scholarly journals Sea Ice Remote Sensing Using GNSS-R: A Review

2019 ◽  
Vol 11 (21) ◽  
pp. 2565 ◽  
Author(s):  
Qingyun Yan ◽  
Weimin Huang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Oil And Gas ◽  
Satellite System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Oil And Gas Exploration ◽  
Offshore Oil And Gas ◽  
Ice Detection ◽  
Global Navigation Satellite

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.

A new method for underwater dynamic gravimetry based on multisensor integrated navigation

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. G69-G80
Author(s):  
Zhiming Xiong ◽  
Juliang Cao ◽  
Kaixun Liao ◽  
Meiping Wu ◽  
Shaokun Cai ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Finite Impulse Response ◽  
Gravity Data ◽  
Satellite System ◽  
New Method ◽  
Integrated Navigation ◽  
Oil And Gas Exploration ◽  
Half Wavelength ◽  
Marine Gravity ◽  
Global Navigation Satellite

Underwater gravity information plays a major role in deepwater oil and gas exploration. To realize underwater dynamic gravimetry, we have developed a strapdown gravimeter mounted in a pressure capsule for adaption to the underwater environment and we adopted a two-stage towed underwater gravimetry scheme. An improved strapdown gravimeter and other underwater sensors were installed in a towed vessel to form an underwater dynamic gravimetry system. Because the global navigation satellite system cannot be used for underwater dynamic gravimetry, we developed a new method based on underwater multisensor integrated navigation, in which a federal Kalman filter was applied for error estimation. This new method allowed us to obtain the accurate attitude, velocity, and position necessary for gravity estimation. In addition, the gravity data can then be extracted from the noisy data through finite impulse response low-pass filtering. We acquired the underwater gravity data at a depth of 300 m to test the validity of the new method and evaluate the accuracy of the underwater gravity system. The results indicated a repeatability from 0.85 to 0.96 mGal at a half wavelength of approximately 0.2 km and also indicated good consistency with the marine gravity data.

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

2020 ◽  
Vol 12 (22) ◽  
pp. 3751
Author(s):  
Yongchao Zhu ◽  
Tingye Tao ◽  
Kegen Yu ◽  
Xiaochuan Qu ◽  
Shuiping Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Region ◽  
Satellite System ◽  
The Arctic ◽  
Monitoring Methods ◽  
Sea Ice Concentration ◽  
Monitoring Model ◽  
Input Variables ◽  
Global Navigation Satellite

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.

scholarly journals Retrieval of sea ice thickness using global navigation satellite system reflected signals

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1131-1136
Author(s):  
Hongxing Gao ◽  
Dongkai Yang ◽  
Qiang Wang
Keyword(s):  
Sea Ice ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Ice Thickness ◽  
Navigation Satellite ◽  
Sea Ice Thickness ◽  
Direct Signal ◽  
Reflected Signal ◽  
Global Navigation ◽  
Global Navigation Satellite

The aim of this paper is to develop a model which can be used to retrieve sea ice thickness based on global navigation satellite system reflected signals at a shore-based platform. First, the method calculates the intensity ratio of the reflected signal and the direct signal of the global navigation satellite system satellite, which is the ratio of the power of the reflected signal to the power of the direct signal. Then, the information of the sea ice thickness is obtained according to the empirical model of the sea ice thickness. In order to verify the effectiveness of the method, the global navigation satellite system reflected signals were observed in the experiment in the Bayu enclosure of Liaoning Province, China. The results show that the sea ice thickness of the global navigation satellite system reflected signal is 10–20 cm, which is consistent with the synthetic-aperture radar observation.

scholarly journals Brief communication: lack of agreement in remote sensing detection of cyclonic drift caused by Atlantic weather in Antarctic sea ice

2021 ◽  
Author(s):  
Wayne de Jager ◽  
Marcello Vichi
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Alternative Methods ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
The Impact

Abstract. Sea-ice extent variability, a measure based on satellite-derived sea ice concentration measurements, has traditionally been used as an essential climate variable to evaluate the impact of climate change on polar regions. However, concentration- based measurements of ice variability do not allow to discriminate the relative contributions made by thermodynamic and dynamic processes, prompting the need to use sea-ice drift products and develop alternative methods to quantify changes in sea ice dynamics that would indicate trends in Antarctic ice characteristics. Here, we present a new method to automate the detection of rotational drift features in Antarctic sea ice at daily timescales using currently available remote sensing ice motion products from EUMETSAT OSI SAF. Results show that there is a large discrepancy in the detection of cyclonic drift features between products, both in terms of intensity and year-to-year distributions, thus diminishing the confidence at which ice drift variability can be further analysed. Product comparisons showed that there was good agreement in detecting anticyclonic drift, and cyclonic drift features were measured to be 1.5–2.2 times more intense than anticyclonic features. The most intense features were detected by the merged product, suggesting that the processing chain used for this product could be injecting additional rotational momentum into the resultant drift vectors. We conclude that it is therefore necessary to better understand why the products lack agreement before further trend analysis of these drift features and their climatic significance can be assessed.

scholarly journals New insight from CryoSat-2 sea ice thickness for sea ice modelling

2018 ◽  
Author(s):  
David Schröder ◽  
Danny L. Feltham ◽  
Michel Tsamados ◽  
Andy Ridout ◽  
Rachel Tilling
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Atmospheric Conditions ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

A cyclone-centered perspective on the drivers of asymmetric patterns in the atmosphere and sea ice during Arctic cyclones

2021 ◽  
pp. 1-47
Author(s):  
Robin Clancy ◽  
Cecilia M. Bitz ◽  
Edward Blanchard-Wrigglesworth ◽  
Marie C. McGraw ◽  
Steven M. Cavallo
Keyword(s):  
Sea Ice ◽  
Spatial Patterns ◽  
The West ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Primary Driver ◽  
The Impact ◽  
Cold Core ◽  
East West

AbstractArctic cyclones are an extremely common, year-round phenomenon, with substantial influence on sea ice. However, few studies address the heterogeneity in the spatial patterns in the atmosphere and sea ice during Arctic cyclones. We investigate these spatial patterns by compositing on cyclones from 1985-2016 using a novel, cyclone-centered approach that reveals conditions as functions of bearing and distance from cyclone centers. An axisymmetric, cold core model for the structure of Arctic cyclones has previously been proposed, however, we show that the structure of Arctic cyclones is comparable to those in the mid-latitudes, with cyclonic surface winds, a warm, moist sector to the east of cyclones and a cold, dry sector to the west. There is no consensus on the impact of Arctic cyclones on sea ice, as some studies have shown that Arctic cyclones lead to sea ice growth and others to sea ice loss. Instead, we find that sea ice decreases to the east of Arctic cyclones and increases to the west, with the greatest changes occurring in the marginal ice zone. Using a sea ice model forced with prescribed atmospheric reanalysis, we reveal the relative importance of the dynamic and thermodynamic forcing of Arctic cyclones on sea ice. The dynamic and thermodynamic responses of sea ice concentration to cyclones are comparable in magnitude, however dynamic processes dominate the response of sea ice thickness and are the primary driver of the east-west difference in the sea ice response to cyclones.

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