scholarly journals High-resolution maps of the sea-ice concentration from MODIS satellite data

2004 ◽  
Vol 31 (20) ◽  
Author(s):  
Clemens Drüe
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Satellite Data ◽  
Sea Ice Concentration ◽  
Ice Concentration

scholarly journals Version 2 of the EUMETSAT OSI SAF and ESA CCI Sea Ice Concentration Climate Data Records

2018 ◽  
Author(s):  
Thomas Lavergne ◽  
Atle Macdonald Sørensen ◽  
Stefan Kern ◽  
Rasmus Tonboe ◽  
Dirk Notz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Grid Point ◽  
Ice Cover ◽  
Quantitative Information ◽  
Horizontal Resolution ◽  
Climate Data ◽  
Time Step ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: First, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz and either 6 GHz or 19 GHz), in their horizontal resolution (25 km or 50 km) and in the time period they cover. We introduce the underlying algorithms and provide an initial evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.

scholarly journals Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation

2016 ◽  
Vol 10 (2) ◽  
pp. 761-774 ◽  
Author(s):  
Qinghua Yang ◽  
Martin Losch ◽  
Svetlana N. Losa ◽  
Thomas Jung ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Satellite Data ◽  
General Circulation ◽  
The Arctic ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Uncertainty Estimates ◽  
Ice Concentration ◽  
Data Products

Abstract. Data assimilation experiments that aim at improving summer ice concentration and thickness forecasts in the Arctic are carried out. The data assimilation system used is based on the MIT general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter. The effect of using sea ice concentration satellite data products with appropriate uncertainty estimates is assessed by three different experiments using sea ice concentration data of the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI) which are provided with a per-grid-cell physically based sea ice concentration uncertainty estimate. The first experiment uses the constant uncertainty, the second one imposes the provided SICCI uncertainty estimate, while the third experiment employs an elevated minimum uncertainty to account for a representation error. Using the observation uncertainties that are provided with the data improves the ensemble mean forecast of ice concentration compared to using constant data errors, but the thickness forecast, based on the sparsely available data, appears to be degraded. Further investigating this lack of positive impact on the sea ice thicknesses leads us to a fundamental mismatch between the satellite-based radiometric concentration and the modeled physical ice concentration in summer: the passive microwave sensors used for deriving the vast majority of the sea ice concentration satellite-based observations cannot distinguish ocean water (in leads) from melt water (in ponds). New data assimilation methodologies that fully account or mitigate this mismatch must be designed for successful assimilation of sea ice concentration satellite data in summer melt conditions. In our study, thickness forecasts can be slightly improved by adopting the pragmatic solution of raising the minimum observation uncertainty to inflate the data error and ensemble spread.

scholarly journals Summer and early-fall Sea-ice concentration in the Ross Sea: comparison of in Situ ASPeCt observations and satellite passive microwave estimates

2006 ◽  
Vol 44 ◽  
pp. 303-309 ◽  
Author(s):  
Margaret A. Knuth ◽  
Stephen F. Ackley
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Ross Sea ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Point Sampling ◽  
Continuous Sampling ◽  
Ice Concentration ◽  
Observation Protocol

AbstractSea-ice conditions were observed using the AsPeCt observation protocol on three cruises in the Ross Sea spanning the Antarctic Summer Season (APIs, December 1999–February 2000; Anslope 1, March–April 2003; Anslope 2, February–April 2004). An additional dataset was analyzed from helicopter video Surveys taken during the APIs cruise. The helicopter video was analyzed using two techniques: first, as an AsPeCt dataset where it was Sampled visually for ice concentration, floe Sizes and ice type on a point basis at 11 km intervals; Second, computerized image processing on a Subset of nine helicopter flights to obtain ice concentration on a continuous basis (1 S intervals) for the entire flight. This continuous Sampling was used to validate the point-sampling methods to characterize the ice cover; the ‘AsPeCt Sampling’ on the helicopter video and the use of the AsPeCt protocol on the Ship Surveys. The estimates for average ice concentration agreed within 5% for the continuous digitized data and point Sampling at 11 km intervals in this comparison. The Ship and video in Situ datasets were then compared with ice concentrations from SsM/I passive microwave Satellite data derived using the Bootstrap and NAsA-Team algorithms. Less than 50% of the variance in Summer ice concentration observed in Situ was explainable by Satellite microwave data. The Satellite data were also inconsistent in measurement, both underestimating and overestimating the concentration for Summer conditions, but improved in the fall period when conditions were colder. This improvement was in the explainable variance of >70%, although in Situ concentration was underestimated (albeit consistently) by the Satellite imagery in fall.

