Sea Ice Sensing From GNSS-R Data Using Convolutional Neural Networks

Yan, Qingyun; Huang, Weimin

doi:10.1109/lgrs.2018.2852143

Cited by 89 publications

(49 citation statements)

References 12 publications

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“…Thanks to the availability of millions of TDS-1 DDM data, the task of SIC estimation has been fulfilled. Nowadays, all the existing methods are machine learning-based techniques, including NNs [69], CNNs [63], and support vector regression (SVR) [89]. The procedure for this regression application is similar to that for sea ice detection described in Section 2.4, and the main difference lies in the fact that the target output is set as the SIC values instead of surface type labels.…”

Section: Sic Estimation Approachesmentioning

confidence: 99%

“…Due to the similarity of the methods for SIC estimation and sea ice detection, the implementation of these techniques will not be described here. More details can be found in [63,69,89].…”

Section: Sic Estimation Approachesmentioning

confidence: 99%

“…The first spaceborne GNSS-R measurement was carried out during the UK Disaster Monitoring Constellation (UK-DMC) mission in 2004 showing its capability of sea ice remote sensing. With the launch of TechDemoSat-1 (TDS-1, another spaceborne platform carrying a GNSS-R receiver) in 2014, the application of GNSS-R in sea ice sensing has been rapidly developed by e.g., [62][63][64][65][66][67][68][69][70]. In this paper, the techniques of sea ice remote sensing using GNSS-R are reviewed.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [63] designed a CNN architecture (see Figure 5b) that contains 1 convolution layer (which is made of 5 seven-by-seven filters) followed by 1 two-by-two pooling layer and 2 fully connected layers (whose functionality is similar to the input and hidden layers for NN). The use of a convolution layer can be described by…”

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confidence: 99%

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Sea Ice Remote Sensing Using GNSS-R: A Review

Yan

Huang

2019

Remote Sensing

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Knowledge of sea ice is critical for offshore oil and gas exploration, global shipping industries, and climate change studies. During recent decades, Global Navigation Satellite System-Reflectometry (GNSS-R) has evolved as an efficient tool for sea ice remote sensing. In particular, thanks to the availability of the TechDemoSat-1 (TDS-1) data over high-latitude regions, remote sensing of sea ice based on spaceborne GNSS-R has been rapidly growing. The goal of this paper is to provide a review of the state-of-the-art methods for sea ice remote sensing offered by the GNSS-R technique. In this review, the fundamentals of these applications are described, and their performances are evaluated. Specifically, recent progress in sea ice sensing using TDS-1 data is highlighted including sea ice detection, sea ice concentration estimation, sea ice type classification, sea ice thickness retrieval, and sea ice altimetry. In addition, studies of sea ice sensing using airborne and ground-based data are also noted. Lastly, applications based on various platforms along with remaining challenges are summarized and possible future trends are explored. In this review, concepts, research methods, and experimental techniques of GNSS-R-based sea ice sensing are delivered, and this can benefit the scientific community by providing insights into this topic to further advance this field or transfer the relevant knowledge and practice to other studies.

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Section: Sic Estimation Approachesmentioning

confidence: 99%

“…Due to the similarity of the methods for SIC estimation and sea ice detection, the implementation of these techniques will not be described here. More details can be found in [63,69,89].…”

Section: Sic Estimation Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Sea Ice Remote Sensing Using GNSS-R: A Review

Yan

Huang

2019

Remote Sensing

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“…Data from these missions enabled a variety of scientific studies. 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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Deep Learning for the Earth Sciences

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Sea Ice Sensing From GNSS-R Data Using Convolutional Neural Networks

Cited by 89 publications

References 12 publications

Sea Ice Remote Sensing Using GNSS-R: A Review

Sea Ice Remote Sensing Using GNSS-R: A Review

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

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