories like urban and peri-urban areas, is an important aspect of any disaster management plan and flood mitigation effort. 1D and 2D hydraulic models, generated based on DEMs with high accuracy (e.g., Light Detection and Ranging (LiDAR)) and processed in geographic information systems (GIS) modeling software (e.g., HEC-RAS), can improve urban flood hazard maps. In this study, we present a small-scale conceptual approach using HEC-RAS multi-scenario methodology based on remote sensing (RS), LiDAR data, and 2D hydraulic modeling for the urban and peri-urban area of Bacău City (Bistriţa River, NE Romania). In order to test the flood mitigation capacity of Bacău 1 reservoir (rB1) and Bacău 2 reservoir (rB2), four 2D streamflow hydraulic scenarios (s1–s4) based on average discharge and calculated discharge (s1–s4) data for rB1 spillway gate (Sw1) and for its hydro-power plant (H-pp) were computed. Compared with the large-scale flood hazard data provided by regional authorities, the 2D HEC-RAS multi-scenario provided a more realistic perspective about the possible flood threats in the study area and has shown to be a valuable asset in the improvement process of the official flood hazard maps.

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Section: State-of-the-artmentioning

confidence: 99%

Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach

Mihu-Pintilie

Cimpianu

Stoleriu

et al. 2019

Water

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“…It was then tested over continental surfaces, where it revealed its strong potential for the monitoring of various surface parameters such as the water content of the soil or vegetation biomass [18][19][20][21][22][23][24][25]. Following various recent in situ and airborne campaigns collecting GNSS-R measurements [22,26,27], several studies have illustrated their potential for the development of GNSS-R based applications [28][29][30][31][32][33][34][35][36][37][38]. [28,31,33] have shown the strong potential of GNSS-R TechDemoSat-1 receiver and reflections collected by the SMAP (Soil Moisture Active Passive) receiver for the mapping of surface conditions, such as soil moisture or vegetation properties.…”

Section: Introductionmentioning

confidence: 99%

Desert Roughness Retrieval Using CYGNSS GNSS-R Data

et al. 2020

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The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (Γ). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS Γ measurements has been developed, at the relatively fine resolution of 0.03° × 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coefficient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness.

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“…For example, TDS-1 SGR-ReSI measurements have been used to characterize ocean winds [6,7], sea surface height [8], soil moisture and vegetation [9,10], wetland inundation [11,12], sea ice detection and concentration [13][14][15][16], sea ice altimetry [17], and sea ice type classification [18]. CYGNSS measurements have been used to observe ocean wind speeds [19][20][21][22], soil moisture [23,24], wetlands inundation characterization and dynamics [25][26][27], and hurricane/tsunami-driven flooding [28,29].…”

Section: Introductionmentioning

confidence: 99%

The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities

et al. 2019

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The Soil Moisture Active Passive (SMAP) mission became one of the newest spaceborne Global Navigation Satellite System–Reflectometry (GNSS-R) missions collecting Global Positioning System (GPS) bistatic radar measurements when the band-pass center frequency of its radar receiver was switched to the GPS L2C band. SMAP-Reflectometry (SMAP-R) brings a set of unique capabilities, such as polarimetry and improved spatial resolution, that allow for the exploration of scientific applications that other GNSS-R missions cannot address. In order to leverage SMAP-R for scientific applications, a calibration must be performed to account for the characteristics of the SMAP radar receiver and each GPS transmitter. In this study, we analyze the unique characteristics of SMAP-R, as compared to other GNSS-R missions, and present a calibration method for the SMAP-R signals that enables the standardized use of these signals by the scientific community. There are two key calibration parameters that need to be corrected: The first is the GPS transmitted power and GPS antenna gain at the incidence angle of the measured reflections and the second is the convolution of the SMAP high gain antenna pattern and the glistening zone (Earth surface area from where GPS signals scatter). To account for the GPS transmitter variability, GPS instrument properties—transmitted power and antenna gain—are collocated with information collected from the CYclone Global Navigation Satellite System (CYGNSS) at SMAP’s range of incidence angles (37.3° to 42.7°). To account for the convolutional effect of the SMAP antenna gain, both the scattering area of the reflected GPS signal and the SMAP antenna footprint are mapped on the surface. We account for the size of the scattering area corresponding to each delay and Doppler bin of the SMAP-R measurements based off the SMAP antenna pattern, and normalize according to the size of a measurement representative to one obtained with an omnidirectional antenna. We have validated these calibration methods through an analysis of the coherency of the reflected signal over an extensive area of old sea ice having constant surface characteristics over a period of 3 months. By selecting a vicarious scattering surface with high coherency, we eliminated scene variability and complexity in order to avoid scene dependent aliases in the calibration. The calibration method reduced the dependence on the GPS transmitter power and gain from ~1.08 dB/dB to a residual error of about −0.2 dB/dB. Results also showed that the calibration method eliminates the effect of the high gain antenna filtering effect, thus reducing errors as high as 10 dB on angles furthest from SMAP’s constant 40° incidence angle.

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Using CYGNSS Data to Monitor China’s Flood Inundation during Typhoon and Extreme Precipitation Events in 2017

Cited by 56 publications

References 21 publications

Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach

Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach

Desert Roughness Retrieval Using CYGNSS GNSS-R Data

The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities

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