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Sea ice, crucial to the Arctic and Earth's climate, requires consistent monitoring and high-resolution mapping. Manual sea ice mapping, however, is time-consuming and subjective, prompting the need for automated deep learning-based classification approaches. However, training these algorithms is challenging because expert-generated ice charts, commonly used as training data, do not map single ice types but instead map polygons with multiple ice types. Moreover, the distribution of various ice types in these charts is frequently imbalanced, resulting in a performance bias toward the dominant class. In this article, we present a novel GeoAI approach to training sea ice classification by formalizing it as a partial label learning task with explicit confidence scores to address multiple labels and class imbalance. We treat the polygon-level labels as candidate partial labels, assign the corresponding ice concentrations as confidence scores to each candidate label, and integrate them with focal loss to train a convolutional neural network. Our proposed approach leads to enhanced performance for sea ice classification in Sentinel-1 dual-polarized SAR images, improving classification accuracy (from 87% to 92%) and weighted average F-1 score (from 90% to 93%) compared to the conventional training approach of using one-hot encoded labels and categorical cross-entropy loss. It also improves the F-1 score in four out of the six sea ice classes.

Bayesian neural networks (BNNs) have been demonstrated to be effective in accurate retrieval of sea ice concentration (SIC) from multisource data, while providing estimates of uncertainty, which are essential for downstream services. However, uncertainty obtained by BNNs is intrinsically uncalibrated, which indicates that it may not correlate well with model error. To address this issue, we investigate a new approach that combines an auxiliary (AUX) prediction interval (PI) estimator with the BNN-based SIC mean estimator to develop a well-calibrated SIC retrieval model that is both accurate and reliable. We adopt a training strategy called “uncertainty matching” to train the model, which ensures that the estimated uncertainties match the estimated PIs. We use a subset of AMSR2 brightness temperature data and ERA5 atmospheric data collected from 2014 to 2015 in the Baffin Bay area as input features of the model. Comparison between model inference and SIC labels obtained from the enhanced NASA Team (NT2) algorithm shows that the proposed approach is able to produce well-calibrated uncertainty with more accurate predictions in marginal ice zones (MIZs). [ABSTRACT FROM AUTHOR]

Precise and fast extraction of sea ice leads is the foundation for polar research and ship navigation. The accuracy of traditional methods for sea ice lead extraction is limited, and the efficiency of deep learning methods is difficult to guarantee. Besides, the tedious preprocessing steps complicate the application of existing methods. In this article, we proposed a lightweight semantic segmentation model based on the U-Net framework for sea ice lead extraction, which introduced lightweight blocks and a feature branch. Lightweight blocks took the place of the convolutional layers in U-Net to reduce the network parameters and increase operational speed. With the input of the contrast feature of horizontal-vertical (HV) polarization, the feature branch was used to improve the extraction precision and robustness. Besides, the combination of the lightweight blocks, feature branch and U-Net framework was beneficial to resist preprocessing. In the experiments, the performance of the proposed method on non-preprocessed Sentinel-1 dataset was better than that of the classical semantic segmentation method on preprocessed Sentinel-1 dataset in floating-point operations (FLOPs), parameters, frames per second (FPS), and accuracy evaluation. The results indicate that the proposed network is effective and lightweight for sea ice lead extraction from non-preprocessed data. [ABSTRACT FROM AUTHOR]

Sea ice is regarded as an indicator of temperature change. In recent years, the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technology has made remarkable progress in sea ice detection. Delay-Doppler maps (DDMs) as one of the significant observations can reflect different characteristics for sea ice and open water, and a single DDM is usually viewed as the unit of feature extraction; however, it is easily influenced by wave height, wind, and other factors. Therefore, this letter proposes building scene semantic objects to enhance the reliability of observation and reflect the object characteristics. The synergism between DDMs and the spatial correlation of specular points was considered. Afterward, histogram features were extracted to express the distribution of scattered energy. The random forest (RF) model was developed to distinguish sea ice from open water. The performance of the method by using TechDemoSat-1 (TDS-1) dataset was evaluated with the sea ice concentration products provided by Ocean and Sea Ice Satellite Application Facility (OSISAF). The results show that the overall accuracy is 98.17%, which outperforms traditional observation methods. [ABSTRACT FROM AUTHOR]

Oceans cover approximately 71% of the Earth's surface and provide numerous services to the environment and humans. Precise, real-time, and large-scale monitoring of the oceanographic parameters is essential for ocean conservation and understanding the interactions between oceans and the atmosphere. In this regard, synthetic aperture radar (SAR) systems, with unique capabilities (e.g., day-night and almost all-weather data acquisition), provide valuable datasets for ocean studies. Many studies have exploited the applications of SAR imagery for oceans and have proposed numerous methods to study oceanographic parameters. In this study, a brief introduction to SAR and the interaction between microwave signals and the ocean surface are initially provided. Then, the important spaceborne and airborne SAR systems for oceanographic applications are summarized. Subsequently, 12 different applications of SAR systems in the ocean are comprehensively discussed, and the advantages and disadvantages of SAR systems for ocean studies are extensively explored. Finally, the research trend on SAR applications in the ocean is provided by analyzing all the relevant papers published between 1973 and the end of December 2022, and the existing challenges are discussed for future studies.

Fully polarimetric (FP) and compact polarimetry (CP) modes are advanced technologies of synthetic aperture radar (SAR). Compared to the parameters available from traditional orthogonal backscatter coefficients, these technologies offer extra parameters, called polarimetric parameters, that fully characterizes the polarization status and the phase shift of the received backscattered signal. They include a set of ratios and correlation between co- and cross-polarization, with phase included, and another set of total backscatter power (SPAN) decomposed into power from each scattering mechanism (single-, multiple-, and double-bounce). Recent studies have demonstrated the potential of using polarimetric parameters in improving retrieval of snow and sea ice information. This review paper presents a brief background on polarimetric parameters available from the FP and CP modes and highlights their uses in applications of sea ice classification, concentration, surface features (rafting and ridging), new ice versus open water identification, thickness of thin ice, and multiyear ice surface discrimination between hummocks and melt pond. The potential of using power from the three scattering mechanisms is demonstrated in a few applications. Some information retrieved using polarimetric data cannot be retrieved using conventional SAR backscatter. This review concludes with a brief presentation on operational use of SAR polarimetric data (FP and CP) in operational ice monitoring programs.

High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune, and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and efficient automatic method for melt pond region segmentation and boundary extraction from high-resolution aerial photographs. The proposed algorithm is based on a swin transformer U-Net in which we introduce novel cross-channel attention mechanisms into the decoder design. The framework operates with optical data and allows for classifying imagery into four classes, i.e., sea ice/snow, open water, melt pond, and submerged ice. We use aerial photographs collected during the Healy–Oden Trans Arctic Expedition over Arctic sea ice in the summer season of 2005 as test data. The experimental results show that the proposed method is suitable for precise automatic extraction of melt pond geometry, and it can also be extended for other optical data sources that involve melt ponds. The approach has a promising potential to be used to analyze melt ponds' corresponding changes between years.

