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16 pages, 44785 KiB

Open AccessArticle

Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs

by Bento C. Gonçalves and Heather J. Lynch

Remote Sens. 2021, 13(18), 3562; https://doi.org/10.3390/rs13183562 - 8 Sep 2021

Cited by 11 | Viewed by 4007

Abstract

Fine-scale sea ice conditions are key to our efforts to understand and model climate change. We propose the first deep learning pipeline to extract fine-scale sea ice layers from high-resolution satellite imagery (Worldview-3). Extracting sea ice from imagery is often challenging due to [...] Read more.

Fine-scale sea ice conditions are key to our efforts to understand and model climate change. We propose the first deep learning pipeline to extract fine-scale sea ice layers from high-resolution satellite imagery (Worldview-3). Extracting sea ice from imagery is often challenging due to the potentially complex texture from older ice floes (i.e., floating chunks of sea ice) and surrounding slush ice, making ice floes less distinctive from the surrounding water. We propose a pipeline using a U-Net variant with a Resnet encoder to retrieve ice floe pixel masks from very-high-resolution multispectral satellite imagery. Even with a modest-sized hand-labeled training set and the most basic hyperparameter choices, our CNN-based approach attains an out-of-sample F1 score of 0.698–a nearly 60% improvement when compared to a watershed segmentation baseline. We then supplement our training set with a much larger sample of images weak-labeled by a watershed segmentation algorithm. To ensure watershed derived pack-ice masks were a good representation of the underlying images, we created a synthetic version for each weak-labeled image, where areas outside the mask are replaced by open water scenery. Adding our synthetic image dataset, obtained at minimal effort when compared with hand-labeling, further improves the out-of-sample F1 score to 0.734. Finally, we use an ensemble of four test metrics and evaluated after mosaicing outputs for entire scenes to mimic production setting during model selection, reaching an out-of-sample F1 score of 0.753. Our fully-automated pipeline is capable of detecting, monitoring, and segmenting ice floes at a very fine level of detail, and provides a roadmap for other use-cases where partial results can be obtained with threshold-based methods but a context-robust segmentation pipeline is desired. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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19 pages, 5824 KiB

Open AccessArticle

A Blended Sea Ice Concentration Product from AMSR2 and VIIRS

by Richard Dworak, Yinghui Liu, Jeffrey Key and Walter N. Meier

Remote Sens. 2021, 13(15), 2982; https://doi.org/10.3390/rs13152982 - 29 Jul 2021

Cited by 5 | Viewed by 2985

Abstract

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky [...] Read more.

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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19 pages, 5233 KiB

Open AccessArticle

Evaluation of 2-m Air Temperature and Surface Temperature from ERA5 and ERA-I Using Buoy Observations in the Arctic during 2010–2020

by Yining Yu, Wanxin Xiao, Zhilun Zhang, Xiao Cheng, Fengming Hui and Jiechen Zhao

Remote Sens. 2021, 13(14), 2813; https://doi.org/10.3390/rs13142813 - 17 Jul 2021

Cited by 37 | Viewed by 4939

Abstract

In data-sparse regions such as the Arctic, atmospheric reanalysis is one of the key tools for understanding rapid climate change at the regional and global scales. The utility of reanalysis datasets based on data assimilation is affected by their accuracy and biases. Therefore, [...] Read more.

In data-sparse regions such as the Arctic, atmospheric reanalysis is one of the key tools for understanding rapid climate change at the regional and global scales. The utility of reanalysis datasets based on data assimilation is affected by their accuracy and biases. Therefore, it is important to evaluate their performance. Here, we conduct inter-comparisons of two temperature variables, namely, the 2-m air temperature (Ta) and the surface temperature (Ts), from the widely used ERA-I and ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) against in situ observations from three international buoy programs (i.e., the International Arctic Buoy Programme (IABP), the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), and the Cold Regions Research and Engineering Laboratory (CRREL)) during 2010–2020 in the Arctic. Overall, the results show that both the ERA-I and ERA5 were well correlated with the buoy observations, with the highest correlation coefficient reaching 0.98. There were generally warm Ta biases for both ERA-I (2.27 ± 3.33 °C) and ERA5 (2.34 ± 3.22 °C) when compared with more than 3000 matching pairs of daily buoy observations. The warm Ta biases of both reanalysis datasets exhibited seasonal variations, reaching the maximum of 3.73 ± 2.84 °C in April and the minimum of 1.36 ± 2.51 °C in September. For Ts, both ERA-I and ERA5 exhibited good consistencies with the buoy observations, but have higher amplitude biases compared with those for Ta, with generally negative biases of −4.79 ± 4.86 °C for ERA-I and −4.11 ± 3.92 °C for ERA5. For both reanalysis datasets, the largest bias of Ts (−11.18 ± 3.08 °C) occurred in December, while the biases were rather small (less than −3 °C) in the warmer months (April to October). The cold Ts biases for ERA-I and ERA5 were probably overestimated due to the location of the surface temperature sensors on the buoys, which may have been affected by snow cover. Both the Ta and Ts biases varied for different buoy programs and different sea ice concentration conditions, yet they exhibited similar trends. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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16 pages, 5538 KiB

