scholarly journals Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study

2021 ◽  
Vol 13 (5) ◽  
pp. 936
Author(s):  
Fengguan Gu ◽  
Rui Zhang ◽  
Xiangshan Tian-Kunze ◽  
Bo Han ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Remote Sensing Data ◽  
Global Ocean ◽  
Economic Losses ◽  
Ice Thickness ◽  
Spatiotemporal Resolution ◽  
Sea Ice Thickness ◽  
Sensing Data ◽  
Skill Scores

The accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-corrected reflectance from Geostationary Ocean Color Imager (GOCI) images in the winters of 2012 and 2013. Compared with previously developed SIT retrieval methods (e.g., the method based on the thermodynamic principle of sea ice) using remote sensing data, our method has significant advantages with respect to the inversion accuracy (achieving retrieval skill scores as high as 0.86) and spatiotemporal resolution. Moreover, there is no significant increase in the computational cost with this method, which makes the method suitable for operational SIT retrieval in the global ocean.

scholarly journals SEA ICE THICKNESS MEASUREMENT BY GROUND PENETRATING RADAR FOR GROUND TRUTH OF MICROWAVE REMOTE SENSING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1259-1262
Author(s):  
M. Matsumoto ◽  
M. Yoshimura ◽  
K. Naoki ◽  
K. Cho ◽  
H. Wakabayashi
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Ground Truth ◽  
Ice Thickness ◽  
Large Area ◽  
Sea Ice Thickness ◽  
Sensing Data ◽  
Ground Penetrating

Observation of sea ice thickness is one of key issues to understand regional effect of global warming. One of approaches to monitor sea ice in large area is microwave remote sensing data analysis. However, ground truth must be necessary to discuss the effectivity of this kind of approach. The conventional method to acquire ground truth of ice thickness is drilling ice layer and directly measuring the thickness by a ruler. However, this method is destructive, time-consuming and limited spatial resolution. Although there are several methods to acquire ice thickness in non-destructive way, ground penetrating radar (GPR) can be effective solution because it can discriminate snow-ice and ice-sea water interface. In this paper, we carried out GPR measurement in Lake Saroma for relatively large area (200 m by 300 m, approximately) aiming to obtain grand truth for remote sensing data. GPR survey was conducted at 5 locations in the area. The direct measurement was also conducted simultaneously in order to calibrate GPR data for thickness estimation and to validate the result. Although GPR Bscan image obtained from 600MHz contains the reflection which may come from a structure under snow, the origin of the reflection is not obvious. Therefore, further analysis and interpretation of the GPR image, such as numerical simulation, additional signal processing and use of 200 MHz antenna, are required to move on thickness estimation.

scholarly journals OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2294
Author(s):  
Hua Su ◽  
Haojie Zhang ◽  
Xupu Geng ◽  
Tian Qin ◽  
Wenfang Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Network Architecture ◽  
Climate Changes ◽  
Heat Content ◽  
Remote Sensing Data ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Coefficient Of Determination ◽  
Sensing Data

Retrieving information concerning the interior of the ocean using satellite remote sensing data has a major impact on studies of ocean dynamic and climate changes; however, the lack of information within the ocean limits such studies about the global ocean. In this paper, an artificial neural network, combined with satellite data and gridded Argo product, is used to estimate the ocean heat content (OHC) anomalies over four different depths down to 2000 m covering the near-global ocean, excluding the polar regions. Our method allows for the temporal hindcast of the OHC to other periods beyond the 2005–2018 training period. By applying an ensemble technique, the hindcasting uncertainty could also be estimated by using different 9-year periods for training and then calculating the standard deviation across six ensemble members. This new OHC product is called the Ocean Projection and Extension neural Network (OPEN) product. The accuracy of the product is accessed using the coefficient of determination (R2) and the relative root-mean-square error (RRMSE). The feature combinations and network architecture are optimized via a series of experiments. Overall, intercomparison with several routinely analyzed OHC products shows that the OPEN OHC has an R2 larger than 0.95 and an RRMSE of <0.20 and presents notably accurate trends and variabilities. The OPEN product can therefore provide a valuable complement for studies of global climate changes.