Mapping different types of sea ice that form, grow, and melt in polar oceans is essential for shipping navigation, climate change modeling, and local community safety. Currently, ice charts are manually generated by analysts at the Canadian Ice Service based on dual-polarized RADARSAT-2/RADARSAT Constellation Mission imagery on a daily basis. Inspired by the demand for a computer-based mapping system, we have developed an automatic sea-ice classification method that integrates spatial contexture (unsupervised segmentation) with textural features (supervised pixel-level labeling). First, the full-scene image is oversegmented, and the segments are merged into homogeneous regions across the entire scene. Second, pixel-based classifiers (support vector machine and random forest) are compared for their ability to label the generated homogeneous regions. Finally, the segmentation and labeling are combined using a proposed energy function. The proposed method was tested on 18 dual-polarization RADARSAT-2 scenes acquired over the Beaufort Sea. This dataset contains water, young ice, first-year ice, and multiyear ice covering melt, summer, and freeze-up seasons. The proposed method obtains an average classification accuracy of 86.33% based on the leave-one-out validation. The experimental results show that the proposed method achieves promising classification results in both the quantity and quality measurements compared with benchmark methods. The robustness against incidence angle variance indicates that the proposed method is well qualified for operational sea-ice mapping.

The Meteor-M No. 2-2 meteorological satellite with the microwave radiometer MTVZA-GY on board was launched into a circular sun-synchronous orbit on July 5, 2019. The radiometer conducts a conical scanning at 65-degree incidence angle and receives the Earth's outgoing radiation in the frequency range ν ≈ 6–190 GHz. The swath width is 2500 km on ascending orbits and 1500 km on descending orbits. The parameters of the ocean, land surface, and troposphere are extracted from brightness temperatures TB(ν) measured at imager frequencies of 6.9, 10.65, 18.7, 23.8, 31.5, 36.5, 42, 48, and 91.65 GHz in vertical and horizontal polarizations. Measurements at the sounder frequencies (ten channels in the 52–58 GHz oxygen absorption band and three channels in the strong water vapor resonance line region centered at 183.31 GHz) provide information on air temperature and humidity in the troposphere and stratosphere. The structure and development of dynamic atmospheric phenomena of synoptic scale are imprinted on the global TB(ν) maps at imager frequencies. The TB(ν) time series at the sounder frequencies allowed us to detect and trace the evolution of a rare phenomenon — a sudden stratospheric warming over Antarctica in August–September 2019.

The surface albedo of Arctic sea ice is a critical variable in the Earth’s energy budget, and various Arctic sea ice albedo datasets have been derived from remote sensing data. However, the influences of reflectance anisotropy and melt ponds were not well considered in previous studies. To improve the estimation accuracy and calculation efficiency, we developed a method for estimating the surface albedo of Arctic sea ice using an ensemble back-propagation neural network (EBPNN) model. A bidirectional reflectance distribution function (BRDF)/albedo dataset of snow, white ice, and melt ponds was constructed using the asymptotic analytical radiative transfer (AART) model, and the nonlinear relationships between the surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) and surface albedo were established using the EBPNN model (prediction correlation coefficient $(R)= 0.996$ and root mean square error (RMSE) = 0.023). Our results are consistent with the in situ measurements and the Medium Resolution Imaging Spectrometer (MERIS) product ($R= 0.916$ , RMSE = 0.085, and bias = 0.001), in which the reflectance anisotropy of the snow/ice and the influence of melt ponds were fully considered, and the calculation efficiency was significantly improved by using the EBPNN model. The results of this study provide new insights for estimating the Arctic sea ice albedo from satellite observations, and the proposed EBPNN method can be used to generate a long-term Arctic sea ice albedo dataset with superior estimation accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

This article addresses the problem of tracking weak and extended targets in the clutter for marine radar systems. In the proposal, multiple kernelized correlation filters (MKCFs) incorporate a low-threshold constant false alarm rate and segmentation model under the principle of multiframe track before detect (MF-TBD). By setting the similarity score of the correlation filter as the test statistic, the maximization of the integration over frames can be solved by the essentially exhaustive searching of the MKCF. Since the correlation filter measures the similarity of the intensity distributions in the extended templates, the proposed method can differentiate the weak target from the surrounding clutter and other targets at a close range. Compared to other amplitude-based MF-TBD methods, it performs better in detecting and tracking extended targets in the real-radar data with heavy clutter caused by floating sea ice and the simulations with Rayleigh and $K$-distributed sea clutters. [ABSTRACT FROM AUTHOR]

Recent investigations using polarimetric decomposition and numerical models have helped to improve the understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer (RT) models provide one method of improving this understanding. These are the first published experiments using the snow microwave RT (SMRT) model to investigate the response of different frequencies (L-, C-, and X-band) at horizontal-horizontal (HH) and vertical-vertical (VV) polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice–water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. An analysis of the backscatter response to different properties indicates that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited to other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing root mean square (rms) height at the ice–water interface, which supports surface scattering at the ice–water interface as being the dominant scattering mechanism. These results demonstrate that the SMRT is a valuable tool for understanding the response of backscatter to changes in freshwater lake ice properties and could be used in the development of inversion models. [ABSTRACT FROM AUTHOR]

Using the precipitable water vapor (PWV) and cloud fraction (CF) measurements from the atmospheric infrared sounder (AIRS), this study investigates the potential feedback on Arctic sea ice concentration (SIC) caused by variations in PWV and total CF, as well as CFs at low, middle and high levels during December 2002–November 2020. We find that increases in PWV and total CF generally coincide with the Arctic sea ice decline in each season, and both PWV and total CF anomalies are typically correlated with SIC anomalies in all seasons except for the PWV anomalies in summer. In addition, during all seasons except summer, the low CF generally exhibits closer connection than the middle and high CFs with the trend in total CF and sea ice, and the correlation of SIC with total CF anomalies is governed by that with low CF anomalies. The sensitivities of SIC to changes in PWV and total CF are typically negative, and they are generally consistent with each other in each season. Moreover, the negative sensitivities of SIC to low, middle, and high CFs are greatest and most extensive in summer. The contribution of PWV is typically much greater and more extensive than that of total CF to Arctic sea ice change in all seasons. Among the CFs at different levels, the positive contribution of low CF is typically most extensive in winter and spring, while the contributions of high and middle CFs are relatively greater in summer and autumn. [ABSTRACT FROM AUTHOR]