Open AccessArticle

Revisiting Trans-Arctic Maritime Navigability in 2011–2016 from the Perspective of Sea Ice Thickness

by Xiangying Zhou, Chao Min, Yijun Yang, Jack C. Landy, Longjiang Mu and Qinghua Yang

Remote Sens. 2021, 13(14), 2766; https://doi.org/10.3390/rs13142766 - 14 Jul 2021

Cited by 14 | Viewed by 3670

Abstract

Arctic navigation has become operational in recent decades with the decline in summer sea ice. To assess the navigability of trans-Arctic passages, combined model and satellite sea ice thickness (CMST) data covering both freezing seasons and melting seasons are integrated with the Arctic [...] Read more.

Arctic navigation has become operational in recent decades with the decline in summer sea ice. To assess the navigability of trans-Arctic passages, combined model and satellite sea ice thickness (CMST) data covering both freezing seasons and melting seasons are integrated with the Arctic Transportation Accessibility Model (ATAM). The trans-Arctic navigation window and transit time are thereby obtained daily from modeled sea ice fields constrained by satellite observations. Our results indicate that the poorest navigability conditions for the maritime Arctic occurred in 2013 and 2014, particularly in the Northwest Passage (NWP) with sea ice blockage. The NWP has generally exhibited less favorable navigation conditions and shorter navigable windows than the Northern Sea Route (NSR). For instance, in 2013, Open Water (OW) vessels that can only safely resist ice with a thickness under 15 cm had navigation windows of 47 days along the NSR (45% shorter than the 2011–2016 mean) and only 13 days along the NWP (80% shorter than the 2011–2016 mean). The longest navigation windows were in 2011 and 2015, with lengths of 103 and 107 days, respectively. The minimum transit time occurred in 2012, when more northward routes were accessible, especially in the Laptev Sea and East Siberian Sea with the sea ice edge retreated. The longest navigation windows for Polar Class 6 (PC6) vessels with a resistance to ice thickness up to 120 cm reached more than 200 days. PC6 vessels cost less transit time and exhibit less fluctuation in their navigation windows compared with OW vessels because of their ice-breaking capability. Finally, we found that restricted navigation along the NSR in 2013 and 2014 was related to the shorter periods of navigable days in the East Siberian Sea and Vilkitskogo Strait, with local blockages of thick ice having a disproportionate impact on the total transit. Shorter than usual navigable windows in the Canadian Arctic Archipelago and Beaufort Sea shortened the windows for entire routes of the NWP in 2013 and 2014. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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15 pages, 6676 KiB

Open AccessArticle

Delay in Arctic Sea Ice Freeze-Up Linked to Early Summer Sea Ice Loss: Evidence from Satellite Observations

by Lei Zheng, Xiao Cheng, Zhuoqi Chen and Qi Liang

Remote Sens. 2021, 13(11), 2162; https://doi.org/10.3390/rs13112162 - 31 May 2021

Cited by 13 | Viewed by 3989

Abstract

The past decades have witnessed a rapid loss of the Arctic sea ice and a significant lengthening of the melt season. The years with the lowest summertime sea ice minimum were found to be accompanied by the latest freeze-up onset on record. Here, [...] Read more.

The past decades have witnessed a rapid loss of the Arctic sea ice and a significant lengthening of the melt season. The years with the lowest summertime sea ice minimum were found to be accompanied by the latest freeze-up onset on record. Here, a synthetic approach is taken to examine the connections between sea ice melt timing and summer sea ice evolution from the remote sensing perspective. A 40-year (1979–2018) satellite-based time-series analysis shows that the date of autumn sea ice freeze-up is significantly correlated with the sea ice extent in early summer (r = −0.90, p < 0.01), while the spring melt onset is not a promising predictor of summer sea ice evolution. The delay in Arctic sea ice freeze-up (0.61 days year⁻¹) in the Arctic was accompanied by a decline in surface albedo (absolute change of −0.13% year⁻¹), an increase in net short-wave radiation (0.21 W m⁻² year⁻¹), and an increase in skin temperature (0.08 °C year⁻¹) in summer. Sea ice loss would be the key reason for the delay in autumn freeze-up, especially in the Laptev, East-Siberian, Chukchi and Beaufort Seas, where sea ice has significantly declined throughout the summer, and strong correlations were found between the freeze-up onset and the solar radiation budget since early summer. This study highlights a connection between the summer sea ice melting and the autumn refreezing process through the ice-albedo feedback based on multisource satellite-based observations. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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31 pages, 7201 KiB

Open AccessArticle

Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI

by Lele Li, Haihua Chen and Lei Guan

Remote Sens. 2021, 13(8), 1457; https://doi.org/10.3390/rs13081457 - 9 Apr 2021

Cited by 14 | Viewed by 3744

Abstract

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, [...] Read more.