Remote Sensing of Sea Ice Thickness and Salinity With 0.5–2 GHz Microwave Radiometry

2019 ◽  
Vol 57 (11) ◽  
pp. 8672-8684 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Ronald Kwok ◽  
Lars Kaleschke ◽  
Domenic J. Belgiovane ◽  
Chi-Chih Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Microwave Radiometry ◽  
Ice Thickness ◽  
Sea Ice Thickness

Using remote sensing to estimate sea ice thickness in the Bohai Sea, China based on ice type

2009 ◽  
Vol 30 (17) ◽  
pp. 4539-4552 ◽  
Author(s):  
Li Ning ◽  
Feng Xie ◽  
Wei Gu ◽  
Yingjun Xu ◽  
Shuqing Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Bohai Sea ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The Bohai Sea

scholarly journals On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

2018 ◽  
Vol 12 (3) ◽  
pp. 993-1012 ◽  
Author(s):  
Lu Zhou ◽  
Shiming Xu ◽  
Jiping Liu ◽  
Bin Wang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Snow Depth ◽  
Microwave Remote Sensing ◽  
Ice Cover ◽  
Ice Thickness ◽  
Laser Altimetry ◽  
Sea Ice Thickness ◽  
Passive Remote Sensing ◽  
L Band

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

scholarly journals Data assimilation in sea-ice monitoring

2000 ◽  
Vol 31 ◽  
pp. 327-332 ◽  
Author(s):  
Ronald L. S. Weaver ◽  
Konrad Steffen ◽  
John Heinrichs ◽  
James A. Maslanik ◽  
Gregory M. Flato
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Sea Ice ◽  
Global Climate ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Physical Processes ◽  
Error Statistics ◽  
Sensing Data ◽  
Ice Model

AbstractThe detection of small changes in concentration or thickness in the Arctic or Antarctic ice cover is an important topic in the current global-climate-change debate. Change detection using satellite data alone requires rigorous error analysis for their derived ice products, including inter-satellite validation for long time series. All models of physical processes are only approximations, and the best models of complicated physical processes have errors and uncertainties. A promising approach is data assimilation, combining model, in situ data and satellite remote-sensing data. Sea-ice monitoring from satellite, ice-model estimates, and the potential benefit of combining the two are discussed in some detail. In a case-study we demonstrate how the sea-ice backscatter for the Beaufort Sea region was derived using a backscattering model in combination with an ice model. We conclude that, for data assimilation, the first steps include the use of simple models, moving, with success at this level, to progressively more complex models. We also recommend reconfiguring the current remote-sensing data to include precise time tags with each pixel. For example, the current Special Sensor Microwave Imager data might be reissued in a time-tagged orbital (or gridded) format as opposed to the currently available daily averaged gridded data. Finally, error statistics and quality-control information also need to be readily available in a form useful for assimilation. The effectiveness of data-assimilation techniques is directly linked to the availability of data error statistics.

scholarly journals On the Retrieval of Sea Ice Thickness and Snow Depth using Concurrent Laser Altimetry and L-Band Remote Sensing Data

2017 ◽  
Author(s):  
Lu Zhou ◽  
Shiming Xu ◽  
Jiping Liu ◽  
Bin Wang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Snow Depth ◽  
Ice Cover ◽  
Retrieval Algorithm ◽  
Ice Thickness ◽  
Laser Altimetry ◽  
Sea Ice Thickness ◽  
Passive Remote Sensing ◽  
L Band

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, are key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved, as compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry, as well the uncertainty associated with the radiation model are potential sources of error. The proposed retrieval algorithm (or methodology) can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat and ICESat-2.

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