Sea Ice Concentration Retrieval Research Articles

Pan-Arctic sea ice concentration from SAR and passive microwave

Abstract. Arctic sea ice monitoring is a fundamental prerequisite for anticipating and mitigating the impacts of climate change. Satellite-based sea ice observations have been subject to intense attention over the last few decades, with passive microwave (PMW) radiometers being the primary sensors for retrieving pan-Arctic sea ice concentration, albeit with coarse spatial resolutions of a few or even tens of kilometers. Spaceborne synthetic aperture radar (SAR) missions, such as Sentinel-1, provide dual-polarized C-band images with < 100 m spatial resolution, which are particularly well-suited for retrieving high-resolution sea ice information. In recent years, deep-learning-based vision methodologies have emerged with promising results for SAR-based sea ice concentration retrievals. Despite recent advancements, most contributions focus on regional or local applications without empirical studies on the generalization of the algorithms to the pan-Arctic region. Furthermore, many contributions omit uncertainty quantification from the retrieval methodologies, which is a prerequisite for the integration of automated SAR-based sea ice products into the workflows of the national ice services or for assimilation into numerical ocean–sea ice coupled forecast models. Here, we present ASIP (Automated Sea Ice Products): a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from Sentinel-1 SAR and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave observations at a pan-Arctic scale for all seasons. We compiled a vast matched dataset of Sentinel-1 HH/HV (horizontal transmit, horizontal/vertical receive polarizations) imagery and AMSR2 brightness temperatures to train ASIP with regional ice charts as labels. ASIP achieves an R2 score of 95 % against a held-out test dataset of regional ice charts. In a comparative study against pan-Arctic ice charts and a PMW-based sea ice product, we show that ASIP generalizes well to the pan-Arctic region. Additionally, the comparison reveals that ASIP consistently produces relatively higher sea ice concentration than the PMW-based sea ice product, with mean biases ranging from 1.45 % to 8.55 %, and that the discrepancies are primarily attributed to disparities in the marginal ice zone.

Arctic Thin Ice Detection Using AMSR2 and FY-3C MWRI Radiometer Data

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature (TB) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath TB datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of TB signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks.

Relevance of warm air intrusions for Arctic satellite sea ice concentration time series

Abstract. Winter warm air intrusions entering the Arctic region can strongly modify the microwave emission of the snow-covered sea ice system due to temperature-induced snow metamorphism and ice crust formations, e.g., after melt–refreeze events. Common microwave radiometer satellite sea ice concentration retrievals are based on empirical models using the snow-covered sea ice emissivity and thus can be influenced by strong warm air intrusions. Here, we carry out a long-term study analyzing 41 years of winter sea ice concentration observations from different algorithms to investigate the impact of warm air intrusions on the retrieved ice concentration. Our results show that three out of four algorithms underestimate the sea ice concentration during (and up to 10 d after) warm air intrusions which increase the 2 m air temperature (daily maximum) above − 5 ∘C. This can lead to sea ice area underestimations in the order of 104 to 105 km2. If the 2 m temperature during the warm air intrusions crosses − 2 ∘C, all algorithms are impacted. Our analysis shows that the strength of these strong warm air intrusions increased in recent years, especially in April. With a further climate change, such warm air intrusions are expected to occur more frequently and earlier in the season, and their influence on sea ice climate data records will become more important.

DNN‐Based Retrieval of Arctic Sea Ice Concentration From GNSS‐R and Its Effects on the Synoptic‐Scale Forecasting as Supplementary Observation Source

AbstractUsing delay‐Doppler maps of Global Navigation Satellite Systems Reflectometry (GNSS‐R) from the TechDemoSat‐1 satellite and considering sea ice and ocean interaction, an innovative method for retrieval of Arctic sea ice concentration (SIC) based on a deep neural network is proposed. This retrieval method shows the potential of future GNSS‐R applications for Arctic missions. Compared with SIC products from Hamburg University, the root mean square errors (RMSE) of retrieved results in March and June 2016 are 0.0284 and 0.0415, respectively. When the retrieved GNSS‐R SIC data are added into the assimilation as supplementary passive microwave remote‐sensing data, it has a positive influence on improving the accuracy of the Arctic SIC forecast. Especially in some edge regions of sea ice, when compared to only assimilating the remote‐sensing data, the regional RMSE of joint assimilation has a maximum decrease of approximately 17% in the 24‐hr forecast time, and over 5% in 72‐hr.

