Search Results (53)

Climate change in the Arctic is causing significant declines in sea ice extent and thickness. This study investigated lab-grownsea ice thickness using Linear Regression and three Machine Learning algorithms: Decision Tree, Random Forest, and Fully Connected Neural Network. To comprehensively track thin sea ice growth using various parameters, a combination of up to 13 radar and physical parameters including surface-based C-band NRCS values in VV, HH, and HV polarizations, air temperature, surface temperature, Cumulative Freezing Degree Moments, humidity, wind speed, surface cover salinity, ice surface salinity, bulk ice salinity, frost flower height and snow depth were input to the four multivariate models in two time series datasets. The results showed that Random Forest was the superior model, with =0.01 cm, for thicknesses of 1–8 cm and 27–47 cm. Using the Permutation Importance method, the role of the employed parameters in the thickness prediction process were ranked and showed that the key parameters were Cumulative Freezing Degree Moment, salinity parameters (surface cover, ice surface, and bulk ice salinities), and C-band co-polarized radar backscattering. The results of this study enhance thickness prediction capacity and accuracy, while providing insights for future research and real-time sea ice thickness prediction in Arctic regions. Full article

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article

Currently, the performance of sea ice thickness (SIT) data retrieved from multisource satellite products in the Arctic seasonal ice zones remains unclear. This study presented the spatiotemporal intercomparison and evaluation of satellite data, including the latest versions of Soil Moisture and Ocean Salinity (SMOS), CryoSat-2, combined CryoSat-2 and SMOS (CS2SMOS), and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), specifically focusing on area with mean SIT below 0.5 m. Five evaluation datasets were used. During 2010–2023, SMOS had the smallest mean SIT, with CryoSat-2 showing the largest mean SIT. During 2018–2023, with the inclusion of ICESat-2, SMOS still showed the smallest mean SIT. CryoSat-2 exhibited the largest mean SIT, followed by ICESat-2, CS2SMOS ranked third. Evaluation results indicated that four satellite products generally underestimated SIT. In two periods, SMOS consistently exhibited the weakest performance, which showed a large gap from what was expected in previous studies. In contrast, CS2SMOS demonstrated the highest alignment with five evaluation datasets during 2010–2023, indicating the best overall performance. During 2018–2023, ICESat-2 exhibited the best overall performance with two evaluation datasets. This study refreshes previous knowledge about SMOS in the seasonal ice zones and contributes to further improvements in SIT retrieval. Full article

The detection of thin-ice thickness using satellite microwave radiometers is a strong tool for estimating sea-ice production in coastal polynyas, which leads to dense water formation driving ocean thermohaline circulation. Thin-ice areas are classified into two ice types: active frazil, comprising frazil ice and open water, and thin solid ice, areas of relatively uniform thin ice. A thin-ice algorithm for AMSR-E has been developed to classify these two ice types and estimate ice thickness of <20 cm. In this study, we validate the applicability of the algorithm to the successor, AMSR2, using validation data of ice types identified from Sentinel-1 and ice thickness derived from MODIS. The validation results show an ice-type misclassification rate of ~3% and mean absolute errors in ice thickness of 2.0 cm and 5.0 cm for active frazil and thin solid ice, respectively. These values are similar to those for AMSR-E, indicating that the thin-ice algorithm can be applied to AMSR2. Further validations with the moored ADCP backscattering data capturing underwater frazil ice signals demonstrate that the algorithm can accurately distinguish between two ice types and effectively detect deep-penetrating frazil ice. The AMSR2 thin-ice thickness data has been released as a JAXA research product. Full article

There now exists two decades of basin-wide coverage of Arctic sea ice from three dedicated polar-orbiting altimetry missions (ICESat, CryoSat-2, and ICESat-2) launched by NASA and ESA. Here, we review our retrieval approaches and discuss the composite record of Arctic ice thickness (2003–2023) after appending two more years (2022–2023) to our earlier records. The present availability of five years of snow depth estimates—from differencing lidar (ICESat-2) and radar (CryoSat-2) freeboards—have benefited from the concurrent operation of two altimetry missions. Broadly, the dramatic volume loss (5500 km³) and Arctic-wide thinning (0.6 m) captured by ICESat (2003–2009), primarily due to the decline in old ice coverage between 2003 and 2007, has slowed. In the central Arctic, away from the coasts, the CryoSat-2 and shorter ICESat-2 records show near-negligible thickness trends since 2007, where the winter and fall ice thicknesses now hover around 2 m and 1.3 m, from a peak of 3.6 m and 2.7 m in 1980. Ice volume production has doubled between the fall and winter with the faster-growing seasonal ice cover occupying more than half of the Arctic Ocean at the end of summer. Seasonal ice behavior dominates the Arctic Sea ice’s interannual thickness and volume signatures. Full article