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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22 pages, 65924 KiB

Open AccessArticle

Deep Learning Based Sea Ice Classification with Gaofen-3 Fully Polarimetric SAR Data

by Tianyu Zhang, Ying Yang, Mohammed Shokr, Chunlei Mi, Xiao-Ming Li, Xiao Cheng and Fengming Hui

Remote Sens. 2021, 13(8), 1452; https://doi.org/10.3390/rs13081452 - 9 Apr 2021

Cited by 43 | Viewed by 4370

Abstract

In this paper, the performance of C-band synthetic aperture radar (SAR) Gaofen-3 (GF-3) quad-polarization Stripmap (QPS) data is assessed for classifying late spring and summer sea ice types. The investigation is based on 18 scenes of GF-3 QPS data acquired in the Arctic [...] Read more.

In this paper, the performance of C-band synthetic aperture radar (SAR) Gaofen-3 (GF-3) quad-polarization Stripmap (QPS) data is assessed for classifying late spring and summer sea ice types. The investigation is based on 18 scenes of GF-3 QPS data acquired in the Arctic Ocean in 2017. In this study, floe ice (FI), brash ice (BI) between floes and open water (OW, ice-free area) were classified based on a mini sea ice residual convolutional network, which we call MSI-ResNet. While investigating the optimal patch size for MSI-ResNet, we found that, as the patch size continues to grow, the classification accuracy first increases and then decreases. A patch size of 31 × 31 was found to achieve the best performance. The performance of classification using different polarization combinations from the QPS data was also assessed. The vertical-vertical (VV) polarization input overestimates the FI category while incorrectly identifying most of the BI as FI. The VH polarization produces a synchronous improvement in FI, BI, and OW discrimination, with a higher overall accuracy and kappa coefficient (91.09% and 0.85, respectively) than the VV polarization (83.37% and 0.70, respectively). The combination of VV and vertical-horizontal (VH) polarizations presents a modest precision improvement for BI and OW together with a slight overestimation for FI. With VV, VH, and horizontal-horizontal (HH) polarization data as the inputs, the user’s accuracy improves to 95.12%, 93.42%, and 95.17% for FI, BI, and OW, respectively. The accuracy was assessed against visual interpretation of the sea ice classes in the images using a stratified sampling method. The application of the MSI-ResNet method to data covering the Beaufort Sea and the north of the Severnaya Zemlya archipelago was found to achieve a high overall accuracy (kappa) of 94.62% (±0.92) and 94.23% (±0.90), respectively. This is similar to the classification accuracy obtained in the Fram Strait. From the results of this study, it is shown that the MSI-ResNet method performs better than the classical support vector machine (SVM) classifier for sea ice discrimination. The GF-3 QPS mode data also show more details in discriminating scattered sea ice floes than the coincident Sentinel-1A Extra Wide (EW) swath mode data. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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20 pages, 7820 KiB

Open AccessEditor’s ChoiceArticle

Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission

by Christoph Herbert, Joan Francesc Munoz-Martin, David Llaveria, Miriam Pablos and Adriano Camps

Remote Sens. 2021, 13(7), 1366; https://doi.org/10.3390/rs13071366 - 2 Apr 2021

Cited by 13 | Viewed by 4334

Abstract

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (T

_{B}

) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject [...] Read more.

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (T

_{B}

) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat—designed to provide T

_{B}

data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 T

_{B}

and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models—covering thin ice up to 0.6 m and full-range thickness—were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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17 pages, 17272 KiB

Open AccessArticle

Effectively Extracting Iceberg Freeboard Using Bi-Temporal Landsat-8 Panchromatic Image Shadows

by Zhenfu Guan, Xiao Cheng, Yan Liu, Teng Li, Baogang Zhang and Zhitong Yu

Remote Sens. 2021, 13(3), 430; https://doi.org/10.3390/rs13030430 - 26 Jan 2021

Cited by 7 | Viewed by 3083

Abstract

The freshwater flux from icebergs into the Southern Ocean plays an important role in the global climate through its impact on the deep-water formation. Large uncertainties exist in the ice volume transported by Southern Ocean icebergs due to the sparse spatial and temporal [...] Read more.