Sea ice plays an important role in global climate system. Operation IceBridge (OIB) was launched in 2009 to bridge the gap in polar observations between the Ice, Cloud, and Land Elevation Satellite (ICESat) and ICESat-2 missions. OIB flew more than 1000 aircraft surveys, providing annual mapping of sea ice, glaciers, and ice sheets in the Antarctic and Arctic. This study utilizes altimetry data and synchronized optical images from OIB to derive Arctic sea ice freeboard estimates for the period from 2009 to 2019. Lead detection is important in determining sea ice freeboard. We developed an improved approach by combining optical images, OIB laser pulse apparent reflectivity, and L1B elevation profiles to detect leads. We then determined the local sea surface height (SSH) with a lowest elevation method from the L1B elevation profile of leads. Thus, sea ice freeboard was calculated by the difference between L2 relative elevation and corresponding local SSH. We derived the estimates of the Arctic sea ice freeboard from 2009 to 2019. Spatial and temporal variations of the freeboard were analyzed based on the estimates. The Arctic annual average sea ice freeboard showed a decreasing trend from OIB observations. However, an obvious increase in multiyear ice freeboard was observed in 2014. We validated our freeboard estimates with OIB freeboard product from the National Snow and Ice Data Center and coincident ICESat-2 freeboard. The comparison shows that our freeboard estimates generally compare well with the ICESat-2 freeboard, and our method is more sensitive to thin ice. [ABSTRACT FROM AUTHOR]

This study proposes a purely data-driven model for the weekly prediction of daily sea ice concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season. The model, SICNet, adopts an encoder–decoder framework with fully convolutional networks (FCNs) and can predict the SIC (covering $320\times224$ grids, each with a resolution of 25 km) one-week lead with high accuracy. We design a temporal–spatial attention module (TSAM) to help SICNet capture spatiotemporal dependencies from SIC sequences. The satellite-derived SIC data of 33 years (1988–2020) from the National Snow and Ice Data Center (NSIDC) are employed to train and test the model, 1988–2015 for training, and 2016–2020 for testing. SICNet achieves the mean absolute error (MAE) of 2.67%, the mean absolute percentage error (MAPE) of 8.67%, and the Nash–Sutcliffe efficiency (NSE) of 0.9784 in weekly predicting of SIC during the melting season. SICNet achieves better performance than existing deep-learning-based models. The TSAM reduced the MAE from 2.73% to 2.67%. We evaluate the model’s performance by recursively predicting, from seven- to 28-day leads. We employ the binary accuracy (BACC) metric to measure the accuracy of the predicted sea ice extent (SIE) and compare SICNet with the anomaly persistence (Persist). SICNet shows better performance than Persist with an average BACC on the 28th day of 2016–2019 over 90% (90.17%). For the 28-day lead predictions of three extreme minimum SIE in September 2007, 2012, and 2020, SICNet outperforms Persist with an average improvement of 1.84% in BACC and $0.16 milkm^{2}$ in the SIE error. [ABSTRACT FROM AUTHOR]

Sea ice leads in the Arctic Ocean and Antarctic Ocean are of great significance to polar ecology, climate change, and ship navigation. While rule-based remote-sensing classification methods, such as the threshold method, have been widely used for surveying and mapping polar sea ice leads, they have difficulty in overcoming the problems of noise and poor generalization. In this article, we presented an automated, deep learning sea ice open lead detection network in low wind speed conditions, entropy-weighted network (EW-Net). In EW-Net, a dense block was introduced into a U-Net baseline network to strengthen feature propagation, and an entropy sampling structure was designed to improve the pertinence of the network to leads and alleviate the influence of noise. Furthermore, an entropy-weighted feature fusion block was designed to better restore features. EW-Net was trained on the Sentinel-1 synthetic aperture radar (SAR) dataset in order to obtain lead maps of different polar regions from Sentinel-1 images. The results showed that the proposed EW-Net model was more effective and more generalizable for polar sea ice lead detection than the threshold method and the deep learning methods U-Net and DeepLab V3 Plus, with an overall accuracy (OA) exceeding 0.97. In addition, EW-Net showed advantages in terms of outstanding generalization from cross-sensor image monitoring of leads. The method proposed in this article could be beneficial for providing accurate maps of polar sea ice leads in open water and contribute to further research on polar science. [ABSTRACT FROM AUTHOR]

Arctic sea-ice thickness and salinity retrievals are simulated to explore the performance of nadir-observing microwave radiometry operating with up to 16 frequency channels in the 0.5–2-GHz frequency range. A radiative transfer model is used to create lookup tables of the circularly polarized thermal emissions of first-year (FY) and multiyear (MY) sea ice, and the performance of two distinct retrieval methods is examined. The first method retrieves only sea-ice thicknesses, while the second retrieves both ice thickness and ice salinity. Retrieval errors are simulated for both FY and MY sea ice as a function of ice thickness, salinity, and temperature to investigate the impact of radiometric uncertainty, the frequency channels used, and any errors in ancillary information. To gain further insight into Arctic scale retrieval performance, a simulated brightness temperature dataset is produced for Arctic sea ice for the period October 2020–March 2021 using sea-ice thicknesses from the SMOS-CryoSat-2 algorithm. Compared to existing sea-ice thickness retrievals obtained from 1.4-GHz microwave radiometers, the results demonstrate that 0.5–2-GHz radiometry can achieve higher sensitivity to a sea-ice thickness within the range 0.5–1.5 m for FY sea ice and enable the retrieval of multiple sea-ice parameters (thickness and salinity) simultaneously. [ABSTRACT FROM AUTHOR]

Automated solutions for sea ice-type classification from synthetic aperture radar (SAR) imagery offer an opportunity to monitor sea ice, unimpeded by cloud cover or the arctic night. However, there is a common struggle to obtain accurate classifications year round, particularly in the melt and freeze-up seasons. During these seasons, the radar backscatter signal is affected by wet snow cover, obscuring information about underlying ice types. By using additional spatiotemporal contextual data and a combination of convolutional neural networks and a dense conditional random field, we can mitigate these problems and obtain a single classifier that is able to classify accurately at 3.5-m spatial resolution for five different classes of sea ice surface from October to May. During the near year-long drift of the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition, we collected satellite scenes of the same patch of Arctic pack ice with X-band SAR with a revisit time of less than a day on average. Combined with in situ observations of the local ice properties, this offers up the unprecedented opportunity to perform a detailed and quantitative assessment of the robustness of our classifier for level, deformed, and heavily deformed ice. For these three classes, we can perform accurate classification with a probability >95% and calculate a lower bound for the robustness between 85% and 88%. [ABSTRACT FROM AUTHOR]

