scholarly journals Arctic-Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice-Ocean Model with Data Assimilation During the CryoSat-2 Period

2018 ◽  
Vol 123 (11) ◽  
pp. 7763-7780 ◽  
Author(s):  
Longjiang Mu ◽  
Martin Losch ◽  
Qinghua Yang ◽  
Robert Ricker ◽  
Svetlana N. Losa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Sea Ice ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Ocean Model ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Sensing Data

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 Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

2020 ◽  
Vol 13 (3) ◽  
pp. 1267-1284 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Y. Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Lake Ecosystem ◽  
Sensing Data ◽  
Localization Scheme ◽  
Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

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.

China land soil moisture EnKF data assimilation based on satellite remote sensing data

2011 ◽  
Vol 54 (9) ◽  
pp. 1430-1440 ◽  
Author(s):  
ChunXiang Shi ◽  
ZhengHui Xie ◽  
Hui Qian ◽  
MiaoLing Liang ◽  
XiaoChun Yang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Satellite remote sensing of sea-ice thickness and kinematics: a review

2010 ◽  
Vol 56 (200) ◽  
pp. 1129-1140 ◽  
Author(s):  
R. Kwok
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Satellite Remote Sensing ◽  
Satellite Altimetry ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
Sensor Technology ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Temporal Sampling

AbstractObservations of sea-ice thickness and kinematics are essential for understanding changes in sea-ice mass balance, interactions between the ice cover and the ocean and atmosphere and for improving projections of sea-ice response in a warming climate. These parameters are not directly observable with current sensor technology, but are derived from satellite altimetry and imagery. While there is progress in the retrievals of Arctic sea-ice thickness from satellite altimetry, approaches to address Southern Ocean ice thickness require additional attention. On the other hand, procedures to derive sea-ice motion from satellite imagery are more mature and better understood and have been employed to produce useful results for more than a decade. Adequate sampling of sub-daily ice motion, however, remains a challenge. Generally, satellite instruments provide large-scale coverage but the frequency of temporal sampling is limited by orbit characteristics. In this review, I focus on the approaches, uncertainties, sampling limitations and validation issues associated with the estimation of sea-ice thickness and motion. I provide a summary of current and anticipated capabilities for monitoring sea-ice thickness and kinematics from space. The prospects for continuing these measurements into the next decade, from a satellite remote-sensing perspective, are discussed.

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