<p>Arctic sea ice type (SITY) variation is a sensitive indicator of climate change.... more <p>Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study analyzed eight daily SITY products from five retrieval approaches covering the winters of 1999–2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA) and evaluated with five Synthetic Aperture Radar images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32×10<sup>6</sup> km<sup>2</sup> to 0.49×10<sup>6</sup> km<sup>2</sup> . Among all, KNMI-SITY and Zhang-SITY in the QSCAT period (2002-2009) agree best with NSIDC-SIA and perform the best, with smallest bias of -0.001×10<sup>6</sup> km<sup>2</sup> in FYI extent and -0.02×10<sup>6</sup> km<sup>2</sup> in MYI extent, respectively. In the ASCAT period (2007-2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases however exhibits large temporal variabilities as OSISAF-SITY. Factors that could impact performances of the SITY products are analyzed and summarized: (1) Ku-band scatterometer generally performs better than C-band scatterometer on SITY discrimination, while the latter sometimes identifies first-year ice (FYI) more accurately, especially when surface scattering dominants the backscatter signature. (2) Simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation of characteristic training dataset should be well accounted in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.</p>
The current climate change episode has impacted sea ice in the 2 polar regions differently. In th... more The current climate change episode has impacted sea ice in the 2 polar regions differently. In the Arctic, remarkable sea ice extent and thickness declines have been observed with a stunning depletion rate of old ice. No similar changes have been observed in the Antarctic. In this paper, the question posed in the title is addressed by reviewing findings retrieved from previous publications. The paper starts by identifying key geographic and climatic features and sea ice characteristics in the 2 polar regions and summarizing relevant recent records. It then proceeds by investigating interactions between sea ice and environmental factors, including atmospheric, oceanic, and dynamic aspects in each region, as well as the increasing number of icebergs in Antarctica. It is concluded that peculiarities of each polar region render the response to climate change differently. Researchers should not apply scenarios regarding the impacts of climate change on Arctic sea ice (i.e., retreat) to Antarctic sea ice. Instead of asking why Antarctic sea ice has not responded to climate change in the same way as Arctic ice, a more reasonable question could be why Arctic ice changes are yielding an annual cycle that resembles that of Antarctic ice. Under current global warming conditions, old ice entrapment within the Arctic basin is relaxed. This could result in Arctic sea ice becoming predominantly seasonal during winter and almost completely melted during summer, which is the current state of Antarctic sea ice.
Antarctica plays a key role in global energy balance and sea level change. It has been convention... more Antarctica plays a key role in global energy balance and sea level change. It has been conventionally viewed as a whole ice body with high albedo in General Circulation Models or Regional Climate Models and the differences of land cover has usually been overlooked. Land cover in Antarctica is one of the most important drivers of changes in the Earth system. Detailed land cover information over the Antarctic region is necessary as spatial resolution improves in land process models. However, there is a lack of complete Antarctic land cover dataset derived from a consistent data source. To fill this data gap, we have produced a database named Antarctic Land Cover Database for the Year 2000 (AntarcticaLC2000) using Landsat Enhanced Thematic Mapper Plus (ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer (MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100000-scale. Three land cover types were included in this map, separately, ice-free rocks, blue ice, and snow/firn. This classification legend was determined based on a review of the land cover systems in Antarctica (LCCSA) and an analysis of different land surface types and the potential of satellite data. Image classification was conducted through a combined usage of computer-aided and manual interpretation methods. A total of 4067 validation sample units were collected through visual interpretation in a stratified random sampling manner. An overall accuracy of 92.3% and the Kappa coefficient of 0.836 were achieved. Results show that the areas and percentages of ice-free rocks, blue ice, and snow/firn are 73268.81 km 2 (0.537%), 225937.26 km 2 (1.656%), and 13345460.41 km 2 (97.807%), respectively. The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality. These indicate that AntarcticaLC2000, the new land cover dataset for Antarctica entirely derived from satellite data, is a reliable product for a broad spectrum of applications.