We have developed a thin ice detection algorithm for the AMSR2 radiometer data. The algorithm, denoted as AMSR2 thin ice detection algorithm—version 2 (ATIDA2), is targeted for the Arctic Ocean. The detection of thin ice with a maximum thickness of 20 cm is based on the classification of the 36-GHz polarization ratio ($PR36$) and H-polarization 89–36-GHz gradient ratio ($GR8936H$) signatures with a linear discriminant analysis (LDA) and thick ice restoration with $GR3610H$. The thick ice restoration removes erroneous thin ice detections due to thin and thick ice $PR36$ and $GR8936H$ signature mixing. ATIDA2 is applied only when sea ice concentration (SIC) is ≥70% and the air temperature is ≤−5 °C to decrease misclassification of thick ice as thin ice. For the AMSR2 L1R brightness temperature data, an atmospheric correction is applied following an EUMETSAT OSI SAF correction scheme in SIC retrieval algorithms. ATIDA2 is applied to L1R swath datasets, and then, the results are combined to a daily thin ice chart. ATIDA2 was developed and validated using MODIS ice thickness charts over the Barents and Kara Seas. The average probability for misclassification of thick ice as thin ice in the daily chart is 8.7%, and 37.0% for vice versa. The comparison of the ATIDA2 chart and the SMOS ice thickness chart over the Arctic showed rough correspondence in the thin versus thick ice classification. The ATIDA2 chart is targeted to be used together with SAR imagery for various sea ice classifications. [ABSTRACT FROM AUTHOR]

Manifold calibration improves parametric angle estimator accuracy and resolution performance by reducing the mismatch between the model of an array’s response to directional sources and truth. This article presents nonparametric array manifold calibration for a multichannel ice-penetrating synthetic aperture radar (SAR) sounder used for imaging subglacial morphology with parametric angle estimation in tomography. In this study, we outline a methodology for identifying scatterers at known angles from multichannel imagery by aligning our measurements to an independent fine-resolution satellite-derived digital elevation model of the Arctic that extends beyond the swath of the SAR. We adopt a support statistic based on our partial knowledge of the array response to identify approximately single-source measurements in our scenes. This technique is a departure from traditional approaches to the sounder array characterization problem that require measurements of flat, specular surface reflections from a maneuvering platform. We aggregate observations of single sources and measure manifold corrections relative to our nominal model from the principal eigenvector of our array covariance. We demonstrate the application of three measured manifolds in tomography and compare performance to a nominal manifold that assumes isotropic radiators and known array geometry. We present radar-derived topography of exposed rock and sea ice in the Canadian Arctic Archipelago under the measured and nominal manifolds and report improved vertical accuracy realized with a measured manifold model assumed by the MUltiple SIgnal Classification angle estimators in 3-D image formation. [ABSTRACT FROM AUTHOR]

The consistent and long-term spaceborne synthetic aperture radar (SAR) missions such as Sentinel-1 (S-1) provide high-quality dual-polarized $C$-band images particularly suited to sea ice monitoring. SAR data are currently the primary source of information for sea ice charting made by human ice experts as a result of its integration with multiple information sources at different scales (mainly radiometers). The rise of deep learning now opens the prospect of automatic sea ice mapping. In this article, we investigate the potential of a fully convolutional network (FCN) for the automatic estimation of sea ice concentration (SIC). With input data down-sampled at 200 m and an FCN architecture (depth and receptive field) duly parameterized, our approach is to mimic the work of an analyst who considers general context and does not necessarily use the highest possible resolution but speckle-noise polluted data. A comprehensive database is generated with 1320 dual-polarized S-1 scenes collocated with ice charts produced by MET Norway. A dedicated attention is paid to seasonal representativeness to ensure the adequate performance for all sea ice types. Even if the FCN output is modeled as a categorical problem, the proposed architecture accounts for the semantic distances between SIC classes by introducing an auxiliary loss. A comparative benchmark with Ocean and Sea Ice Satellite Application Facility (OSISAF) and MET Norway SIC products is carried out, showing an overall accuracy of 78.2% for our 6-class classification approach. The FCN model is shown to be evenly robust to sea ice seasonal variability and incidence angle.

It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step.

Sea ice classification using synthetic aperture radar (SAR) in the wide-swath mode is one method for ice type retrieval. The conventional method of sea ice classification involves single-scale classification, wherein the backscatter and gray-level co-occurrence matrix features in a specific resolution are used as classification bases. The classification is implemented only once. In this way, however, the scalloping and interscan banding (ISB) artifacts in some TerraSAR-X ScanSAR data affect the classification result. In this article, a two-scale method of sea ice classification is proposed. The two-scale process includes both the coarse- and fine-resolution classifications. First, the coarse-resolution classification utilizes downscale images to generate the ice zone. Subsequently, the fine-scale classification utilizes the full-resolution SAR image and the ice zone to generate the final classification result. The support vector machine is used as the classifier. This two-scale method provides a better classification result compared to the single-scale method in two aspects. First, it effectively avoids the appearance of scalloping and ISB artifacts in the results. Second, it is less time-consuming and provides satisfactory results for discriminating multiyear ice, first-year ice, and water. The validation is accomplished using independent images, with an overall average accuracy of 86.38%. This article shows that this method may provide an alternative for sea ice classification using TerraSAR-X ScanSAR data.

The L-band radiometer onboard the soil moisture active passive (SMAP) mission is used to retrieve sea surface salinity (SSS) over global ocean. In the Arctic seas, one of the major challenges of SSS remote sensing is the presence of sea ice. This paper proposes a data-driven ice correction (IC) algorithm which extracts emission from the water portion of measured brightness temperature (TB) in scenes mixed with water and ice. Emission of the ice portion was removed based on estimation according to the ice fraction (fice) in the satellite footprint and ice signature derived from surrounding pixels. The IC algorithm is applied to SMAP TB data to obtain TB with IC (TBIC), which are used for SSS retrieval using the standard JPL SMAP CAP processing system. We show that the algorithm is most effective near the ice edge, thereby increasing the fice threshold for possible SSS retrieval to 15% from the current 3% without IC. SMAP SSS are validated using in situ salinity collected during NASA's Ocean Melting Greenland (OMG) mission from 2016 to 2020 along the Greenland coast. The number of collocations between OMG and SMAP daily gridded salinity increased by more than 30% with IC. The statistical analysis shows a similar retrieval accuracy with or without IC, with the standard deviation of the difference between OMG and SMAP of 1.41 psu (with IC) and 1.42 psu (without IC). The bias-adjusted SMAP SSS depicts salinity patterns and gradients around Greenland consistent with OMG measurements.

In this study application of convolutional neural networks (CNNs) preceded by synthetic aperture radar (SAR), image segmentation for sea ice concentration (SIC) estimation over the Baltic Sea from dual-polarized C-band SAR imagery is studied. Three algorithm variants were studied and trained using FMI ice chart SIC or a synthetic SIC dataset with different SIC values generated by combining pure open water and sea ice blocks by applying binary masks. The first two algorithm variants were trained using only open water and fully ice-covered patches, based on the FMI ice charts, and they had a similar CNN structure. These two algorithm variants differ only in deriving the segmentwise SIC from the CNN output. In the third algorithm, variant synthetic SIC data derived as a mixture of open water and fully ice-covered ice patches according to the ice charts were used in training. The estimation results were evaluated with respect to the FMI ice chart SIC for an independent test dataset. The results were very encouraging for operational purposes and significantly better than for our earlier SAR-based SIC estimation algorithms. The algorithm version trained with synthetic SIC data clearly outperformed the two other algorithm versions and our earlier SIC estimation results based on dual-polarized SAR, using independent FMI ice chart SIC as a reference. [ABSTRACT FROM AUTHOR]