The freshwater flux from icebergs into the Southern Ocean plays an important role in the global climate through its impact on the deep-water formation. Large uncertainties exist in the ice volume transported by Southern Ocean icebergs due to the sparse spatial and temporal coverage of observations, especially observations of ice thickness. The iceberg freeboard is a critical geometric parameter for measuring the thickness of an iceberg and then estimating its volume. This study developed a new, highly efficient shadow-height method to precisely measure the freeboard of various icebergs surrounded by sea ice using Landsat-8 Operational Land Imager 15-m bi-temporal panchromatic image shadows at low-solar-elevation angles. We evaluated and validated shadow length precision according to bi-temporal measurements and comparison with the measurements from the unmanned aerial vehicle. We determined freeboard precision according to shadow length precision and solar elevation angle. In our case study area, 4832 available freeboard measuring points with shadow length precision better than 2 pixels covered 376 icebergs with sizes ranging from 0.002 to 0.7 km² and with freeboard ranging from 2.3 to 83.4 m. At the solar elevation angles of 5.2°, the freeboard precision of 64.1% data could reach 1 m and 86.9% could reach 2 m. Our proposed method effectively filled in the data gap of existing freeboard measurement methods. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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22 pages, 6355 KiB

Open AccessArticle

MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery

by Shiyi Chen, Mohammed Shokr, Xinqing Li, Yufang Ye, Zhilun Zhang, Fengming Hui and Xiao Cheng

Remote Sens. 2020, 12(19), 3221; https://doi.org/10.3390/rs12193221 - 3 Oct 2020

Cited by 17 | Viewed by 3591

Abstract

The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change [...] Read more.

The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change studies. Satellite-based Synthetic Aperture Radar (SAR) systems have shown advantages of retrieving this information. Operational ice mapping relies on visual analysis of SAR images along with ancillary data. However, these maps estimate ice types and concentrations within large-size polygons of a few tens or hundreds of kilometers, which are subjectively identified and selected by analysts. This study aims at developing an automated algorithm to identify individual MYI floes from SAR images then classify the rest of the image as FYI and other ice types. The algorithm identifies the MYI floes using extended-maximum operator, morphological image processing, and a few geometrical features. Classifying the rest of the image uses texture and neural network model. The input data is a set of Sentinel-1 A/B Extended Wide (EW) mode images, acquired between September and March 2016–2019. Although the overall accuracy (for all type classification) from the new method scored 93.26%, the accuracy from using the texture classifier only was 75.81%. The kappa coefficient from the former was higher than the latter by 0.25. Compared with the operational ice charts from the Canadian Ice Service, ice type maps from the new method show better distribution of MYI at the fine scale of individual floes. Comparison against MYI concentration from two automated algorithms that use a combination of coarse-resolution passive and active microwave data also confirms the advantage of resolving MYI floes from the fine-resolution SAR. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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28 pages, 3405 KiB

Open AccessArticle

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

by Valentin Ludwig, Gunnar Spreen and Leif Toudal Pedersen

Remote Sens. 2020, 12(19), 3183; https://doi.org/10.3390/rs12193183 - 29 Sep 2020

Cited by 31 | Viewed by 6001

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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29 pages, 20974 KiB

Open AccessArticle

Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network

by Yifan Ding, Xiao Cheng, Jiping Liu, Fengming Hui, Zhenzhan Wang and Shengzhe Chen

Remote Sens. 2020, 12(17), 2746; https://doi.org/10.3390/rs12172746 - 25 Aug 2020

Cited by 12 | Viewed by 4798

Abstract

The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all [...] Read more.

The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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15 pages, 9224 KiB

Open AccessArticle

Efficient Location and Extraction of the Iceberg Calved Areas of the Antarctic Ice Shelves

by Mengzhen Qi, Yan Liu, Yijing Lin, Fengming Hui, Teng Li and Xiao Cheng

Remote Sens. 2020, 12(16), 2658; https://doi.org/10.3390/rs12162658 - 18 Aug 2020

Cited by 7 | Viewed by 3291

Abstract

Continuous, rapid, and precise monitoring of calving events contributes to an in-depth understanding of calving mechanisms, which have the potential to cause significant mass loss from the Antarctic ice sheet. The difficulties in the precise monitoring of iceberg calving lie with the coexistence [...] Read more.

Continuous, rapid, and precise monitoring of calving events contributes to an in-depth understanding of calving mechanisms, which have the potential to cause significant mass loss from the Antarctic ice sheet. The difficulties in the precise monitoring of iceberg calving lie with the coexistence of ice shelf advances and calving. The manual location of iceberg calving is time-consuming and painstaking, while achieving precise extraction has mostly relied on the surface textural characteristics of the ice shelves and the quality of the images. Here, we propose a new and efficient method of separating the expansion and calving processes of ice shelves. We visualized the extension process by simulating a new coastline, based on the ice velocity, and detected the calved area using the simulated coastline and single-temporal post-calving images. We extensively tested the validity of this method by extracting four annual calving datasets (from August 2015 to August 2019) from the Sentinel-1 synthetic aperture radar mosaic of the Antarctic coastline. A total of 2032 annual Antarctic calving events were detected, with areas ranging from 0.05 km² to 6141.0 km², occurring on almost every Antarctic ice shelf. The extraction accuracy of the calved area depends on the positioning accuracy of the simulated coastline and the spatial resolution of the images. The positioning error of the simulated coastline is less than one pixel, and the determined minimum valid extraction area is 0.05 km², when based on 75 m resolution images. Our method effectively avoids repetition and omission errors during the calved area extraction process. Furthermore, its efficiency is not affected by the surface textural characteristics of the calving fronts and the various changes in the frontal edge velocity, which makes it fully applicable to the rapid and accurate extraction of different calving types. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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14 pages, 3975 KiB