A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection sch... more A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection scheme is presented. The advection scheme accounts for the observed divergence or convergence and freezing or melting of sea ice and predicts consequent generation or loss of new ice. The algorithm uses daily gridded sea ice drift and sea ice concentration products from the Ocean and Sea Ice Satellite Application Facility. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel of the output product or, in other words, to provide a frequency distribution of the ice age allowing to apply mean, median, weighted average or other statistical measures. Comparison with the National Snow and Ice Data Center SIA product revealed several improvements of the new SIA maps and time series. First, the application of the Eulerian scheme provides smooth distribution of the ice age parameters and prevents product undersampling which may occur when a Lagrangian tracking approach is used. Second, utilization of the new sea ice drift product void of artifacts from EUMETSAT OSI SAF resulted in more accurate and reliable spatial distribution of ice age fractions. Third, constraining SIA computations by the observed sea ice concentration expectedly led to considerable reduction of multi-year ice (MYI) fractions. MYI concentration is computed as a sum of all MYI fractions and compares well to the MYI products based on passive and active microwave and SAR products.
<p>Arctic sea ice type (SITY) variation is a sensitive indicator of climate change.... more <p>Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study analyzed eight daily SITY products from five retrieval approaches covering the winters of 1999–2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA) and evaluated with five Synthetic Aperture Radar images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32×10<sup>6</sup> km<sup>2</sup> to 0.49×10<sup>6</sup> km<sup>2</sup> . Among all, KNMI-SITY and Zhang-SITY in the QSCAT period (2002-2009) agree best with NSIDC-SIA and perform the best, with smallest bias of -0.001×10<sup>6</sup> km<sup>2</sup> in FYI extent and -0.02×10<sup>6</sup> km<sup>2</sup> in MYI extent, respectively. In the ASCAT period (2007-2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases however exhibits large temporal variabilities as OSISAF-SITY. Factors that could impact performances of the SITY products are analyzed and summarized: (1) Ku-band scatterometer generally performs better than C-band scatterometer on SITY discrimination, while the latter sometimes identifies first-year ice (FYI) more accurately, especially when surface scattering dominants the backscatter signature. (2) Simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation of characteristic training dataset should be well accounted in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.</p>
Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting ... more Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting triggered by global warming, in turn partly due to the possible acceleration of ice export from the Arctic Ocean to southern latitudes through identifiable gates. In this study, MYI and total sea ice areal flux through six Arctic gateways over the winters (October–April) of 2002–2021 were estimated using daily sea ice motion and MYI/total sea ice concentration data. Inconsistencies caused by different data sources were considered for the estimate of MYI flux. Results showed that, there is a slight declining trend in the Arctic MYI areal flux over the past two decades, which is attributable to the decrease in MYI concentration. Overall speaking, MYI flux through Fram Strait accounts for ~87% of the Arctic MYI outflow, with an average of ~325.92 × 103 km2 for the winters of 2002–2021. The monthly MYI areal flux through Fram Strait is characterized with a peak in March (~55.56 × 103 km2) an...
The Greenland high (GL-high) coincides with a local center of action of the summer North Atlantic... more The Greenland high (GL-high) coincides with a local center of action of the summer North Atlantic Oscillation and is known to have significant influence on Greenland ice sheet melting and summer Arctic sea ice. However, the mechanism behind the influence on regional Arctic sea ice is not yet clear. In this study, using reanalysis datasets and satellite observations, the influence of the GL-high in early summer on Arctic sea ice variability, and the mechanism behind it, are investigated. In response to an intensified GL-high, sea ice over the Beaufort Sea shows significant decline in both concentration and thickness from June through September. This decline in sea ice is primarily due to thermodynamic and mechanical redistribution processes. Firstly, the intensified GL-high increases subsidence over the Canadian Basin, leading to an increase in surface air temperature by adiabatic heating, and a substantial decrease in cloud cover and thus increased downward shortwave radiation. Seco...
The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) ... 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. Classifyi...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
The aim of this article was to investigate the potential of polarimetric decomposition of Chinese... more The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14-18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°-46°) were superior to low incidence angles (21°-27°) due to the overall higher accuracies.
