Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat‐1 mission

Foti, Giuseppe; Gommenginger, Christine; Jales, Philip; Unwin, Martin; Shaw, Andrew; Robertson, Colette; Rosello, Josep

doi:10.1002/2015gl064204

Cited by 292 publications

(197 citation statements)

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“…Since no reference (direct signal) is measured, GNSS-R data are uncalibrated. Therefore, in this study, following the same procedure as in [15] and [16], the data processing is performed using the variations of the signal-to-noise ratio (SNR) of the delay-Doppler maps (DDMs) computed using 1-ms coherent integration time, followed by 1000 incoherent averages (total integration time = 1 s), as the ratio of: 1) the uncalibrated signal power computed as the average power over a 1.5 kHz × 1 chip window centered around the peak position of the DDM, and 2) the uncalibrated noise power computed as the average power over a 10 kHz × 1 chip window in the signal-free area of the DDM, before the leading edge of the waveform after compensation of the antenna gain versus the gain in the boresight direction, as in [16]. 1 Fig .…”

Section: Methodsmentioning

confidence: 99%

Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation

Camps

Park

Pablos

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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236

107

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Abstract-Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant. Index Terms-Global

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Section: Methodsmentioning

confidence: 99%

Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation

Camps

Park

Pablos

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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236

107

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“…Accurate sea surface heights and wind direction/speeds have been retrieved based on GNSS-R observations from different ground-based, airborne and spaceborne platforms [8][9][10][11][12]. The sea/ice and Scatterometry-International Space Station) mission for the minimum (330 km) and maximum (460 km) ISS orbital heights [30].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

Gao

Wang

et al. 2018

Remote Sensing

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Spaceborne GNSS-R (global navigation satellite system reflectometry) is an innovative and powerful bistatic radar remote sensing technique that uses specialized GNSS-R instruments on LEO (low Earth orbit) satellites to receive GNSS L-band signals reflected by the Earth's surface. Unlike monostatic radar, the illuminated areas are elliptical regions centered on specular reflection points. Evaluation of the spatiotemporal resolution of the reflections is necessary at the GNSS-R mission design stage for various applications. However, not all specular reflection signals can be received because the size and location of the GNSS-R antenna's available reflecting ground coverage depends on parameters including the on-board receiver antenna gain, the signal frequency and power, the antenna face direction, and the LEO's altitude. Additionally, the number of available reflections is strongly related to the number of GNSS-R LEO and GNSS satellites. By 2020, the Galileo and BeiDou Navigation Satellite System (BDS) constellations are scheduled to be fully operational at global scale and nearly 120 multi-GNSS satellites, including Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) satellites, will be available for use as illuminators. In this paper, to evaluate the future capacity for repetitive GNSS-R observations, we propose a GNSS satellite selection method and simulate the orbit of eight-satellite LEO and partial multi-GNSS constellations. We then analyze the spatiotemporal distribution characteristics of the reflections in two cases: (1) When only GPS satellites are available; (2) when multi-GNSS satellites are available separately. Simulation and analysis results show that the multi-GNSS-R system has major advantages in terms of available satellite numbers and revisit times over the GPS-R system. Additionally, the spatial density of the specular reflections on the Earth's surface is related to the LEO inclination and constellation construction.

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“…In order to assess the performance of the proposed estimators, which basically measure the coherency level UK TDS-1 data this parameter was even more restrictive (3 dB minimum SNR required) [37]. However, as…”

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confidence: 99%

Sea Ice Detection Using U.K. TDS-1 GNSS-R Data

Alonso-Arroyo

Zavorotny

Camps

2017

IEEE Trans. Geosci. Remote Sensing

150

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1A sea ice detection algorithm developed using UK TDS-1 Global Navigation Satellite Systems (GNSS)- newly formed sea ice, the scattering is mostly coherent which is typical for a mostly flat surface. In order to 7 measure the degree of coherence of the received waveform or DDM three different estimators are presented: 8 the normalized DDM average, the Trailing Edge Slope (TES), and the matched filter approach. Here we present 9 a probabilistic study based on a Bayesian approach using two different and independent ground-truth datasets. 10This approach allow us thoroughly assessing the performance of the estimators. The best results are achieved 11 for both the TES and the matched filter approach with a probability of detection of 97%, a probability of false 12 alarm of ∼ 2%, and a probability of error of 2.5%. However, the matched filter approach is preferred due to 13 its simplicity. The measurement of the sea ice concentration is also assessed in this work, but the nature of the 14 UK TDS-1 data (lack of calibrated data) does not allow us to make any specific conclusions about the sea ice 15 concentration. 16 Index Terms 17Sea Ice, coherent scattering, incoherent scattering, GNSS-R, UK TDS-1 18

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Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat‐1 mission

Cited by 292 publications

References 20 publications

Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation

Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

Sea Ice Detection Using U.K. TDS-1 GNSS-R Data

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