Open AccessArticle

Assessing the Potential of Enhanced Resolution Gridded Passive Microwave Brightness Temperatures for Retrieval of Sea Ice Parameters

by Walter N. Meier and J. Scott Stewart

Remote Sens. 2020, 12(16), 2552; https://doi.org/10.3390/rs12162552 - 8 Aug 2020

Cited by 4 | Viewed by 4156

Abstract

A new enhanced resolution gridded passive microwave brightness temperature (TB) product is used to estimate sea ice concentration and motion. The effective resolution of the TBs is found to be roughly twice that of the standard 25 km resolution, though the gridded resolution [...] Read more.

A new enhanced resolution gridded passive microwave brightness temperature (TB) product is used to estimate sea ice concentration and motion. The effective resolution of the TBs is found to be roughly twice that of the standard 25 km resolution, though the gridded resolution of the distributed product is higher. Enhanced resolution sea ice concentrations from the Bootstrap algorithm show more detail in the sea ice, including relatively small open water regions within the ice pack. Sea ice motion estimates from the enhanced resolution TBs using a maximum cross-correlation method show a smoother motion circulation pattern; in comparison to buoys, RMS errors are 15–20% lower than motion estimates from the standard resolution fields and the magnitude of the bias is smaller as well. The enhanced resolution product includes other potentially beneficial characteristics, including twice-daily grids based on local time of day and a complete timeseries of data from nearly all multi-channel passive microwave radiometers since 1978. These enhanced resolution TBs are potential new source for long-term records of sea ice concentration, motion, age, melt, as well as salinity and ocean-atmosphere fluxes. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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19 pages, 16219 KiB

Open AccessArticle

Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning

by Ryan Kruk, M. Christopher Fuller, Alexander S. Komarov, Dustin Isleifson and Ian Jeffrey

Remote Sens. 2020, 12(15), 2486; https://doi.org/10.3390/rs12152486 - 3 Aug 2020

Cited by 19 | Viewed by 4682

Abstract

Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce [...] Read more.

Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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16 pages, 3014 KiB

Open AccessArticle

Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals

by Walter N. Meier and J. Scott Stewart

Remote Sens. 2020, 12(14), 2197; https://doi.org/10.3390/rs12142197 - 9 Jul 2020

Cited by 4 | Viewed by 3214

Abstract

Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over [...] Read more.

Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over their years of operation, resulting in changing relative observing times that could cause biases in TB estimates and near-real-time sea ice concentrations derived from the NASA Team algorithm that are produced at the National Snow and Ice Data Center. Comparisons of TB histograms and concentrations show that there are small mean differences between sensors, but variability within an individual sensor is much greater. There are some indications of small changes due to orbital drift, but these are not consistent across different frequencies. Further, the overall effect of the drift, while not definitive, is small compared to the intra- and interannual variability in individual sensors. These results suggest that, for near-real-time use, the differences in the sensors are not critical. However, for long-term time series, even the small biases should be corrected for. The strong day-to-day, seasonal, and interannual variability in TB distributions indicate that time-varying algorithm coefficients in the NASA team algorithm would lead to improved, more consistent sea ice concentration estimates. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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20 pages, 7971 KiB

Open AccessArticle

Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks

by Hugo Boulze, Anton Korosov and Julien Brajard

Remote Sens. 2020, 12(13), 2165; https://doi.org/10.3390/rs12132165 - 7 Jul 2020

Cited by 99 | Viewed by 10959

Abstract

A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning [...] Read more.

A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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17 pages, 61578 KiB

Open AccessArticle

A New Algorithm for Daily Sea Ice Lead Identification in the Arctic and Antarctic Winter from Thermal-Infrared Satellite Imagery

by Fabian Reiser, Sascha Willmes and Günther Heinemann

Remote Sens. 2020, 12(12), 1957; https://doi.org/10.3390/rs12121957 - 17 Jun 2020

Cited by 42 | Viewed by 6303

Abstract

The presence of sea ice leads in the sea ice cover represents a key feature in polar regions by controlling the heat exchange between the relatively warm ocean and cold atmosphere due to increased fluxes of turbulent sensible and latent heat. Sea ice [...] Read more.