scholarly journals Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records

2019 ◽  
Vol 13 (1) ◽  
pp. 49-78 ◽  
Author(s):  
Thomas Lavergne ◽  
Atle Macdonald Sørensen ◽  
Stefan Kern ◽  
Rasmus Tonboe ◽  
Dirk Notz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Grid Point ◽  
Ice Cover ◽  
Quantitative Information ◽  
Horizontal Resolution ◽  
Climate Data ◽  
Time Step ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: first, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz and either 6 or 19 GHz), in their horizontal resolution (25 or 50 km), and in the time period they cover. We introduce the underlying algorithms and provide an evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.

scholarly journals Arctic sea ice cover data from spaceborne SAR by deep learning

2020 ◽  
Author(s):  
Yi-Ran Wang ◽  
Xiao-Ming Li
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Sea Ice ◽  
Spatial Resolution ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Absolute Difference ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high-resolution sea ice cover data are drawing increasing attention for polar navigation, environmental research, and offshore operations. In this paper, we focused on developing an approach for deriving a high-resolution sea ice cover product for the Arctic using Sentinel-1 (S1) dual-polarization (horizontal-horizontal, HH, and horizontal-vertical, HV) data in extra wide swath (EW) mode. The approach for discriminating sea ice from open water by synthetic aperture radar (SAR) data is based on a modified U-Net architecture, a deep learning network. By employing an integrated stacking model to combine multiple U-Net classifiers with diverse specializations, sea ice segmentation is achieved with superior accuracy over any individual classifier. We applied the proposed approach to over 28,000 S1 EW images acquired in 2019 to obtain sea ice cover products in a high spatial resolution of 400 m. By converting the S1-derived sea ice cover to concentration and then compared with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data, showing an average absolute difference of 5.55 % with seasonal fluctuations. A direct comparison with Interactive Multisensor Snow and Ice Mapping System (IMS) daily sea ice cover data achieves an average accuracy of 93.98 %. These results show that the developed S1-derived sea ice cover results are comparable to the AMSR and IMS data in terms of overall accuracy but superior to these data in presenting detailed sea ice cover information, particularly in the marginal ice zone (MIZ). Data are available at: https://doi.org/10.11922/sciencedb.00273 (Wang and Li, 2020).

scholarly journals A sea ice concentration estimation algorithm utilizing radiometer and SAR data

2014 ◽  
Vol 8 (5) ◽  
pp. 1639-1650 ◽  
Author(s):  
J. Karvonen
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Estimation Algorithm ◽  
Sea Ice Concentration ◽  
Finnish Meteorological Institute ◽  
Brightness Temperatures ◽  
Concentration Estimation ◽  
Ice Concentration ◽  
Bootstrap Algorithm ◽  
The University

Abstract. We have studied the possibility of combining the high-resolution synthetic aperture radar (SAR) segmentation and ice concentration estimated by radiometer brightness temperatures. Here we present an algorithm for mapping a radiometer-based concentration value for each SAR segment. The concentrations are estimated by a multi-layer perceptron (MLP) neural network which has the AMSR-2 (Advanced Microwave Scanning Radiometer 2) polarization ratios and gradient ratios of four radiometer channels as its inputs. The results have been compared numerically to the gridded Finnish Meteorological Institute (FMI) ice chart concentrations and high-resolution AMSR-2 ASI (ARTIST Sea Ice) algorithm concentrations provided by the University of Hamburg and also visually to the AMSR-2 bootstrap algorithm concentrations, which are given in much coarser resolution. The differences when compared to FMI daily ice charts were on average small. When compared to ASI ice concentrations, the differences were a bit larger, but still small on average. According to our comparisons, the largest differences typically occur near the ice edge and sea–land boundary. The main advantage of combining radiometer-based ice concentration estimation and SAR segmentation seems to be a more precise estimation of the boundaries of different ice concentration zones.

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