Sea Ice Remote Sensing Using GNSS-R: A Review

Yan, Qingyun; Huang, Weimin

doi:10.3390/rs11212565

Cited by 41 publications

(26 citation statements)

References 94 publications

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“…The elevation angle during the acquisition has an important impact on the received SNR of the reflection, as well, as shown in [20,21]. Therefore, in addition to the DDM and the SNR, these additional data are added to the algorithm.…”

Section: Gnss-r Datamentioning

confidence: 99%

“…Therefore, the GNSS-R tracks were pre-processed to filter out reflections with poor SNR, or with inconsistent absolute Doppler frequencies, as shown in [12] and explained in [13]. The elevation angle during the acquisition has an important impact on the received SNR of the reflection, as well, as shown in [20,21]. Therefore, in addition to the DDM and the SNR, these additional data are added to the algorithm.…”

Section: Gnss-r Datamentioning

confidence: 99%

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Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

et al. 2021

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CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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Section: Gnss-r Datamentioning

confidence: 99%

Section: Gnss-r Datamentioning

confidence: 99%

Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

et al. 2021

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“…It essentially focuses on ships' positions and dynamics, even though environmental maritime surveillance, such as sea-state monitoring, have also developed significantly [e.g. [24][25][26]. Maritime surveillance is mainly designed for instantaneous maritime situational awareness [26] and short-term reactions: collision avoidance, Search and Rescue (SAR), police operations, detection of dangerous or illegal events.…”

Section: Setting the Scene With Maritime Surveillance Data?mentioning

confidence: 99%

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

et al. 2020

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Maritime Spatial Planning (MSP) has become of key interest in many countries around the world in the last decade, leading to an increased need to inform the historical spatial and temporal footprint of maritime activities. If this knowledge was still recently considered a blind spot, recent developments of maritime surveillance systems (e.g. AIS, VTS, VMS) have allowed to fill some of these gaps by generating significant amounts of spatial data on ships at sea. In this paper, the use of these maritime surveillance data for planning purposes is explored through the lens of the international scientific literature. A first set of 2030 articles dealing with maritime surveillance data and collected through the Web of Science collection was explored to determine the type of data used, the maritime activities addressed and the main objective of each paper (technical developments, safety and security, environmental protection, MSP). This allowed to highlight the growing interest on maritime surveillance data in the scientific literature over the past decades, predominantly towards AIS data and shipping. Over the 2030 papers, only 63 dealt specifically with MSP. These were extracted and explored, allowing to highlight the potential of these data to feed in MSP processes, with a specific focus on fisheries. Nevertheless, the description of actual uses of surveillance-based information within MSP processes remained particularly rare. If this can be seen as a result of a science-policy delay, we suggest that it is mostly related to issues of accessibility, acceptance by economic stakeholders and appropriation by decision makers.

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“…Significant progress has been made in sea ice detection [1]- [5], type classification [6], sea ice concentration (SIC) estimation [7], [8], ice sheet [9], [10], and sea ice [11], [12] altimetry. Furthermore, a comprehensive review of sea ice sensing using GNSS-R has been presented in [13]. Nonetheless, limited work has been done for obtaining sea ice thickness (SIT), which is another important sea ice parameter.…”

Section: Introductionmentioning

confidence: 99%

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

Yan

Huang

2020

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

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In this article, an effective schematic is developed for estimating sea ice thickness (SIT) from the reflectivity (Γ) produced with TechDemoSat-1 (TDS-1) Global Navigation Satellite System-Reflectometry data. Here, Γ is formulated as the product of the propagation loss due to SIT and the reflection coefficient of underlying seawater. The effect of surface roughness on Γ is neglected when only considering signals of coherent reflection. In practice, Γ at the specular point is first generated using TDS-1 data. Afterwards, SIT is calculated from TDS-1 Γ based on the proposed reflectivity model, and verified with two sets of reference SIT data; one is obtained by the Soil Moisture Ocean Salinity (SMOS) satellite, and the other is the combined SMOS/Soil Moisture Active Passive (SMAP) measurements. This analysis is performed on the data with SIT less than 1 m. Through comparison, good consistency between the derived TDS-1 SIT and the reference SIT is obtained, with a correlation coefficient (r) of 0.84 and a root-mean-square difference (RMSD) of 9.39 cm with SMOS, and an r of 0.67 and an RMSD of 9.49 cm with SMOS/SMAP, which demonstrates the applicability of the developed model and the utility of TDS-1 data for SIT estimation. In addition, this method is proved to be useful for improving existing sea ice detection accuracy.

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Sea Ice Remote Sensing Using GNSS-R: A Review

Cited by 41 publications

References 94 publications

Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

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

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