The presence of sea ice leads in the sea ice cover represents a key feature in polar regions by controlling the heat exchange between the relatively warm ocean and cold atmosphere due to increased fluxes of turbulent sensible and latent heat. Sea ice leads contribute to the sea ice production and are sources for the formation of dense water which affects the ocean circulation. Atmospheric and ocean models strongly rely on observational data to describe the respective state of the sea ice since numerical models are not able to produce sea ice leads explicitly. For the Arctic, some lead datasets are available, but for the Antarctic, no such data yet exist. Our study presents a new algorithm with which leads are automatically identified in satellite thermal infrared images. A variety of lead metrics is used to distinguish between true leads and detection artefacts with the use of fuzzy logic. We evaluate the outputs and provide pixel-wise uncertainties. Our data yield daily sea ice lead maps at a resolution of 1 km² for the winter months November– April 2002/03–2018/19 (Arctic) and April–September 2003–2019 (Antarctic), respectively. The long-term average of the lead frequency distributions show distinct features related to bathymetric structures in both hemispheres. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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15 pages, 5254 KiB

Open AccessArticle

Is Radar Phase Information Useful for Sea Ice Detection in the Marginal Ice Zone?

by Fuhong Ding, Hui Shen, William Perrie and Yijun He

Remote Sens. 2020, 12(11), 1847; https://doi.org/10.3390/rs12111847 - 8 Jun 2020

Cited by 6 | Viewed by 3323

Abstract

With continuing sea ice reductions in the Arctic, dynamic physical and ecological processes have more active roles compared to the ice-locked, isolated Arctic Ocean of previous decades. To better understand these changes, observations of high-resolution sea ice conditions are needed. Remote sensing is [...] Read more.

With continuing sea ice reductions in the Arctic, dynamic physical and ecological processes have more active roles compared to the ice-locked, isolated Arctic Ocean of previous decades. To better understand these changes, observations of high-resolution sea ice conditions are needed. Remote sensing is a useful tool for observations in the harsh Arctic environment. For unsupervised ice detection, we demonstrate the promising value of radar phase difference from polarimetric radar measurements in this study, based on full polarimetric complex RADARSAT-2 SAR images in the marginal ice zone. It is demonstrated that the phase difference from co-polarized and cross-polarized synthetic aperture radar (SAR) images show promising capability for high resolution sea ice discrimination from open water. In particular, the phase difference shows superior potential for the detection of frazil ice compared to the traditional methodology based on the radar intensity ratio. The relationship between phase difference and radar incidence angle is also analyzed, as well as the potential influence of high sea state. The new methodology provides an additional tool for ice detection. In order to make the best use of this tool, directions for further studies are discussed for operational ice detection and possible ice classification. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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18 pages, 3131 KiB

Open AccessArticle

Long-Term Analysis of Sea Ice Drift in the Western Ross Sea, Antarctica, at High and Low Spatial Resolution

by Usama Farooq, Wolfgang Rack, Adrian McDonald and Stephen Howell

Remote Sens. 2020, 12(9), 1402; https://doi.org/10.3390/rs12091402 - 29 Apr 2020

Cited by 12 | Viewed by 4720

Abstract

The Ross Sea region, including three main polynya areas in McMurdo Sound, Terra Nova Bay, and in front of the Ross Ice Shelf, has experienced a significant increase in sea ice extent in the first four decades of satellite observations. Here, we use [...] Read more.

The Ross Sea region, including three main polynya areas in McMurdo Sound, Terra Nova Bay, and in front of the Ross Ice Shelf, has experienced a significant increase in sea ice extent in the first four decades of satellite observations. Here, we use Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) to estimate 894 high-resolution sea ice motion fields of the Western Ross Sea in order to explore ice-atmosphere interactions based on sequential high-resolution Advanced Synthetic Aperture Radar (ASAR) images from the Envisat satellite acquired between 2002–2012. Validation of output motion vectors with manually drawn vectors for 24 image pairs show Pearson correlation coefficients of 0.92 ± 0.09 with a mean deviation in direction of −3.17 ± 6.48 degrees. The high-resolution vectors were also validated against the Environment and Climate Change Canada sea ice motion tracking algorithm, resulting in correlation coefficients of 0.84 ± 0.20 and the mean deviation in the direction of −0.04 ± 17.39 degrees. A total of 480 one-day separated velocity vector fields have been compared to an available NSIDC low-resolution sea ice motion vector product, showing much lower correlations and high directional differences. The high-resolution product is able to better identify short-term and spatial variations, whereas the low-resolution product underestimates the actual sea ice velocities by 47% in this important near-coastal region. The large-scale pattern of sea ice drift over the full time period is similar in both products. Improved image coverage is still desired to capture drift variations shorter than 24 h. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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18 pages, 8067 KiB

Open AccessArticle

Evaluating Landfast Sea Ice Ridging near UtqiaġVik Alaska Using TanDEM-X Interferometry

by Marjan Marbouti, Leif E. B. Eriksson, Dyre Oliver Dammann, Denis Demchev, Joshua Jones, Anders Berg and Oleg Antropov

Remote Sens. 2020, 12(8), 1247; https://doi.org/10.3390/rs12081247 - 15 Apr 2020

Cited by 7 | Viewed by 4168

Abstract

Seasonal landfast sea ice stretches along most Arctic coastlines and serves as a platform for community travel and subsistence, industry operations, and as a habitat for marine mammals. Landfast ice can feature smooth ice and areas of m-scale roughness in the form of [...] Read more.