The current climate change episode has impacted sea ice in the 2 polar regions differently. In th... more The current climate change episode has impacted sea ice in the 2 polar regions differently. In the Arctic, remarkable sea ice extent and thickness declines have been observed with a stunning depletion rate of old ice. No similar changes have been observed in the Antarctic. In this paper, the question posed in the title is addressed by reviewing findings retrieved from previous publications. The paper starts by identifying key geographic and climatic features and sea ice characteristics in the 2 polar regions and summarizing relevant recent records. It then proceeds by investigating interactions between sea ice and environmental factors, including atmospheric, oceanic, and dynamic aspects in each region, as well as the increasing number of icebergs in Antarctica. It is concluded that peculiarities of each polar region render the response to climate change differently. Researchers should not apply scenarios regarding the impacts of climate change on Arctic sea ice (i.e., retreat) to A...
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systema... more Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systematic intercomparison and analysis for SITY products are lacking. This study analysed eight daily SITY products from five retrieval approaches covering the winters of 1999-2019, including purely radiometer-based (C3S-SITY), scatterometerbased (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA-National Snow and Ice Data Center sea ice age) and evaluated with five synthetic aperture radar (SAR) images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from − 1.32 × 10 6 to 0.49 × 10 6 km 2. Among them, KNMI-SITY and Zhang-SITY in the QuikSCAT (QSCAT) period (2002-2009) agree best with NSIDC-SIA and perform the best, with the smallest bias of −0.001 × 10 6 km 2 in first-year ice (FYI) extent and −0.02 × 10 6 km 2 in MYI extent. In the Advanced Scatterometer (ASCAT) period (2007-2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases but exhibits large temporal variabilities like OSISAF-SITY. Factors that could impact performances of the SITY products are analysed and summarized. (1) The Ku-band scatterometer generally performs better than the C-band scatterometer for SITY discrimination, while the latter sometimes identifies FYI more accurately, especially when surface scattering dominates the backscat-ter signature. (2) A simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation in characteristic training datasets should be well accounted for in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution. Published by Copernicus Publications on behalf of the European Geosciences Union. * : the Kappa coefficient and overall accuracy values of C3S-1, C3S-2 and OSISAF-SITY are represented within a lower bound and an upper bound calculated when the Amb class is regarded as FYI and MYI, respectively. : best matches;
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The aim of this article was to investigate the potential of polarimetric decomposition of Chinese... more The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14-18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°-46°) were superior to low incidence angles (21°-27°) due to the overall higher accuracies.
The corrected multiyear ice (MYI) concentration product from the University of Bremen, Institute ... more The corrected multiyear ice (MYI) concentration product from the University of Bremen, Institute of Environmental Physics (IUP), is being retrieved with a two-step algorithm:(1) A constrained optimisation technique that uses different sets of microwave satellite data and the probability distributions of radiometric signatures of different ice types whose concentration (area fraction) is to be retrieved. This is the Environment Canada Ice Concentration Extractor (ECICE) [Shokr et al. 2008, Shokr and Agnew 2013]. The input data used here are microwave radiometer data at 18 and 37 GHz (horizontal and vertical polarisation) from the instrument AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1, as well as microwave scatterometer data from ASCAT on the European satellites MetOp-A and -B.(2) Two correction schemes for the output from ECICE in order to correct for anomalous radiometric and backscatter observations. Such anomalies are caused by snow wetness and m...
