scholarly journals Evaluation of a New Merged Sea-Ice Concentration Dataset at 1 km Resolution from Thermal Infrared and Passive Microwave Satellite Data in the Arctic

2020 ◽  
Vol 12 (19) ◽  
pp. 3183
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen ◽  
Leif Toudal Pedersen
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Thermal Infrared ◽  
Poor Quality ◽  
Passive Microwave ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Continuous Coverage ◽  
Uncertainty Estimates ◽  
Ice Concentration

Sea-ice concentration (SIC) data with fine spatial resolution and spatially continuous coverage are needed, for example, for estimating heat fluxes. Passive microwave measurements of the Advanced Scanning Microwave Radiometer 2 (AMSR2) offer spatial continuity, but are limited to spatial resolutions of 5 km and coarser. Thermal infrared data of the Moderate Resolution Imaging Spectroradiometer (MODIS) provide a spatial resolution of 1 km, but are limited to cloud-free scenes. We exploit the benefits of both and present a merged SIC dataset with 1 km spatial resolution and spatially continuous coverage for the Arctic. MODIS and AMSR2 SIC are retrieved separately and then merged by tuning the MODIS SIC to preserve the mean AMSR2 SIC. We first evaluate the variability of the dynamically retrieved MODIS ice tie-point. Varying the starting position of the area used for the tie-point retrieval changes the MODIS SIC by on average 1.9%, which we mitigate by considering different starting positions and using the average as ice tie-point. Furthermore, the SIC datasets are evaluated against a reference dataset derived from Sentinel-2A/B reflectances between February and May 2019. We find that the merged SIC are 1.9% smaller than the reference SIC if thin ice is considered as ice and 4.9% higher if thin ice is considered as water. There is only a slight bias (0.3%) between the MODIS and the merged SIC; however, the root mean square deviation of 5% indicates that the two datasets do yield different results. In an example of poor-quality MODIS SIC, we identify an unscreened cloud and high ice-surface temperature as reasons for the poor quality. Still, the merged SIC are of similar quality as the passive microwave SIC in this example. The benefit of merging MODIS and AMSR2 data is demonstrated by showing that the finer resolution of the merged SIC compared to the AMSR2 SIC allows an enhanced potential for the retrieval of leads. At the same time, the data are available regardless of clouds. Last, we provide uncertainty estimates. The MODIS and merged SIC uncertainty are between 5% and 10% from February to April and increase up to 25% (merged SIC) and 35% (MODIS SIC) in May. They are identified as conservative uncertainty estimates.

Sea–ice concentration at 1 km resolution in summer from merged visible and microwave radiometer observations

2021 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen
Keyword(s):  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Statistical Consistency ◽  
Microwave Data ◽  
Infrared Data

<p>Sea–ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean–atmosphere heat exchange. Satellite–based remote sensing offers the possibility for large–scale monitoring of the sea–ice concentration. Using passive microwave measurements, it is possible to observe the sea–ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m–1 km, but need clear–sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal–infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea–ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea–ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year–long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.</p>

scholarly journals Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

2020 ◽  
Vol 12 (7) ◽  
pp. 1060 ◽  
Author(s):  
Lise Kilic ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Georg Heygster ◽  
Victor Pellet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Special Focus ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Ice Concentration

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

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 The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset

2019 ◽  
Vol 13 (7) ◽  
pp. 2051-2073 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen ◽  
Christian Haas ◽  
Larysa Istomina ◽  
Frank Kauker ◽  
...  
Keyword(s):  
Sea Ice ◽  
Growth Yield ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
Pressure System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Spatial Coverage ◽  
Ice Concentration

Abstract. Observations of sea-ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea-ice concentrations. This yields a merged sea-ice concentration product combining the gap-free spatial coverage of the passive microwave sea-ice concentration and the 1 km resolution of the thermal infrared sea-ice concentration. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea-ice concentration product resolves leads at sea-ice concentrations between 60 % and 90 %. They are not resolved by the coarser passive microwave sea-ice concentration product. The benefit of the merged product is most pronounced during the formation of the polynya. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly below 0 ∘C and thus more than 20 ∘C higher than normal. Two estimates of thermodynamic sea-ice growth yield sea-ice thicknesses of 60 and 65 cm at the end of March in the area opened by the polynya. This differed from airborne sea-ice thickness measurements, indicating that sea-ice growth processes in the polynya are complicated by rafting and ridging. A sea-ice volume of 33 km3 was produced thermodynamically.

scholarly journals VALIDATION OF ASMR2 SEA ICE CONCENTRATION DATA USING MODIS DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1741-1746
Author(s):  
K. Cho ◽  
R. Nagao ◽  
K. Naoki
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Threshold Level ◽  
Passive Microwave ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Free Condition ◽  
Modis Data ◽  
Ice Concentration