There is a heightened risk of an oil spill occurring in the Arctic, as climate change driven sea ice loss permits an increase in Arctic marine transportation. The ability to detect an oil spill and monitor its progression is key to enacting an effective response. Microwave scatterometer systems may be used detect changes in sea ice thermodynamic and physical properties, so we examined the potential of C-band polarimetric radar for detecting diesel fuel beneath a thin sea ice layer. Sea ice physical properties, including thickness, temperature, and salinity, were measured before and after diesel addition beneath the ice. Time-series polarimetric C-band scatterometer measurements monitored the sea ice evolution and diesel migration to the sea ice surface. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (conformity coefficient (μ), copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. We delineated three stages, with distinct NRCS and polarimetric results, which could be connected to the thermophysical state and the presence of diesel on the surface. Stage 1 described the initial formation of sea ice, while in Stage 2, we injected 20L of diesel beneath the sea ice. No immediate response was noted in the radar measurements. With the emergence of diesel on the sea ice surface, denoted by Stage 3, the NRCS dropped substantially. The largest response was for VV and HH polarizations at 20° incidence angle. Physical sampling indicated that diesel emerged to the surface of the sea ice and trended towards the tub edge and the polarimetric scatterometer was sensitive to these physical changes. This study contributes to a greater understanding of how C-band frequencies can be used to monitor oil products in the Arctic and act as a baseline for the interpretation of satellite data. Additionally, these findings will assist in the development of standards for oil and diesel fuel detection in the Canadian Arctic in association with the Canadian Standards Association Group. Full article

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 ($T_B$) 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 $T_B$ 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 $T_B$ 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. Full article

Arctic sea ice prediction is of great practical significance in facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily-scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the models’ capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the prediction accuracy of the four models significantly surpasses the CMIP6 model in three prospective climate scenarios (SSP126, SSP245, and SSP585). Of the four models, the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance than the PredRNN-multi model in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction, and meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin-ice region at the edge of the sea ice. Full article

Accurate retrieval of ice surface temperature (IST) over the Arctic ice-water mixture zone (IWMZ) is significantly essential for monitoring the change of the polar sea ice environment. Previous researchers have focused on evaluating the accuracy of IST retrieval in pack ice regions, possibly on account of the availability of in situ measurement data. Few of them have assessed the accuracy of IST retrieval on IWMZ. This study utilized Landsat 8/TIRS and Operation IceBridge observations (OIB) to evaluate the accuracy of the current IST retrieval method in IWMZ and proposed an adjustment method for improving the overall accuracy. An initial comparison shows that Landsat 8 IST and OIB IST have minor differences in the pack ice region with RMSE of 0.475 K, MAE of 0.370 K and cold bias of −0.256 K. In the thin ice region, however, the differences are more significant, with RMSE of 0.952 K, MAE of 0.776 K and warm bias of 0.703 K. We suggest that this phenomenon is because the current ice-water classification method misclassified thin ice as water. To address this issue, an adjusted method is proposed to refine the classification of features within the IWMZ and thus improve the accuracy of IST retrieval using Landsat 8 imagery. The results demonstrate that the accuracy of the retrieved IST in the two cases was improved in the thin ice region, with RMSE decreasing by about 0.146 K, Bias decreasing by about 0.311 K, and MAE decreasing by about 0.129 K. After the adjustment, high accuracy was achieved for both pack ice and thin ice in IWMZ. Full article