A New Sea Ice Concentration Retrieval Algorithm Based on Relationship Between AMSR2 89-GHz Polarization and Landsat 8 Observations

A newly developed linear sea ice concentration (SIC) retrieval algorithm based on passive microwave AMSR2 measurements is proposed. SIC is retrieved by a linear function of the polarization ratio at 89 GHz (PR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">89</sub> ) corrected for atmospheric influence. We use Landsat 8 SIC data to derive the coefficients of the linear function. Results using this linear algorithm are compared to those of ASI2 developed by Lu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> . (2018), which is a non-linear 89 GHz algorithm with polarization difference at 89 GHz (PD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">89</sub> ) that also includes a correction for atmospheric influence. Both algorithms are compared with independent SIC data derived from Landsat 8, ship-based observation, and synthetic aperture radar (SAR) and that both tend to underestimate the ship-based and Landsat 8 SICs, particularly over thin ice. However, the proposed algorithm tends to provide results with lower bias and root mean square error (RMSE) for different ice categories.

Calibration of Uncertainty in Sea Ice Concentration Retrieval With an Auxiliary Prediction Interval Estimator

Calibration of Uncertainty in Sea Ice Concentration Retrieval With an Auxiliary Prediction Interval Estimator

Arctic sea ice concentration retrieval study of FY-3/MWRI based on the bootstrap algorithm

Arctic sea ice concentration retrieval study of FY-3/MWRI based on the bootstrap algorithm

Editorial for the Special Issue “Remote Sensing of the Polar Oceans”

This Special Issue gathers papers reporting research on various aspects of the use of satellites for monitoring polar oceans. It includes contributions presenting improvements in the retrieval of sea ice concentration, extent and area, and concerning error information; the interannual and decadal variability of sea surface temperature and sea ice concentration in the Barents Sea; validation and comparison of Arctic salinity products; melt pond retrieval applying a Linear Polar algorithm to Landsat data; the characterization of surface layer freshening from sea surface salinity and coloured detrital matter in the Kara and Laptev Seas; multi-sensor estimations of chlorophyll-a concentrations in the Western Antarctic Peninsula; and enhanced techniques for detection and monitoring of glacier dynamics and iceberg paths.

The Contribution of Sea-Ice Contamination to Inaccuracies in Sea-Ice Concentration Retrieval from Satellite Microwave Radiometry Data during the Ice-Melt Period

The Contribution of Sea-Ice Contamination to Inaccuracies in Sea-Ice Concentration Retrieval from Satellite Microwave Radiometry Data during the Ice-Melt Period

Retrieval of the Arctic sea ice concentration from FY-3B/MWRI using ASI algorithm

Retrieval of the Arctic sea ice concentration from FY-3B/MWRI using ASI algorithm

Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY-3B/MWRI data

Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY-3B/MWRI data

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

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

Consistent Comparison of Remotely Sensed Sea Ice Concentration Products with ERA-Interim Reanalysis Data in Polar Regions

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies.

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

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.

Spaceborne GNSS Reflectometry for Retrieving Sea Ice Concentration Using TDS-1 Data

A geophysical model function (GMF) for sea ice concentration (SIC) retrieval is developed based on the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data measured by the TechDemoSat-1 (TDS-1) satellite. The spreading characteristics of onboard processed delay-Doppler maps (DDMs) change with the surface roughness, which can be related to the SIC. A GNSS-R observable termed as differential delay waveform (DDW) generated from DDM is first used in this article to estimate SIC. Collocated SIC data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are used as the ground truth to develop and evaluate the SIC model based on the right edge waveform summation (REWS) of DDW. All usable TDS-1 data collected from February 2015 to February 2016 are adopted, and data collected over land were excluded. SIC models of the northern and southern hemispheres (SH) are developed, respectively, for avoiding the impact of geometry. In general, the REWS-based model can achieve a root mean square error (RMSE) of 11.78% and a bias of 1.67% for the northern hemisphere, and 12.10% and 1.94% for the SH, respectively. This article demonstrates the capabilities of the spaceborne GNSS-R in SIC retrieval.