In this article, we investigate coherent global navigation satellite system reflectometry (GNSS-R) measurements obtained at the low earth orbits (LEOs) as a potential new data source for ionospheric total electron content (TEC) and ionospheric disturbance observations. Current global ionospheric TEC maps (GIMs) have limited spatial and temporal resolutions and accuracy due to lack of GNSS observations over oceans, polar caps, and inaccessible terrains. Our analysis of Spire Global’s CubeSat data indicates that coherent GNSS signals reflected off sea ice, inland water bodies, and calm ocean surface can be processed to achieve cm-level precision carrier phase estimations. Signal coherency is especially prevalent over sea ice where 41.7% reflections are coherent, compared to 4.3% in the overall dataset. This article presents the methodology to estimate slant TEC along the reflection signal ray path using coherent dual-frequency GNSS-R pseudoranage and carrier phase estimations obtained from low-cost CubeSats. The methodology is applied to Spire Global’s CubeSat data. The results show that the slant TEC retrieved from GNSS-R measurements and from GIM follow a similar trend. Moreover, the GNSS-R-based TEC time series offer a nearly “frozen in time” observation of the ionospheric structures due to the rapid scan velocity of GNSS-R rays across the ionosphere. The study demonstrates the potential of GNSS-R observations to fill the data gaps over the polar regions where space weather activities and TEC disturbances are most frequent and intense. Potential error sources and mitigation techniques are also discussed. [ABSTRACT FROM AUTHOR]

Currently, sea-ice thickness is often estimated using microwave radar altimetry. It is well known that radar measurements are highly dependent on the roughness characteristics of the observed scene and can have a profound effect on altimetry-based thickness estimates. This simulation-based study highlights the importance of sea-ice roughness parameterization in the context of radar altimetry. This work provides a new sea-ice roughness parameterization in the form of an elevation spectrum developed from field campaign data. The formulation of this elevation spectrum is partly based on the generalized power law. Simulations have been developed to predict electromagnetic scattering from surfaces described by the new spectrum, with a focus on the X-band (10 GHz) scattering with nadir incidence. In this work, both the analytical small slope approximation and numerically exact Method of Moments are applied to various sea-ice surface spectra, representing a wide range of surface roughness conditions. The results show that the normalized radar cross section of the sea-ice interface varies significantly depending on the surface elevation spectrum and the statistics of the rough interface. Comparisons with numerically exact methods show that analytical approximations to rough surface scattering work reasonably well for the cases considered here, except at large scattering angles. [ABSTRACT FROM AUTHOR]

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2–18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017–2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid of in situ snow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales. [ABSTRACT FROM AUTHOR]

The Microwave Radiation Imager (MWRI) sensors aboard on the Chinese FengYun-3 (FY-3) series satellites have a great potential for long-term study of sea ice distribution. This study corrected the newly released FY-3D MWRI brightness temperature (TB) data with the Advanced Microwave Scanning Radiometer 2 (AMSR2) TB data and used an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm to derive the sea ice concentration (SIC) from these corrected MWRI TB data. We assessed the accuracy of our MWRI-ASI SIC product by comparing with the published SIC products at different spatial resolution and by validating with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-1 data. We find that MWRI-ASI SIC has the smallest difference with AMSR2-ASI SIC with the mean absolute difference (MAD) of 6.8%, and the differences mainly occur at the marginal ice zone regions. The MWRI-ASI displays the highest difference with Sea Ice Index and OSI-430-b at 25 km with the mean MAD of 11.8%. MADs between MWRI-ASI SIC and SIC products from other sensors are below 10% except for late June to early October, when those are higher. Compared with the MODIS SIC, MWRI-ASI SIC outperforms other SIC products at the same resolution, with the mean bias of −2.1% and mean RMSD of 13.5%. Although MWRI-ASI tends to under-estimate high SIC, it can capture details of large leads, ice edge, and fragmented ice area. Our MWRI-ASI SIC product at 12.5 km is promising to integrate into long-term sea ice records. [ABSTRACT FROM AUTHOR]

Based on a developed three-layerair–ice–water reflection model, this article simulates the evolution of reflection coefficient versus elevation angle. Due to the interference between the signal components from the air–ice and ice–water interfaces, the reflection coefficient experiences an oscillating pattern versus elevation angle so that detecting sea ice using the power or amplitude of the reflected global navigation satellite system (GNSS) signal has to choose a suitable satellite to reduce the influence of the oscillating pattern. A sea ice surface is more stable and presents higher correlation than a dynamic ocean surface, this article explores the potential of detecting and measuring sea ice using the coherency of reflected GNSS signal for coastal scenario. Experimental results show that phase coherency can significantly detect sea ice without strictly limiting elevation and azimuth angle. This article also is to is to explore the potential of retrieving sea ice thickness using the oscillating phase pattern versus elevation angle. The phase compensation and the dual-polarization observation are proposed to remove the delay phase between the direct and reflected signal from the estimated phase of the reflected GNSS signal. The results show that the amplitude and frequency of the oscillating phase pattern, respectively, have an inversely proportional and positively linear relationship with sea ice thickness. Simulation shows that, compared to the oscillating amplitude, the oscillating frequency is a better choice to measure sea ice thickness. The frequency of the dual-polarization oscillating pattern could provide the measurement performance with a root-mean-square error of 0.05 m. [ABSTRACT FROM AUTHOR]

Sea ice concentration (SIC) is an indispensable parameter for the study of polar sea ice. The existing methods can obtain accurate SICs for most situations, but they usually perform poorly in low SIC regions because of the spatial differences in the neighboring pixels induced by the discontinuity of the sea ice cover. In this letter, to cope with the difficulty of this problem, an improved SIC estimation method is proposed to retrieve SIC, focusing on low SIC regions. The proposed method introduces the spatial relationships into SIC estimation by employing a total variation (TV) regularizer. Moreover, nonnegative constrained least squares (NCLS) is used to derive the optimal solutions from the SIC estimation equation. Verification was conducted in low SIC regions (0%–50%) of the Antarctic utilizing ship-based in situ data and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results were compared with those of some of the mature methods. The results indicated that the proposed method can obtain a superior accuracy with a smaller root-mean-square error (RMSE) (6.0%–14.61%) than the other algorithms in low SIC regions. Furthermore, the proposed method can accurately estimate the SIC of both first-year ice and multiyear ice. The findings of this study confirm the need to consider the spatial relationships in the processing of SIC estimation. [ABSTRACT FROM AUTHOR]