Polar sea ice is one of the Earth's climate components that has been significantly affected by th... more Polar sea ice is one of the Earth's climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4% per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10%-15% per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5% per decade until 2014 and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly of MYI. Due to differences in physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently, no method has existed to monitor the distribution and temporal development of Antarctic ice types, particularly MYI throughout the freezing season and on decadal time scales. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The first circumpolar, long-term time series of Antarctic sea ice types; MYI, first-year ice and young ice is being established, so far covering years 2013-2019. Qualitative comparison with synthetic aperture radar data, with charts of the development stage of the sea ice, and with Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval for the first time allows insight into the evolution and dynamics of Antarctic sea ice types. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors. 1 Introduction As an important component of the global climate system, sea ice affects and reflects changes in other climate components, controls energy and gas fluxes between ocean and atmosphere in polar regions, and it is an important part of the polar marine ecosystem. The Arctic sea ice extent has decreased by 4.1% per decade in the past three decades (Parkinson and Cavalieri, 2012b), while the declining rate for multiyear ice (MYI), ice that has survived at least one summer melt, is much higher, 1
IEEE Transactions on Geoscience and Remote Sensing, 2021
As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in t... more As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in the Arctic. Microwave scatterometers in the Ku-band and C-band can provide daily observations of sea ice type. However, their comparative capabilities in mapping ice type have not been thoroughly evaluated. We present a systematic intercomparison of the backscatter signature in VV polarization (<inline-formula> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula>) and the sea ice classification from three scatterometer systems using the same ice classification approach. The systems are the Ku-band quick scatterometer (QSCAT) and the newly launched Chinese rotating fan-beam scatterometer (RFSCAT) and the C-band advanced scatterometer (ASCAT). Three freezing seasons are used, i.e., 2007/08 and 2008/09 for the QSCAT/ASCAT comparison and 2019/20 for the RFSCAT/ASCAT comparison. With reference to ASCAT, <inline-formula> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> bias between QSCAT and RFSCAT results from their different incidence angles. A continuous declining trend of <inline-formula> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> from MYI and FYI is observed during winter, with a greater difference between MYI and FYI in the Ku-band. The MYI and FYI extent derived from QSCAT/RFSCAT is highly consistent with that derived from ASCAT, with a difference less than 7% and 3% for MYI and FYI, respectively. The overall accuracy (OA) is around 77% and 80% for the RFSCAT results and ASCAT results, respectively, compared with Sentinel-1 SAR images. The classification results show high consistency (81%–89%) with ice charts from the Canadian Ice Service. The incorporation of <inline-formula> <tex-math notation="LaTeX">${\mathrm {Tb}}_{36\mathrm {h}}$ </tex-math></inline-formula> from AMSR-E/AMSR2 improves the OA of the classification when using ASCAT or RFSCAT by 7%–11%.
&lt;p&gt;Arctic sea ice type (SITY) variation is a sensitive indicator of climate change.... more &lt;p&gt;Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study analyzed eight daily SITY products from five retrieval approaches covering the winters of 1999&amp;#8211;2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA) and evaluated with five Synthetic Aperture Radar images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32&amp;#215;10&lt;sup&gt;6&lt;/sup&gt; km&lt;sup&gt;2&lt;/sup&gt; to 0.49&amp;#215;10&lt;sup&gt;6&lt;/sup&gt; km&lt;sup&gt;2&lt;/sup&gt; . Among all, KNMI-SITY and Zhang-SITY in the QSCAT period (2002-2009) agree best with NSIDC-SIA and perform the best, with smallest bias of -0.001&amp;#215;10&lt;sup&gt;6&lt;/sup&gt; km&lt;sup&gt;2&lt;/sup&gt; in FYI extent and -0.02&amp;#215;10&lt;sup&gt;6&lt;/sup&gt; km&lt;sup&gt;2&lt;/sup&gt; in MYI extent, respectively. In the ASCAT period (2007-2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases however exhibits large temporal variabilities as OSISAF-SITY. Factors that could impact performances of the SITY products are analyzed and summarized: (1) Ku-band scatterometer generally performs better than C-band scatterometer on SITY discrimination, while the latter sometimes identifies first-year ice (FYI) more accurately, especially when surface scattering dominants the backscatter signature. (2) Simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation of characteristic training dataset should be well accounted in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.&lt;/p&gt;
The current climate change episode has impacted sea ice in the 2 polar regions differently. In th... more The current climate change episode has impacted sea ice in the 2 polar regions differently. In the Arctic, remarkable sea ice extent and thickness declines have been observed with a stunning depletion rate of old ice. No similar changes have been observed in the Antarctic. In this paper, the question posed in the title is addressed by reviewing findings retrieved from previous publications. The paper starts by identifying key geographic and climatic features and sea ice characteristics in the 2 polar regions and summarizing relevant recent records. It then proceeds by investigating interactions between sea ice and environmental factors, including atmospheric, oceanic, and dynamic aspects in each region, as well as the increasing number of icebergs in Antarctica. It is concluded that peculiarities of each polar region render the response to climate change differently. Researchers should not apply scenarios regarding the impacts of climate change on Arctic sea ice (i.e., retreat) to Antarctic sea ice. Instead of asking why Antarctic sea ice has not responded to climate change in the same way as Arctic ice, a more reasonable question could be why Arctic ice changes are yielding an annual cycle that resembles that of Antarctic ice. Under current global warming conditions, old ice entrapment within the Arctic basin is relaxed. This could result in Arctic sea ice becoming predominantly seasonal during winter and almost completely melted during summer, which is the current state of Antarctic sea ice.