Assessment of Categorical Triple Collocation for Sea Ice/Open Water Observations: Application to the Gulf of Saint Lawrence

Monitoring the sea ice cover is important for both climate studies and ice operations, such as shipping. It is challenging to validate even basic essential variables, such as the sea ice extent, due to a lack of appropriate validation data. Instead of focusing on validation, this paper looks at the use of categorical triple collocation (CTC) for the task of quantitatively comparing three colocated data sets. CTC has been developed and used in earlier studies to rank binary data sets. In this paper, we extend earlier studies and bring in recent results from the binary classification community to estimate the class imbalance (the relative proportion of each class, ice or water). We then use this class imbalance to obtain quantitative estimates of the proportion correct of ice (sensitivity) and the proportion correct of water (specificity). The methodology is first tested using toy data, after which three data sets from the Gulf of Saint Lawrence, on the east coast of Canada, are used. These data sets are from an ice–ocean model, a passive microwave sea ice concentration retrieval, and a sea ice concentration retrieval from synthetic aperture radar (SAR). By looking at both the sensitivity and the specificity, it is found that the passive microwave data have difficulty in recognizing ice during freeze-up, but they perform well at obtaining the correct water observations. This distinction cannot be made by ranking the data sets. The CTC method is compared with, and found to be complementary to, a validation using ice/water states from the interactive multisensor snow and ice mapping system.

Thin Ice Detection in the Barents and Kara Seas Using AMSR2 High-Frequency Radiometer Data

We have developed an algorithm for thin ice detection under winter conditions using the Advanced Microwave Scanning Radiometer 2 (AMSR2) radiometer high-frequency brightness temperature (36 and 89 GHz) L1R swath data, and a method to combine thin ice swath charts to a more reliable daily thin ice chart. Moderate Resolution Imaging Spectroradiometer (MODIS) ice thickness swath charts were used as reference data for the algorithm development. The algorithm is based on the classification of 36-GHz polarization ratio (PR36) and H-polarization 89-36-GHz gradient ratio (GR3689H) signatures with linear discriminant analysis. We applied an atmospheric correction to the AMSR2 L1R data following established sea ice concentration (SIC) retrieval algorithms. The PR36 and GR8936H signatures were adjusted to a constant air temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> ) before the thin ice detection using an empirical relationship between them and Ta. The maximum thickness of detected thin ice was estimated to be 20 cm. The thin ice detection with the L1R data is conducted only when SIC ≥ 70% and Ta ≤ -5 °C to limit conditions where thick ice may be erroneously detected as thin ice. The thin ice detection algorithm was developed for the Barents and Kara Seas, but it should also be applicable for other Arctic marginal ice zones (MIZs). The daily thin ice chart was validated using an independent set of MODIS daily ice thickness charts. The average probability for misclassification of thick ice as thin ice was 10% and 32% for vice versa. We demonstrate the use of the daily thin ice chart for monitoring the thin ice fraction in the Barents and Kara Seas.

Review of methods to retrieve sea ice parameters from satellite microwave radiometer data

Sea ice monitoring using long-term data of satellite passive microwave instruments enables climate change estimates. These numerical estimates depend on the methods used for sea ice parameter retrievals. This work presents a review of methods to retrieve sea ice parameters from the data of satellite microwave radiometers. Physical modeling of the sea ice–ocean–atmosphere microwave radiation provides the means to identify the general sources of the retrieval errors and to classify the methods by used approach. The basics of the algorithms are formulated along with assumptions and approximations as well as the data used for the algorithm verification. Weather filters are considered to identify the areas of open water. A comparative analysis of method advantages and limitations is given related to sea ice concentration retrievals from such satellite instruments as the series of Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR). A review of the basic satellite sea ice products based on SSM/I, AMSR-E and AMSR2 data is complemented by the list of the essential internet resources for operational and historical sea ice data.

Consideration of Atmospheric Effects for Sea Ice Concentration Retrieval from Satellite Microwave Observations

The numerical modeling of the sea ice-ocean-atmosphere system microwave radiation for the characteristics of AMSR2 (Advanced Microwave Scanning Radiometer 2) measurement channels is applied to substantiate the weather filters’ usage in the algorithms for sea ice concentration (SIC) retrievals from AMSR2 data. It is shown that the weather filter performance depends not only on atmospheric parameters but also on the type of the geophysical model function (GMF) used to parameterize the ocean emission dependence on surface wind speed (SWS). The analysis of high-resolution synthetic aperture radar along with SWS and SIC satellite products reveals that the SIC of spurious sea ice increases proportionally to SWS and exceeds 50% under conditions of storm winds with SWS > 25 m/s.

Усовершенствованный метод восстановления сплочённости морского льда по данным спутниковых микроволновых измерений вблизи 90 ГГц

Усовершенствованный метод восстановления сплочённости морского льда по данным спутниковых микроволновых измерений вблизи 90 ГГц