Sea Ice Thickness Measurement Using Spaceborne GNSS-R: First Results With TechDemoSat-1 Data

Yan, Qingyun; Huang, Weimin

doi:10.1109/jstars.2020.2966880

Cited by 44 publications

(30 citation statements)

References 34 publications

(46 reference statements)

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“…During the last decades, Global navigation satellite system reflectometry (GNSS-R) has become a valid option for ocean remote sensing (Garrison and Katzberg, 2000). The feasibilities of the GNSS-R technique for sensing different geophysical parameters, such as ocean wind speed (Clarizia and Ruf, 2016;Ruf and Balasubramaniam, 2018), significant wave height (SWH; Soulat et al, 2004;Roggenbuck et al, 2019), sea ice detection (Alonso- Arroyo et al, 2017;Yan and Huang, 2018), ocean altimetry (Li et al, 2017;Hu et al, 2020;Tabibi et al, 2020), and soil moisture (Alonso Arroyo et al, 2014;Chew and Small, 2018;Yan and Huang, 2020), have been demonstrated with theoretical analyses and massive ground-based, airborne, and spaceborne experiments.…”

Section: Introductionmentioning

confidence: 99%

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

Yang

et al. 2021

Remote Sensing of Environment

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Section: Introductionmentioning

confidence: 99%

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

Yang

et al. 2021

Remote Sensing of Environment

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“…The applications of TDS-1 data for sea ice altimetry were explored in a number of previous studies [29][30][31], while the raw data used in [29] and [30] are not in the standard dataset open to the public. Besides the detection of sea ice, GNSS-R has been applied to retrieve sea ice parameters, such as sea ice type [32], sea ice concentration [33] and sea ice thickness [34].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Zhu

Tao

et al. 2020

Remote Sensing

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Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are applied to extract six features, which are combined to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms. Firstly, the feature sequences are used as input variables and sea ice concentration (SIC) data from the Advanced Microwave Space Radiometer-2 (AMSR-2) are applied as targeted output to train the sea ice monitoring model. Hereafter, the performance of the proposed method is evaluated through comparing with the sea ice edge (SIE) data from the Special Sensor Microwave Imager Sounder (SSMIS) data. The DT- and RF-based methods achieve an overall accuracy of 97.51% and 98.03%, respectively, in the Arctic region and 95.46% and 95.96%, respectively, in the Antarctic region. The DT- and RF-based methods achieve similar accuracies, while the Kappa coefficient of RF-based approach is slightly larger than that of the DT-based approach, which indicates that the RF-based method outperforms the DT-based method. The results show the potential of monitoring sea ice using machine learning-aided GNSS-R approaches.

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“…Data quality is also affected by incident angle, antenna gain, and satellite attitude. After denoising the DDM data, we calculated the RMS values of DDW, as shown in Equation (13). We selected good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW.…”

Section: Methodsmentioning

confidence: 99%

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

Zhu

et al. 2020

Remote Sensing

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This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods.

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Sea Ice Thickness Measurement Using Spaceborne GNSS-R: First Results With TechDemoSat-1 Data

Cited by 44 publications

References 34 publications

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

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