Seasonal landfast sea ice stretches along most Arctic coastlines and serves as a platform for community travel and subsistence, industry operations, and as a habitat for marine mammals. Landfast ice can feature smooth ice and areas of m-scale roughness in the form of pressure ridges. Such ridges can significantly hamper trafficability, but if grounded can also serve to stabilize the shoreward ice. We investigate the use of synthetic aperture radar interferometry (InSAR) to assess the formation and movement of ridges in the landfast sea ice near Utqiaġvik, Alaska. The evaluation is based on the InSAR-derived surface elevation change between two TanDEM-X bistatic image pairs acquired during January 2012. We compare the results with backscatter intensity, coastal radar data, and SAR-derived ice drift and evaluate the utility of this approach and its relevance for evaluation of ridge properties, as well as landfast sea ice evolution, dynamics, and stability. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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20 pages, 5178 KiB

Open AccessEditor’s ChoiceArticle

Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

by Marko Mäkynen, Jari Haapala, Giuseppe Aulicino, Beena Balan-Sarojini, Magdalena Balmaseda, Alexandru Gegiuc, Fanny Girard-Ardhuin, Stefan Hendricks, Georg Heygster, Larysa Istomina, Lars Kaleschke, Juha Karvonen, Thomas Krumpen, Mikko Lensu, Michael Mayer, Flavio Parmiggiani, Robert Ricker, Eero Rinne, Amelie Schmitt, Markku Similä, Steffen Tietsche, Rasmus Tonboe, Peter Wadhams, Mai Winstrup and Hao Zuo Show full author list Hide full author list

Remote Sens. 2020, 12(7), 1214; https://doi.org/10.3390/rs12071214 - 10 Apr 2020

Cited by 17 | Viewed by 7553

Abstract

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from [...] Read more.

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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17 pages, 2020 KiB

Open AccessArticle

Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections

by Jessica L. Matthews, Ge Peng, Walter N. Meier and Otis Brown

Remote Sens. 2020, 12(5), 807; https://doi.org/10.3390/rs12050807 - 3 Mar 2020

Cited by 8 | Viewed by 5729

Abstract

Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice [...] Read more.

Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and examine the sensitivity of sea ice extents and their decadal trends to sea ice concentration threshold values. Threshold choice can affect the timing of annual SIE minimums: a threshold choice as low as 30% can change the timing to August instead of September. Threshold choice impacts the value of annual SIE minimums: in particular, changing the threshold from 15% to 35% can change the annual SIE by more than 10% in magnitude. Monthly SIE data distributions are seasonally dependent. Although little impact was seen for threshold choice on data distributions during annual minimum times (August and September), there is a strong impact in May. Threshold choices were not found to impact the choice of optimal statistical models characterizing annual minimum SIE time series. However, the first ice-free Arctic summer year (FIASY) estimates are impacted; higher threshold values produce earlier FIASY estimates and, more notably, FIASY estimates amongst all considered models are more consistent. This analysis suggests that some of the threshold choice impacts to SIE trends may actually be the result of biased data due to surface melt. Given that the rapid Arctic sea ice depletion appears to have statistically changed SIE characteristics, particularly in the summer months, a more extensive investigation to verify surface melt impacts on this data set is warranted. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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20 pages, 6168 KiB

Open AccessArticle

Spatial and Temporal Variations in the Extent and Thickness of Arctic Landfast Ice

by Zixuan Li, Jiechen Zhao, Jie Su, Chunhua Li, Bin Cheng, Fengming Hui, Qinghua Yang and Lijuan Shi

Remote Sens. 2020, 12(1), 64; https://doi.org/10.3390/rs12010064 - 23 Dec 2019

Cited by 31 | Viewed by 5387

Abstract

Analyses of landfast ice in Arctic coastal areas provide a comprehensive understanding of the variations in Arctic sea ice and generate data for studies on the utilization of the Arctic passages. Based on our analysis, Arctic landfast ice mainly appears in January–June and [...] Read more.