<p><strong>Abstract.</strong> Passive microwave radiometer AMSR2 was launched by JAXA in May 2012 on-board GCOM-W satellite. The antenna diameter of AMSR2 is 2.0&amp;thinsp;m which provide highest spatial resolution as a passive microwave radiometer in space. The sea ice concentration images derived from AMSR2 data allow us to monitor the detailed sea ice distributions of whole globe every day. The AMSR bootstrap algorithm developed by Dr. Josefino Comiso is used as the standard algorithm for calculating sea ice concentration from AMSR2 data. Under the contract with JAXA, the authors have been evaluating the performance of the algorithm. The sea ice concentration estimated from AMSR2 data were evaluated using MODIS data observed from Aqua satellite within few minutes after AMSR2 observation from GCOM-W. Since the spatial resolution of MODIS is much higher than that of AMSR2, under the cloud free condition, the ice concentration corresponds to the size of a pixel of AMSR2 can be calculated much accurately with MODIS data. The procedures of the evaluation are as follows. Firstly, MODIS band 1 reflectance were binarized to discriminate sea ice(1) from open water(0) and sea ice concentration of each pixel size of AMSR2 were calculated. In calculating sea ice concentration from MODIS data, the selection of the threshold level of MODIS band 1 reflectance is critical. Through the detailed evaluation, the authors selected 5% as the optimum threshold level. Then the AMSR2 sea ice concentration of each pixel was compared with the sea ice concentration calculated from MODIS data. The result suggested the possibility of estimating sea ice concentration from AMSR2 data with less than 10% error under the cloud free condition.</p>

scholarly journals Antarctic summer sea ice concentration and extent: comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea ice charts

2008 ◽  
Vol 2 (4) ◽  
pp. 623-647 ◽  
Author(s):  
B. Ozsoy-Cicek ◽  
H. Xie ◽  
S. F. Ackley ◽  
K. Ye
Keyword(s):  
Sea Ice ◽  
Linear Trend ◽  
Passive Microwave ◽  
The Arctic ◽  
Poor Correlation ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Ice Edge

Abstract. Antarctic sea ice cover has shown a slight increase in overall observed ice extent as derived from satellite mapping from 1979 to 2008, contrary to the decline observed in the Arctic regions. Spatial and temporal variations of the Antarctic sea ice however remain a significant problem to monitor and understand, primarily due to the vastness and remoteness of the region. While satellite remote sensing has provided and has great future potential to monitor the variations and changes of sea ice, uncertainties remain unresolved. In this study, the National Ice Center (NIC) ice edge and the AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) ice extent are examined, while the ASPeCt (Antarctic Sea Ice Process and Climate) ship observations from the Oden expedition in December 2006 are used as ground truth to verify the two products during Antarctic summer. While there is a general linear trend between ASPeCt and AMSR-E ice concentration estimates, there is poor correlation (R2=0.41) and AMSR-E tends to underestimate the low ice concentrations. We also found that the NIC sea ice edge agrees well with ship observations, while the AMSR-E shows the ice edge further south, consistent with its poorer detection of low ice concentrations. The northward extent of the ice edge at the time of observation (NIC) had mean values varying from 38 km to 102 km greater on different days for the area as compared with the AMSR-E sea ice extent. For the circumpolar area as a whole in the December period examined, AMSR-E therefore underestimates the area inside the ice edge at this time by up to 14% or, 1.5 million km2 less area, compared to the NIC ice charts. These differences alone can account for more than half of the purported sea ice loss between the pre 1960s and the satellite era suggested earlier from comparative analysis of whale catch data with satellite derived data. Preliminary comparison of satellite scatterometer data suggests better resolution of low concentrations than passive microwave, and therefore better fidelity with ship observations and NIC charts of the area inside the ice edge during Antarctic summer.

scholarly journals Observation of the 2018 North Greenland polynya with a new merged optical and passive microwave sea ice concentration dataset

2019 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen ◽  
Christian Haas ◽  
Larysa Istomina ◽  
Frank Kauker ◽  
...  
Keyword(s):  
Sea Ice ◽  
Growth Yield ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
Pressure System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Air Temperatures ◽  
Spatial Coverage ◽  
Ice Concentration

Abstract. Observations of sea ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km, but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea ice concentrations. This yields a merged sea ice concentration product which combines the gap-free spatial coverage of the passive microwave sea ice concentrations and the 1 km resolution of the thermal infrared sea ice concentrations. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea ice concentration product resolves leads as sea ice concentration between 60 % and 80 %. They are not resolved by the coarser passive microwave sea ice concentration product. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly beneath 0 °C and thus more than 20 °C above the average. Two estimates of thermodynamic growth yield accumulated growth of 60 and 65 cm at the end of March. This compares well with airborne sea ice thickness measurements. 33 km3 of sea ice were produced thermodynamically.

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

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

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.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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