Antarctica plays a key role in global energy balance and sea level change. It has been convention... more Antarctica plays a key role in global energy balance and sea level change. It has been conventionally viewed as a whole ice body with high albedo in General Circulation Models or Regional Climate Models and the differences of land cover has usually been overlooked. Land cover in Antarctica is one of the most important drivers of changes in the Earth system. Detailed land cover information over the Antarctic region is necessary as spatial resolution improves in land process models. However, there is a lack of complete Antarctic land cover dataset derived from a consistent data source. To fill this data gap, we have produced a database named Antarctic Land Cover Database for the Year 2000 (AntarcticaLC2000) using Landsat Enhanced Thematic Mapper Plus (ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer (MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100000-scale. Three land cover types were included in this map, separately, ice-free rocks, blue ice, and snow/firn. This classification legend was determined based on a review of the land cover systems in Antarctica (LCCSA) and an analysis of different land surface types and the potential of satellite data. Image classification was conducted through a combined usage of computer-aided and manual interpretation methods. A total of 4067 validation sample units were collected through visual interpretation in a stratified random sampling manner. An overall accuracy of 92.3% and the Kappa coefficient of 0.836 were achieved. Results show that the areas and percentages of ice-free rocks, blue ice, and snow/firn are 73268.81 km 2 (0.537%), 225937.26 km 2 (1.656%), and 13345460.41 km 2 (97.807%), respectively. The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality. These indicate that AntarcticaLC2000, the new land cover dataset for Antarctica entirely derived from satellite data, is a reliable product for a broad spectrum of applications.
A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection sch... more A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection scheme is presented. The advection scheme accounts for the observed divergence or convergence and freezing or melting of sea ice and predicts consequent generation or loss of new ice. The algorithm uses daily gridded sea ice drift and sea ice concentration products from the Ocean and Sea Ice Satellite Application Facility. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel of the output product or, in other words, to provide a frequency distribution of the ice age allowing to apply mean, median, weighted average or other statistical measures. Comparison with the National Snow and Ice Data Center SIA product revealed several improvements of the new SIA maps and time series. First, the application of the Eulerian scheme provides smooth distribution of the ice age parameters and prevents product undersampling which may occur when a Lagrangian tracking approach is used. Second, utilization of the new sea ice drift product void of artifacts from EUMETSAT OSI SAF resulted in more accurate and reliable spatial distribution of ice age fractions. Third, constraining SIA computations by the observed sea ice concentration expectedly led to considerable reduction of multi-year ice (MYI) fractions. MYI concentration is computed as a sum of all MYI fractions and compares well to the MYI products based on passive and active microwave and SAR products.
&lt;p&gt;Arctic sea ice type (SITY) variation is a sensitive indicator of climate change.... more &lt;p&gt;Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study analyzed eight daily SITY products from five retrieval approaches covering the winters of 1999&amp;#8211;2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA) and evaluated with five Synthetic Aperture Radar images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32&amp;#215;10&lt;sup&gt;6&lt;/sup&gt; km&lt;sup&gt;2&lt;/sup&gt; to 0.49&amp;#215;10&lt;sup&gt;6&lt;/sup&gt; km&lt;sup&gt;2&lt;/sup&gt; . Among all, KNMI-SITY and Zhang-SITY in the QSCAT period (2002-2009) agree best with NSIDC-SIA and perform the best, with smallest bias of -0.001&amp;#215;10&lt;sup&gt;6&lt;/sup&gt; km&lt;sup&gt;2&lt;/sup&gt; in FYI extent and -0.02&amp;#215;10&lt;sup&gt;6&lt;/sup&gt; km&lt;sup&gt;2&lt;/sup&gt; in MYI extent, respectively. In the ASCAT period (2007-2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases however exhibits large temporal variabilities as OSISAF-SITY. Factors that could impact performances of the SITY products are analyzed and summarized: (1) Ku-band scatterometer generally performs better than C-band scatterometer on SITY discrimination, while the latter sometimes identifies first-year ice (FYI) more accurately, especially when surface scattering dominants the backscatter signature. (2) Simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation of characteristic training dataset should be well accounted in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.&lt;/p&gt;
Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting ... more Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting triggered by global warming, in turn partly due to the possible acceleration of ice export from the Arctic Ocean to southern latitudes through identifiable gates. In this study, MYI and total sea ice areal flux through six Arctic gateways over the winters (October–April) of 2002–2021 were estimated using daily sea ice motion and MYI/total sea ice concentration data. Inconsistencies caused by different data sources were considered for the estimate of MYI flux. Results showed that, there is a slight declining trend in the Arctic MYI areal flux over the past two decades, which is attributable to the decrease in MYI concentration. Overall speaking, MYI flux through Fram Strait accounts for ~87% of the Arctic MYI outflow, with an average of ~325.92 × 103 km2 for the winters of 2002–2021. The monthly MYI areal flux through Fram Strait is characterized with a peak in March (~55.56 × 103 km2) an...