Analyses of landfast ice in Arctic coastal areas provide a comprehensive understanding of the variations in Arctic sea ice and generate data for studies on the utilization of the Arctic passages. Based on our analysis, Arctic landfast ice mainly appears in January–June and is distributed within the narrow straits of the Canadian Archipelago (nearly 40%), the coastal areas of the East Siberian Sea, the Laptev Sea, and the Kara Sea. From 1976–2018, the landfast ice extent gradually decreased at an average rate of −1.1 ± 0.5 × 10⁴ km²/yr (10.5% per decade), while the rate of decrease for entire Arctic sea ice was −6.0 ± 2.4 × 10⁴ km²/yr (5.2% per decade). The annual maximum extent reached 2.3 × 10⁶ km² in the early 1980s, and by 2018, the maximum extent decreased by 0.6 × 10⁶ km², which is an area approximately equivalent the Laptev Sea. The mean duration of Arctic landfast ice is 44 weeks, which has gradually been reduced at a rate of −0.06 ± 0.03 weeks/yr. Regional landfast ice extent decreases in 16 of the 17 subregions except for the Bering Sea, making it the only subregion where both the extent and duration increases. The maximum mean landfast ice thickness appears in the northern Canadian Archipelago (>2.5 m), with the highest increasing trend (0.1 m/yr). In the Northeast Passage, the mean landfast ice thickness is 1.57 m, with a slight decreasing trend of −1.2 cm/yr, which is smaller than that for entire Arctic sea ice (−5.1 cm/yr). The smaller decreasing trend in the landfast ice extent and thickness suggests that the well-known Arctic sea ice decline largely occurred in the pack ice zone, while the larger relative extent loss indicates a faster ice free future in the landfast ice zone. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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13 pages, 3423 KiB

Open AccessArticle

The Case for a Single Channel Composite Arctic Sea Surface Temperature Algorithm

by R.F. Vincent

Remote Sens. 2019, 11(20), 2393; https://doi.org/10.3390/rs11202393 - 16 Oct 2019

Cited by 7 | Viewed by 3019

Abstract

Surface temperatures derived from satellite thermal infrared (TIR) data are critical inputs for assessing climate change in polar environments. Sea and ice surface temperature (SST, IST) are commonly determined with split window algorithms that use the brightness temperature from the 11 μm channel [...] Read more.

Surface temperatures derived from satellite thermal infrared (TIR) data are critical inputs for assessing climate change in polar environments. Sea and ice surface temperature (SST, IST) are commonly determined with split window algorithms that use the brightness temperature from the 11 μm channel (BT₁₁) as the main estimator and the difference between BT₁₁ and the 12 μm channel (BTD_11–12) to correct for atmospheric water vapor absorption. An issue with this paradigm in the Arctic maritime environment is the occurrence of high BTD_11–12 that is not indicative of atmospheric absorption of BT₁₁ energy. The Composite Arctic Sea Surface Temperature Algorithm (CASSTA) considers three regimes based on BT₁₁ pixel value: seawater, ice, and marginal ice zones. A single channel (BT₁₁) estimator is used for SST and a split window algorithm for IST. Marginal ice zone temperature is determined with a weighted average between the SST and IST. This study replaces the CASSTA split window IST with a single channel (BT₁₁) estimator to reduce errors associated with BTD_11–12 in the split window algorithm. The single channel IST returned improved results in the CASSTA dataset with a mean average error for ice and marginal ice zones of 0.142 K and 0.128 K, respectively. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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13 pages, 10195 KiB

Open AccessTechnical Note

Arctic Sea Ice Freeboard Retrieval from Envisat Altimetry Data

by Shengkai Zhang, Yue Xuan, Jiaxing Li, Tong Geng, Xiao Li and Feng Xiao

Remote Sens. 2021, 13(8), 1414; https://doi.org/10.3390/rs13081414 - 7 Apr 2021

Cited by 9 | Viewed by 2366

Abstract

Arctic sea ice variations are sensitive to Arctic environmental changes and global changes. Freeboard and thickness are two important parameters in sea ice change research. Satellite altimetry can provide long-time and large-scale sea ice monitoring. We estimated the Arctic sea ice freeboard and [...] Read more.

Arctic sea ice variations are sensitive to Arctic environmental changes and global changes. Freeboard and thickness are two important parameters in sea ice change research. Satellite altimetry can provide long-time and large-scale sea ice monitoring. We estimated the Arctic sea ice freeboard and its variations for the period from 2002 to 2012 from Envisat satellite altimetry data. To remove geoid undulations, we reprocessed the Envisat data using a newly developed mean sea surface (MSS) model, named DTU18. Residuals in the static geoid were removed by using the moving average technique. We then determined the local sea surface height and sea ice freeboard from the Envisat elevation profiles. We validated our freeboard estimates using two radar freeboard products from the European Space Agency (ESA) Climate Change Initiative (CCI) and the Alfred Wegener Institute (AWI), as well as the Operation IceBridge (OIB) sea ice freeboard product. The overall differences between our estimates and the CCI and AWI data were 0.11 ± 0.14 m and 0.12 ± 0.14 m, respectively. Our estimates show good agreement with the three products for areas of freeboard larger than 0.2 m and smaller than 0.3 m. For areas of freeboard larger than 0.3 m, our estimates correlate better with OIB freeboard than with CCI and AWI. The variations in the Arctic sea ice thickness are discussed. The ice freeboard reached its minimum in 2008 during the research period. Sharp decreases were found in the winters of 2005 and 2007. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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