Estimation of melt pond fraction (MPF) using the Advanced Microwave Scanning Radiometer for the Earth Observing System (EOS)/2 (AMSR-E/2) brightness temperature (TB) data is required for enhancing understanding of the role of melt ponds in the Arctic and in the earth’s climate system. An original MPF retrieval is based on gradient ratio (GR), defined as the normalized difference between the 6.9 GHz horizontal (H) channel and the 89.0 GHz vertical channel GR(6/89) of AMSR-E. However, using the coarsest spatial resolution 6.9 GHz channel includes potential land contamination in TB in regions of narrow waterways such as the Canadian Arctic Archipelago (CAA). This letter aims to provide a higher resolution MPF product, derived by using 10.7, 18.7, 23.8, 36.5, and 89.0 GHz TBs at H-polarization instead of the 6.9 GHz TB H-polarization in the GR calculation. For GR(10/89)- and GR(18/89)-based MPF retrievals, the difference standard deviations were ~3.8%. This is lower than the ~14.3% standard deviation of the GR(23/89)-, GR(36/89)-, and PR(89)-based MPF retrievals. Our results suggest that the GR(10/89) and (18/89) MPF retrievals are available at a product quality similar to the original GR(6/89)-based MPF retrieval. We recommend that the GR(18/89) MPF retrieval adopted is used in nearshore environments such as in the CAA. [ABSTRACT FROM AUTHOR]

The accuracy of sea surface parameters retrieved from altimeter missions is predominantly governed by the choice of the so-called “retracking” algorithm, i.e., the model and inversion method implemented to obtain the surface parameters from the backscattered waveform. For continuity reasons, the choice of space agencies is usually to apply the same retracker from one satellite mission to the other to ensure long-time homogeneous series. In this article, taking the opportunity of a new configuration of the nadir pointing measurements onboard the recently launched China France Oceanography Satellite (CFOSAT) with the Surface Waves Investigation and Monitoring (SWIM) instrument (Hauser et al., 2020), the retracking method was upgraded, by implementing a novel algorithm, called “Adaptive” retracker. It combines the improvements brought by Poisson et al., (2018) for the estimation of surface parameters from peaked waveforms over sea ice, improvements in the way the instrumental characteristics are considered in the model (mispointing, point target response) and a more accurate consideration of speckle statistics. In this article, we first show from simulations carried out in the instrumental configuration of SWIM that the Adaptive algorithm has better accuracy and performance than the classical MLE4 algorithm. Then, the geophysical parameters obtained with real data from SWIM are analyzed with comparisons to reference data sets (model and products from altimeters). We show that this new algorithm has several benefits with respect to the classical MLE4 method: no need of lookup tables to correct biases, significant noise reduction on all geophysical variables especially the significant wave height, and performance of inversion over a large set of echo shapes, resulting from standard oceanic scenes as well as highly specular conditions such as over bloom or sea ice. [ABSTRACT FROM AUTHOR]

Sea ice has a significant effect on climate change and ship navigation. Hence, it is crucial to draw sea-ice maps that reflect the geographical distribution of different types of sea ice. Many automatic sea-ice classification methods using synthetic aperture radar (SAR) images are based on the polarimetric characteristics or image texture features of sea ice. They either require professional knowledge to design the parameters and features or are sensitive to noise and condition changes. Moreover, ice changes over time are often ignored. In this article, we propose a new SAR sea-ice image classification method based on a combined learning of spatial and temporal features, derived from residual convolutional neural networks (ResNet) and long short-term memory (LSTM) networks. In this way, we achieve automatic and refined classification of sea-ice types. First, we construct a seven-type ice data set according to the Canadian Ice Service ice charts. We extract spatial feature vectors of a time series of sea-ice samples using a trained ResNet network. Then, using the feature vectors as inputs, the LSTM network further learns the variation of the set of sea-ice samples with time. Finally, the extracted high-level features are fed into a softmax classifier to output the most recent ice type. Taking both spatial features and time variation into consideration, our method can achieve a high classification accuracy of 95.7% for seven ice types. Our method can automatically produce more objective sea-ice interpretation maps, allowing detailed sea-ice distribution and improving the efficiency of sea-ice monitoring tasks. [ABSTRACT FROM AUTHOR]

In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type classification algorithm on a comprehensive data set of ALOS-2 PALSAR-2 fully polarimetric images acquired with a range of incidence angles and during different environmental conditions. The variability within the data set means that it is ideal for making a novel assessment of the robustness of the sea ice classification, investigating the intraclass variability, the seasonal variations, and the incidence angle effect on the sea ice classification results. The images coincide with two different Arctic campaigns in 2015: the Norwegian Young Sea Ice Cruise 2015 (N-ICE2015) and the Polarstern’s (PS92) Transitions in the Arctic Seasonal Sea Ice Zone (TRANSSIZ). We find that it is essential to take into account seasonality and intraclass variability when establishing training data for machine learning-based algorithms though moderate differences in incidence angle are possible to accommodate by the classifier during the dry and cold winter season. We also conclude that the incidence angle dependence of backscatter for a given ice type is consistent for different Arctic regions. [ABSTRACT FROM AUTHOR]

Changes in the calving front position of marine-terminating glaciers strongly influence the mass balance of glaciers, ice caps, and ice sheets. At present, quantification of frontal position change primarily relies on time-consuming and subjective manual mapping techniques, limiting our ability to understand changes to glacier calving fronts. Here we describe a newly developed automated method of mapping glacier calving fronts in satellite imagery using observations from a representative sample of Greenland’s peripheral marine-terminating glaciers. Our method is adapted from the 2-D wavelet transform modulus maxima (WTMM) segmentation method, which has been used previously for image segmentation in biomedical and other applied science fields. The gradient-based method places edge detection lines along regions with the greatest intensity gradient in the image, such as the contrast between glacier ice and water or glacier ice and sea ice. The lines corresponding to the calving front are identified using thresholds for length, average gradient value, and orientation that minimize the misfit with respect to a manual validation data set. We demonstrate that the method is capable of mapping glacier calving fronts over a wide range of image conditions (light to intermediate cloud cover, dim or bright, mélange presence, etc.). With these time series, we are able to resolve subseasonal to multiyear temporal patterns as well as regional patterns in glacier frontal position change. [ABSTRACT FROM AUTHOR]

In order to improve the understanding of the dynamical deformation processes of sea ice in the seasonal ice zone (SIZ), measures to detect deformed ice were developed and validated using satellite L-band synthetic aperture radar (ScanSAR) images for the southern Sea of Okhotsk. To approach, sea ice was categorized into three ice types, typical of the sea ice in this region: nilas (thin level), pancake ice (thin rough), and deformed ice (thick rough), and then the measures to classify into these categories were developed using ALOS/Phased Array type L-band Synthetic Aperture Radar (PALSAR) as a function of backscatter coefficients at HH polarization ($\sigma _{\text {HH}}^{0}$) and incidence angle ($\theta _{i}$), based on the field observations. Comparative analysis confirmed that PALSAR can detect deformed ice more efficiently than RADARSAT-2 (C-band SAR). The temporal evolution of the area, judged as deformed ice from these measures, shows significant variability with both time and space, and deformed ice regions appear in relatively linear alignments with a width of a few tens of kilometers in the inner ice pack region, consistent with ice drift convergence. To confirm the results, PALSAR-2 images at HH and HV polarizations were examined as a function of $\theta _{i}$ , based on the four-year field observations in the same area. The results revealed that $\sigma _{\text {HH}}^{0}$ and $\sigma _{\text{HV}}^{0}$ are both subject to floe sizes as well as deformed ice, and $\sigma _{\text{HV}}^{0}$ is more sensitive. This indicates that care should be taken when applying these measures to the ice areas where significantly small floes are dominant like the marginal ice zone. [ABSTRACT FROM AUTHOR]