The Greenland high (GL-high) coincides with a local center of action of the summer North Atlantic... more The Greenland high (GL-high) coincides with a local center of action of the summer North Atlantic Oscillation and is known to have significant influence on Greenland ice sheet melting and summer Arctic sea ice. However, the mechanism behind the influence on regional Arctic sea ice is not yet clear. In this study, using reanalysis datasets and satellite observations, the influence of the GL-high in early summer on Arctic sea ice variability, and the mechanism behind it, are investigated. In response to an intensified GL-high, sea ice over the Beaufort Sea shows significant decline in both concentration and thickness from June through September. This decline in sea ice is primarily due to thermodynamic and mechanical redistribution processes. Firstly, the intensified GL-high increases subsidence over the Canadian Basin, leading to an increase in surface air temperature by adiabatic heating, and a substantial decrease in cloud cover and thus increased downward shortwave radiation. Seco...
The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) ... 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. Classifyi...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
The aim of this article was to investigate the potential of polarimetric decomposition of Chinese... more The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14-18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°-46°) were superior to low incidence angles (21°-27°) due to the overall higher accuracies.
The current climate change episode has impacted sea ice in the 2 polar regions differently. In th... more The current climate change episode has impacted sea ice in the 2 polar regions differently. In the Arctic, remarkable sea ice extent and thickness declines have been observed with a stunning depletion rate of old ice. No similar changes have been observed in the Antarctic. In this paper, the question posed in the title is addressed by reviewing findings retrieved from previous publications. The paper starts by identifying key geographic and climatic features and sea ice characteristics in the 2 polar regions and summarizing relevant recent records. It then proceeds by investigating interactions between sea ice and environmental factors, including atmospheric, oceanic, and dynamic aspects in each region, as well as the increasing number of icebergs in Antarctica. It is concluded that peculiarities of each polar region render the response to climate change differently. Researchers should not apply scenarios regarding the impacts of climate change on Arctic sea ice (i.e., retreat) to A...
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systema... more Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systematic intercomparison and analysis for SITY products are lacking. This study analysed eight daily SITY products from five retrieval approaches covering the winters of 1999-2019, including purely radiometer-based (C3S-SITY), scatterometerbased (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA-National Snow and Ice Data Center sea ice age) and evaluated with five synthetic aperture radar (SAR) images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from − 1.32 × 10 6 to 0.49 × 10 6 km 2. Among them, KNMI-SITY and Zhang-SITY in the QuikSCAT (QSCAT) period (2002-2009) agree best with NSIDC-SIA and perform the best, with the smallest bias of −0.001 × 10 6 km 2 in first-year ice (FYI) extent and −0.02 × 10 6 km 2 in MYI extent. In the Advanced Scatterometer (ASCAT) period (2007-2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases but exhibits large temporal variabilities like OSISAF-SITY. Factors that could impact performances of the SITY products are analysed and summarized. (1) The Ku-band scatterometer generally performs better than the C-band scatterometer for SITY discrimination, while the latter sometimes identifies FYI more accurately, especially when surface scattering dominates the backscat-ter signature. (2) A simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation in characteristic training datasets should be well accounted for in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution. Published by Copernicus Publications on behalf of the European Geosciences Union. * : the Kappa coefficient and overall accuracy values of C3S-1, C3S-2 and OSISAF-SITY are represented within a lower bound and an upper bound calculated when the Amb class is regarded as FYI and MYI, respectively. : best matches;
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The aim of this article was to investigate the potential of polarimetric decomposition of Chinese... more The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14-18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°-46°) were superior to low incidence angles (21°-27°) due to the overall higher accuracies.
