Neural Networks Based Sea Ice Detection and Concentration Retrieval From GNSS-R Delay-Doppler Maps

Yan, Qingyun; Huang, Weimin; Moloney, Cecilia

doi:10.1109/jstars.2017.2689009

Cited by 81 publications

(59 citation statements)

References 22 publications

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“…It should be noted that while previous studies (Alonso‐Arroyo et al, ; Yan et al, ; Yan & Huang, ; Zhu et al, ) have attained similar or higher accuracies than those seen here, the detection techniques described in those studies are often used across only one season or a single track and have not been applied across the data set as a whole. The method developed and applied here is trained on 20% of the filtered data and shown to produce excellent agreement with CCI across the whole of the remaining data.…”

Section: Validation Against Esa CCI Sea Ice Concentrationsupporting

confidence: 50%

“…Previous studies (Alonso‐Arroyo et al, ; Rivas et al, ; Yan et al, ; Yan & Huang, ; Zhu et al, ) have been shown to correctly distinguish between sea ice and open ocean in up to 98.4% of cases in the detection of sea ice compared to colocated passive microwave data. However, these studies were limited in scope, examining small subsets of TDS‐1 observations only.…”

Section: Introductionmentioning

confidence: 94%

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Sea Ice Detection Using GNSS‐R Data From TechDemoSat‐1

2019

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A new method for the detection of sea ice using GNSS‐R (Global Navigation Satellite Systems Reflectometry) is presented and applied to 33 months of data from the U.K. TechDemoSat‐1 mission. This method of sea ice detection shows the potential for a future GNSS‐R polar mission, attaining an agreement of over 98% and 96% in the Antarctic and Arctic, respectively, when compared to the European Space Agency's Climate Change Initiative sea ice concentration product. The algorithm uses a combination of two parameters derived from the delay‐Doppler Maps to quantify the spread of power in delay and Doppler. Application of thresholds then allows sea ice to be distinguished from open water. Differences between the TechDemoSat‐1 sea ice detection and comparison data sets are explored. The results provide information on the seasonal and multiyear changes in sea ice distribution of the Arctic and Antarctic. Future potential and applications of this technique are discussed.

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Section: Validation Against Esa CCI Sea Ice Concentrationsupporting

confidence: 50%

Section: Introductionmentioning

confidence: 94%

Sea Ice Detection Using GNSS‐R Data From TechDemoSat‐1

2019

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“…The main data product, i.e., Level 1b data, is the Delay-Doppler Map (DDM) of GPS scattered power, which has been analyzed by different groups for various remote sensing applications, such as ocean scatterometry [e.g., Foti et al, 2015], ocean altimetry [e.g., Clarizia et al, 2016], and soil moisture [e.g., Chew et al, 2016]. The DDMs collected over sea ice have been also analyzed for sea ice detection and sea ice concentration retrieval [e.g., Yan and Huang, 2016;Yan et al, 2017;Alonso-Arroyo et al, 2017]. With the DDM, only the coarse delay of the reflected signal can be extracted from the ranging code, resulting in a very low altimetry precision, e.g., 7.4-8.1 m precision over sea surface with 1 s observations [Clarizia et al, 2016].…”

Section: 1002/2017gl074513mentioning

confidence: 99%

“…However, carrier phase information can be only retrieved from coherent reflections of GNSS signal. The DDMs collected over sea ice have been also analyzed for sea ice detection and sea ice concentration retrieval [e.g., Yan and Huang, 2016;Yan et al, 2017;Alonso-Arroyo et al, 2017]. The presence of sea ice at the water surface significantly shifts the diffuse reflection limit and improves the phase coherence of L band observations.…”

Section: Introductionmentioning

confidence: 99%

First spaceborne phase altimetry over sea ice using TechDemoSat‐1 GNSS‐R signals

Cardellach

Fabra

et al. 2017

Geophysical Research Letters

169

127

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A track of sea ice reflected Global Navigation Satellite System (GNSS) signal collected by the TechDemoSat‐1 mission is processed to perform phase altimetry over sea ice. High‐precision carrier phase measurements are extracted from coherent GNSS reflections at a high angle of elevation (>57°). The altimetric results show good consistency with a mean sea surface (MSS) model, and the root‐mean‐square difference is 4.7 cm with an along‐track sampling distance of ∼140 m and a spatial resolution of ∼400 m. The difference observed between the altimetric results and the MSS shows good correlation with the colocated sea ice thickness data from Soil Moisture and Ocean Salinity. This is consistent with the reflecting surface aligned with the bottom of the ice‐water interface, due to the penetration of the GNSS signal into the sea ice. Therefore, these high‐precision altimetric results have potential to be used for determination of sea ice thickness.

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“…The reader is referred to these references for the details of the TDS-1 mission. Sea surface applications are reported in [23] for scatterometry and in [24] for altimetry, while sea ice detection is studied in [25][26][27][28]. Soil moisture applications of TDS-1 are given in [29,30] and the ionosphere is studied in [31].…”

Section: Gnss-r and The Tds-1 Reflectometry Instrumentmentioning

confidence: 99%

GNSS-R Ice Sheet Altimetry in Greenland Using TDS-1 Data

Rius¹,

Cardellach²,

Fabra³

et al. 2017

Preprint

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Radar altimetry provides valuable measurements to characterize the state and the evolution of the Antartica and Greenland ice sheet cover. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential capacity of complementing the dedicated radar altimeters incrementing the temporal and spatial resolution of the surface height measurements. In this work we perform an study of the Greenland ice sheet using data obtained by the GNSS-R instrument aboard the British TechDemoSat-1 (TDS-1) satellite mission, designed primarily to provide sea state information, like sea surface roughness or wind, but not altimetric products. The data has been analyzed with altimetric methodologies, already proved in aircraft based experiments, to extract signal delay observables to be used to infer the topography of the Greenland cover. The penetration depth of the GNSS signals into ice has also considered. The topographic signal obtained is consistent with those obtained with other passive or active microwave sensors. The main conclusion derived from this work is that GNSS-R also provides valuable measurements of the ice sheet cover and, as taken at a variety of geometries and at least two frequency bands, they prospect different depths into the ice. They have thus potential to complement our understanding of the ice firn and its evolution.

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Neural Networks Based Sea Ice Detection and Concentration Retrieval From GNSS-R Delay-Doppler Maps

Cited by 81 publications

References 22 publications

Sea Ice Detection Using GNSS‐R Data From TechDemoSat‐1

Sea Ice Detection Using GNSS‐R Data From TechDemoSat‐1

First spaceborne phase altimetry over sea ice using TechDemoSat‐1 GNSS‐R signals

GNSS-R Ice Sheet Altimetry in Greenland Using TDS-1 Data

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