Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture

Chew, Clara; Small, E. E.

doi:10.1029/2018gl077905

Cited by 259 publications

(209 citation statements)

References 38 publications

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“…It consists of a constellation of eight microsatellites, whose primary objective is the measurement of near‐surface wind speed over the ocean in and near the inner core of tropical cyclones (Ruf, Atlas, et al, , Ruf, Gleason, & McKague, ). CYGNSS operates continuously over both ocean and land, and some other scientific utilities have been demonstrated with the CYGNSS observations (Chew et al, ; Chew & Small, , Kim & Lakshmi, ; Ruf, Chew, et al, ). Moreover, the raw intermediate frequency samples of the direct and reflected signals (known as Level 0 or L0 data) have been also recorded occasionally for the exploration of other possible GNSS‐R applications.…”

Section: Introductionmentioning

confidence: 98%

Lake Level and Surface Topography Measured With Spaceborne GNSS‐Reflectometry From CYGNSS Mission: Example for the Lake Qinghai

Cardellach

Fabra

et al. 2018

Geophysical Research Letters

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This paper demonstrates inland water altimetry of spaceborne Global Navigation Satellite System‐Reflectometry (GNSS‐R) using the Cyclone GNSS (CYGNSS) mission data. From 12 tracks of raw data overpassing the Lake Qinghai, the bistatic group delay and carrier phase delay are extracted from the quasi‐specular GNSS reflections. The water levels derived from the group delay observations are consistent with the Cryosat‐2 and in situ gauge measurements. The surface topography profiles from the phase delay measurements show good self‐consistence along the coincident tracks. Decimeter‐level surface height anomalies are resolved with the phase delay measurements, which can be associated with the accuracy degradation of the geoid model in this region. Systematic errors remain in both group delay and phase delay altimetry measurements, which are attributed to the receiver orbit errors and ionospheric correction residuals. These limitations can be eliminated in further GNSS‐R missions dedicated to altimetry applications.

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Section: Introductionmentioning

confidence: 98%

Lake Level and Surface Topography Measured With Spaceborne GNSS‐Reflectometry From CYGNSS Mission: Example for the Lake Qinghai

Cardellach

Fabra

et al. 2018

Geophysical Research Letters

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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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“…The SNR or power of the reflected signal is not only sensitive to the surface condition but also affected by the transmitted power, antenna gain, and distances (Voronovich & Zavorotny, ; Zavorotny & Voronovich, ), which should be corrected before further analyses. Due to the lack of a simple model for ice sheet reflected GNSS signal to determine both the coherent and incoherent scattered power, these effects are corrected by simply assuming a coherent reflection to calculate the reflectivity

{normalΓ}_{rl}

similar to (Chew & Small, )

{normalΓ}_{rl} \propto S N R - N_{0} - G_{normalr} - G_{normalt} - P_{normalt} + 20 \log ()(, R_{ts} + R_{rs}) - 20 \log false(λ false) + 20 \log ()(, 4 π),

where

S N R

is the SNR ratio of the DDM provided in the TDS‐1 L1b data,

N_{0}

is the thermal noise floor,

G_{normalr}

is the receiving antenna gain,

G_{normalt}

and

P_{normalt}

are the transmitting antenna gain and transmitting signal power provided in Wang et al (), which are estimated with a ground‐based GPS signal power monitor system,

R_{ts}

is the distance between the transmitter and the specular point,

R_{rs}

is the distance between the receiver and the specular point, and

λ

is the GPS L1 signal carrier wavelength.…”

Section: Data Sets and Methodsmentioning

confidence: 99%

Measuring Greenland Ice Sheet Melt Using Spaceborne GNSS Reflectometry From TechDemoSat‐1

Cardellach

Fabra

et al. 2020

Geophysical Research Letters

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The capability of spaceborne Global Navigation Satellite System‐reflectometry (GNSS‐R) for Greenland ice sheet melt detection is investigated using the TechDemoSat‐1 satellite (TDS‐1) data. The melt detection is based on the sensitivity of GNSS‐R signal to the presence of liquid water in snow pack. Statistical analysis during the 2018 melt season shows melt detection using GNSS‐R is possible with an agreement of 90% comparing to the microwave radiometer (MWR) data. In 37% of the cases GNSS‐R detects melting when MWR does not. The inconsistency observed between GNSS‐R and MWR is mainly due to the different features of the two observation concepts and the limitations of currently available GNSS‐R data. Furthermore, the large penetration depth of the GNSS signal can potentially provide complementary information on melt occurrence in ice sheet subsurface. These results show the potential of future GNSS‐R missions for ice sheet melt detection.

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Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture

Abstract: This paper quantifies the relationship between forward scattered L-band Global Navigation

Cited by 259 publications

References 38 publications

Lake Level and Surface Topography Measured With Spaceborne GNSS‐Reflectometry From CYGNSS Mission: Example for the Lake Qinghai

Lake Level and Surface Topography Measured With Spaceborne GNSS‐Reflectometry From CYGNSS Mission: Example for the Lake Qinghai

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

Measuring Greenland Ice Sheet Melt Using Spaceborne GNSS Reflectometry From TechDemoSat‐1

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