The corrected multiyear ice (MYI) concentration product from the University of Bremen, Institute ... more The corrected multiyear ice (MYI) concentration product from the University of Bremen, Institute of Environmental Physics (IUP), is being retrieved with a two-step algorithm:(1) A constrained optimisation technique that uses different sets of microwave satellite data and the probability distributions of radiometric signatures of different ice types whose concentration (area fraction) is to be retrieved. This is the Environment Canada Ice Concentration Extractor (ECICE) [Shokr et al. 2008, Shokr and Agnew 2013]. The input data used here are microwave radiometer data at 18 and 37 GHz (horizontal and vertical polarisation) from the instrument AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1, as well as microwave scatterometer data from ASCAT on the European satellites MetOp-A and -B.(2) Two correction schemes for the output from ECICE in order to correct for anomalous radiometric and backscatter observations. Such anomalies are caused by snow wetness and m...
Polar sea ice is one of the Earth's climate components that has been significantly affected by th... more Polar sea ice is one of the Earth's climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4% per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10%-15% per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5% per decade until 2014 and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly of MYI. Due to differences in physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently, no method has existed to monitor the distribution and temporal development of Antarctic ice types, particularly MYI throughout the freezing season and on decadal time scales. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The first circumpolar, long-term time series of Antarctic sea ice types; MYI, first-year ice and young ice is being established, so far covering years 2013-2019. Qualitative comparison with synthetic aperture radar data, with charts of the development stage of the sea ice, and with Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval for the first time allows insight into the evolution and dynamics of Antarctic sea ice types. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors. 1 Introduction As an important component of the global climate system, sea ice affects and reflects changes in other climate components, controls energy and gas fluxes between ocean and atmosphere in polar regions, and it is an important part of the polar marine ecosystem. The Arctic sea ice extent has decreased by 4.1% per decade in the past three decades (Parkinson and Cavalieri, 2012b), while the declining rate for multiyear ice (MYI), ice that has survived at least one summer melt, is much higher, 1
IEEE Transactions on Geoscience and Remote Sensing, 2021
As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in t... more As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in the Arctic. Microwave scatterometers in the Ku-band and C-band can provide daily observations of sea ice type. However, their comparative capabilities in mapping ice type have not been thoroughly evaluated. We present a systematic intercomparison of the backscatter signature in VV polarization (<inline-formula> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula>) and the sea ice classification from three scatterometer systems using the same ice classification approach. The systems are the Ku-band quick scatterometer (QSCAT) and the newly launched Chinese rotating fan-beam scatterometer (RFSCAT) and the C-band advanced scatterometer (ASCAT). Three freezing seasons are used, i.e., 2007/08 and 2008/09 for the QSCAT/ASCAT comparison and 2019/20 for the RFSCAT/ASCAT comparison. With reference to ASCAT, <inline-formula> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> bias between QSCAT and RFSCAT results from their different incidence angles. A continuous declining trend of <inline-formula> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> from MYI and FYI is observed during winter, with a greater difference between MYI and FYI in the Ku-band. The MYI and FYI extent derived from QSCAT/RFSCAT is highly consistent with that derived from ASCAT, with a difference less than 7% and 3% for MYI and FYI, respectively. The overall accuracy (OA) is around 77% and 80% for the RFSCAT results and ASCAT results, respectively, compared with Sentinel-1 SAR images. The classification results show high consistency (81%–89%) with ice charts from the Canadian Ice Service. The incorporation of <inline-formula> <tex-math notation="LaTeX">${\mathrm {Tb}}_{36\mathrm {h}}$ </tex-math></inline-formula> from AMSR-E/AMSR2 improves the OA of the classification when using ASCAT or RFSCAT by 7%–11%.
Uploads
Papers by Yufang Ye