We consider a conceptual approach for the applicability of airborne weather radar for the sea ice/water discrimination and estimation of the sea ice age. The sea ice/water discrimination method suited for the airborne weather radar utilization is based on finding the minimum statistical distance of the measured normalized radar cross sections within a wide azimuth sector to the geophysical model functions of the sea ice and water, respectively. The sea ice age classification is based on the comparison of the normalized radar cross section value at a given incidence angle to the lower normalized radar cross section boundaries for the first-, second-, and multi-year ice. Implementation of the proposed methodology is considered for the C-band airborne weather radar operated in the ground mapping mode as a scatterometer scanning in a wide azimuth sector up to ±100°. We show explicitly that the proposed approach provides the enhancement of the conventional airborne weather radar functionality for the sea ice/water discrimination and sea ice age estimation either using stand-alone or joint measurements.

Currently, reflectometry-based sea ice detection methods rely on observables extracted from delay-Doppler maps (DDM)s, which are sensitive to ice in the specular zone. Due to the size of the glistening zone, the transition from open seas to sea ice in the specular zone can take up to 10 s for satellite platforms and no methods exist that can process this. In this article, using DDMs collected by TechDemoSat-1, we demonstrate that this transition is comprised of a response that is fixed in the spatial domain, at the ice edge, and moving in the delay-Doppler domain. This is the first observation of persistent nonspecular coherent reflections from sea ice. The delay-Doppler trajectory of the ice response is shown to correspond with a point that is located on the ambiguity free line. Furthermore, the response is point-like as it suffers from delay and Doppler walk suggesting that it originates from a small spatial footprint, i.e., the first (few) Fresnel zone(s). Exploiting these facts, we then propose a technique that integrates the ice response in the spatial domain after preprocessing. This results in the edges of the ice sheet being emphasized as all of the power received during the transition phase maps to the edge of the sheet. We also propose to compensate for the delay-Doppler walk during preprocessing by modifying Woodward's ambiguity function when deconvolution is performed.

Automatic and visual sea ice classification of SAR imagery is impeded by the incidence angle dependence of backscatter intensities. Knowledge of the angular dependence of different ice types is therefore necessary to account for this effect. While consistent estimates exist for HH polarization for different ice types, they are lacking HV polarization data, especially for multiyear sea ice. Here we investigate the incidence angle dependence of smooth and rough/deformed first-year and multiyear ice of different ages for wintertime dual-polarization Sentinel-1 C-band SAR imagery in the Beaufort Sea. Assuming a linear relationship, this dependence is determined using the difference in incidence angle and backscatter intensities from ascending and descending images of the same area. At cross-polarization rough/deformed first-year sea ice shows the strongest angular dependence with -0.11 dB/1° followed by multiyear sea ice with -0.07 dB/1°, and old multiyear ice (older than three years) with -0.04 dB/1°. The noise floor is found to have a strong impact on smooth first-year ice and estimated slopes are therefore not fully reliable. At co-polarization, we obtained slope values of -0.24, -0.20, -0.15, and -0.10 dB/1° for smooth first-year, rough/deformed first-year, multiyear, and old multiyear sea ice, respectively. Furthermore, we show that imperfect noise correction of the first subswath influences the obtained slopes for multiyear sea ice. We demonstrate that incidence angle normalization should not only be applied to co-polarization but should also be considered for cross-polarization images to minimize intra ice type variation in backscatter intensity throughout the entire image swath.

Uncertainty in snow properties impacts the accuracy of Arctic sea ice thickness estimates from radar altimetry. On first-year sea ice (FYI), spatiotemporal variations in snow properties can cause the Ku-band main radar scattering horizon to appear above the snow/sea ice interface. This can increase the estimated sea ice freeboard by several centimeters, leading to FYI thickness overestimations. This article examines the expected changes in Ku-band main scattering horizon and its impact on FYI thickness estimates, with variations in snow temperature, salinity, and density derived from ten naturally occurring Arctic FYI Cases encompassing saline/nonsaline, warm/cold, simple/complexly layered snow (4-45 cm) overlying FYI (48-170 cm). Using a semi-empirical modeling approach, snow properties from these Cases are used to derive layer-wise brine volume and dielectric constant estimates, to simulate the Ku-band main scattering horizon and delays in radar propagation speed. Differences between modeled and observed FYI thickness are calculated to assess sources of error. Under both cold and warm conditions, saline snow covers are shown to shift the main scattering horizon above from the snow/sea ice interface, causing thickness retrieval errors. Overestimates in FYI thicknesses of up to 65% are found for warm, saline snow overlaying thin sea ice. Our simulations exhibited a distinct shift in the main scattering horizon when the snow layer densities became greater than 440 kg/m3, especially under warmer snow conditions. Our simulations suggest a mean Ku-band propagation delay for snow of 39%, which is higher than 25%, suggested in previous studies.

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments.

Global navigation satellite system reflectometry (GNSS-R) has found many applications in the field of Earth observation including ocean wind-speed detection, ice altimetry, soil moisture monitoring, and more. The main focus of GNSS-R research to date has been on forward-scattered reflections, but theoretical work has proposed a backscattering regime and associated new application opportunities, including marine target detection. This article discusses the methods and results of processing the U.K. TechDemoSat-1 raw data collections in a backscattering regime for the first time, with initial results from sea ice datasets presented. The research has also identified a key problem with the backscatter method-for certain geometries the power from the specular point (forward scattered) may contaminate the data. The theory behind this and a method for predicting such occurrences is also discussed.

This article presents the FMPL-2 on board the FSSCat mission, the 2017 ESA Small Sentinel Satellite Challenge and overall Copernicus Masters Competition winner. FMPL-2 is a passive microwave instrument based on a software-defined radio that implements a conventional global navigation satellite system-Reflectometer and an L-band radiometer, occupying 1 U of a 6 U CubeSat. The article describes the FSSC at mission context, the payload design and implementation phases, the tests results in a controlled environment, and finally the calibration algorithms applied to the downloaded data in order to extract the appropriate geophysical parameters: sea-ice coverage, sea-ice thickness (SIT), and low-resolution soil moisture. This article covers the overall payload design, from a high-level block diagram down to single-component specifications from both hardware and software points of view. The main block of the instrument is based on the combination of an FPGA, which virtualizes a dual-core ARM processor, where most of the calculus are performed, and a software-defined radio module, in charge of I/Q data demodulation. The article also explains the design and implementation of a signal conditioning board required for the correct operation and calibration of both instruments.

Globally gridded sea surface temperatures (SSTs) provide key data for long-term monitoring of the stability of satellite data. Despite apparent limitations, accurate hyperspectral data can provide useful independent information to critique the stability of global SST products on the annual-to-decadal time scale. We compared data from atmospheric infrared sounder (AIRS) on EOS Aqua and Crosstrack Interferometer Sounder (CrIS) on SNPP with the SST products from NOAA/NESDIS [real time global (RTG)], the Canadian Meteorological Centre (CMC), and the U.K. Met Office [Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)]. For the 2013–2019 period, the overall standard deviation of the difference between AIRS and the RTG was 0.55 K, with an increasing trend over time. In contrast, the standard deviation of the difference between the AIRS, the CMC, and the OSTIA dropped steadily to below 0.4 K, a level previously seen only in SST products relative to independent buoy data. Unexplained biases between the observed and the gridded SSTs at the 100-mK level are consistent with the already existing estimates of the AIRS and CrIS absolute calibration accuracy. However, the AIRS and CrIS observations both show artifacts in all three SST products, increasing with distance from the equator, with the CMC artifacts being the smallest. Even with the CMC, a trend of 4 mK per year relative to AIRS and CrIS was observed between 2013 and 2019 for the 30S–30N oceans. Investigation of the underlying causes of the observed discrepancies requires further work. [ABSTRACT FROM AUTHOR]

The carrier phase measurements of coherent Global Navigation Satellite Systems Reflectometry (GNSS-R)signals have demonstrated the potential for high-accuracy altimetry applications. However, the coherent components in the reflected signal are often accompanied by noncoherent scattered signals, which may cause numerous cycle slips in the conventional open-loop (OL) tracking and postprocessing approach. These cycle slips can lead to meter-level errors in the altimetry retrieval. This article presents a simultaneous cycle slip and noise filtering (SCANF) method for dual-frequency GNSS-R phase measurements generated by OL tracking. The performance of this method is demonstrated using real GNSS-R phase measurements recorded by receivers on board Spire’s low-Earth orbit (LEO) satellites. The retrieved sea surface height anomaly (SSHA) has an average root mean square (rms) of 7.3 cm for sea and sea ice surface reflections relative to the mean sea surface (MSS). The technique presented in this article is also applicable to GNSS carrier phase measurements obtained under other challenging conditions, such as signals propagating through ionosphere plasma structures, radio occultation (RO) signals traversing lower troposphere, and multipath-rich environments. [ABSTRACT FROM AUTHOR]

The desire to conduct research in the Arctic on an ever-larger spatiotemporal scale has led to the development of long-range autonomous underwater vehicles (AUVs), such as the Autosub Long-Range 1500 (ALR1500). While these platforms open up a world of new applications, their actual use is limited in GPS-denied environments since self-contained navigation remains yet unavailable. In response, this study evaluates whether terrain-aided navigation (TAN) can enable multimonth deployments using basic navigation sensors and sparse bathymetric maps. To evaluate the potential, ALR1500 undertakes a hypothetical science-driven mission from Svalbard (Norway) to Point Barrow (Alaska, USA) under the sea ice (a mission over 3200 km). Therefore, a simulated environment is developed, which integrates a state-of-the-art model of water circulation, error models for heading estimation at high latitudes, and an Arctic bathymetric map. Recognizing that this map is constructed based on sparse depth measurements and interpolation techniques, a bathymetric uncertainty model is developed. The performance of the TAN algorithm is examined with respect to the type of the heading sensor utilized and a range of vertical map distortions, calculated using the developed bathymetric uncertainty model. Simulations show that unaided navigation experiences an error of hundreds of kilometers, whereas TAN provides acceptable accuracy given a moderate map distortion. By degrading the quality of the map further, it appears that the navigation filter may diverge when traversing large regions subject to interpolation. Therefore, a rapidly-exploring random tree star algorithm is used to design a new path such that the AUV traverses reliable and rich in topographic information areas. [ABSTRACT FROM AUTHOR]

Fronts between Arctic- and Atlantic-origin waters are characterized by strong lateral gradients in temperature and salinity. Ocean processes associated with fronts are complex with considerable space and time variability. Therefore, resolving the processes in frontal zones by observation is challenging but important for understanding the associated physical–biological interactions and their impact on the marine ecosystem. The use of autonomous robotic vehicles and in situ data-driven sampling can help improve and augment the traditional sampling practices, such as ships and profiling instruments. Here, we present the development and results of using an autonomous agent for detection and sampling of an Arctic front, integrated on board an autonomous underwater vehicle. The agent is based on a subsumption architecture implemented as behaviors in a finite-state machine. Once a front is detected, the front tracking behavior uses observations to continuously adapt the path of the vehicle to perform transects across the front interface. Following successful sea trials in the Trondheimsfjord, the front-tracking agent was deployed to perform a full-scale mission near 82 $^{\circ}$ N north of Svalbard, close to the sea ice edge. The agent was able to detect and track an Arctic frontal feature, performing a total of six crossings while collecting vertical profiles in the upper 90 m of the water column. Measurements yield a detailed volumetric description of the frontal feature with high resolution along the frontal zone, augmenting ship-based sampling that was run in parallel. [ABSTRACT FROM AUTHOR]

This study is based on daily sea-ice concentration (SIC) data collected from the National Snow and Ice Data Center between 1989 and 2018. We analyzed the concentration, total sea ice extent (SIE), multiyear ice extent, and first- and second-year ice extent of Polar (Arctic and Antarctic) sea ice. The results showed that, over the analyzed 30 year span, the spatial distribution of SIC decreased in the Arctic, while it showed high regional variability within the Antarctic. The total SIE decreased in the polar regions at a rate of − 0.051 × 106 km2 yr−1 (−2.07% per decade) and the multiyear ice extent at a rate of − 0.086 × 106 km2 yr−1 (−12.31% per decade), while the first- and second-year ice extent increased at a rate of 0.054 × 106 km2 yr−1 (2.13% per decade). [ABSTRACT FROM AUTHOR]

Analysis of high-resolution imagery acquired by the Digital Mapping System during annual, late-winter NASA Operation IceBridge surveys of Arctic sea ice between 2010 and 2018 reveals that pressure ridge sail heights (HS) vary regionally and interannually. We find distinct differences in HS distributions between the central Arctic (CA) and the Beaufort/Chukchi Seas region. Our results show that differences with respect to ice type occur within the tails of the HS distributions and that the 95th and 99th percentiles of HS are strong indicators of the predominant ice type in which the pressure ridge formed. During the first part of the study period HS increased, with the largest sails observed in the winters of 2015 and 2016, after which HS declined, suggesting that the most heavily deformed sea ice may have drifted beyond the area surveyed and exited the CA. Our analysis of the interannual and regional variability in sea ice deformation in the western Arctic during the last decade provides an improved understanding of sail height that will help advance ridge parameterizations in sea ice models. [ABSTRACT FROM AUTHOR]