With the goal of automatic sea ice mapping during the summer sea ice melt cycle, this study involved designing a fully automatic sea ice segmentation method based on a deep learning semantic segmentation network applicable to summer SAR images, which achieved high accuracy and the fully automatic extraction of sea ice segmentation during the summer ice melt cycle by optimizing the process, improving the pixel-level semantic segmentation network, and introducing high-resolution sea ice concentration features. Firstly, a convolution-based, high-resolution sea ice concentration calculation method is proposed and was applied to the deep learning task. Secondly, the proposed DF-UHRNet network was improved upon by designing high- and low-level fusion modules, introducing an attention mechanism, and reducing the number of convolution layers and other operations, and it can effectively fuse high- and low-scale semantic features and global contextual information based on reducing the overall number of network parameters, enabling it to achieve pixel-level classification. The results show that this method meets the needs associated with the automatic mapping and high-precision classification of thin ice, one-year ice, open water, and multi-year ice and effectively reduces the model size. Full article

Since the mid-1990s, there has been a marked decrease in the sea ice extent (SIE) in the Arctic Ocean. After reaching an absolute minimum in September 2012, the seasonal variations in the SIE have settled at a new level, which is almost one-quarter lower than the average climatic norm of 1979–2022. Increased melting and accelerated ice export from marginal seas ensure an increase in the open water area, which affects the lower atmosphere and the surface layer of the ocean. Scientists are cautiously predicting a transition to a seasonally ice-free Arctic Ocean as early as the middle of this century, which is about 50 years earlier than was predicted just a few years ago. Such predictions are based on the fact that the decrease in sea ice extent and ice thinning that occurred at the beginning of this century, initially caused by an increase in air temperature, triggered an increase in the thermal and dynamic contribution of the ocean to the further reduction in the ice cover. This paper reviews published evidence of such changes and discusses possible mechanisms behind the observed regional anomalies of the Arctic Sea ice cover parameters in the last decade. Full article

This paper presents recent development and applications of thermal lens microscopy (TLM) and beam deflection spectrometry (BDS) for the analysis of water samples and sea ice. Coupling of TLM detection to a microfluidic system for flow injection analysis (μFIA) enables the detection of microcystin-LR in waters with a four samples/min throughput (in triplicate injections) and provides an LOD of 0.08 µg/L which is 12-times lower than the MCL for microcystin-LR in water. μFIA-TLM was also applied for the determination of total Fe and Fe(II) in 3 µL samples of synthetic cloudwater. The LODs were found to be 100 nM for Fe(II) and 70 nM for total Fe. The application of µFIA-TLM for the determination of ammonium in water resulted in an LOD of 2.3 µM for injection of a 5 µL sample and TLM detection in a 100 µm deep microfluidic channel. For the determination of iron species in sea ice, the BDS was coupled to a diffusive gradient in the thin film technique (DGT). The 2D distribution of Fe(II) and total Fe on DGT gels provided by the BDS (LOD of 50 nM) reflected the distribution of Fe species in sea ice put in contact with DGT gels. Full article

Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an $R^2$ (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, r = 0.58, p < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, $r^s$ = 0.66, p < 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas. Full article

The surface effective emissivities of Arctic sea ice are calculated using Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements. These emissivities are analyzed for stable winter conditions during the months of January–May and November and December of 2020 for several main sea ice types defined with the sea ice maps of the Arctic and Antarctic Research Institute (AARI). The sea ice emissivities are derived from the AMSR2 data using the radiation transfer model for a non-scattering atmosphere and ERA5 reanalysis data. The emissivities are analyzed only for areas of totally consolidated sea ice of definite types. Probability distribution functions are built for the emissivities and their functions for such sea ice types as nilas, young ice, thin first-year (FY) ice, medium FY ice, thick FY ice and multi-year ice. The emissivity variations with frequency are estimated for each of the considered sea ice type for all seven months. The variations are calculated both for the emissivities and for their gradients at the AMSR2 channel frequencies. Obtained emissivities turned out to be generally lower than reported previously in scientific studies, whereas the emissivity variability values proved to be much larger than was known before. For all FY ice types, at all the frequencies, an increase in the emissivity at the beginning of winter and its decrease by the end of May are observed. The emissivity gradients demonstrate noticeable decreases with sea ice age, and their values may be used in sea ice classification algorithms based on the AMSR2 data. Full article

The relatively stable conditions of the sea ice cover in the Antarctic, observed for almost 40 years, seem to be changing recently. Therefore, it is essential to provide sea ice thickness (SIT) and volume (SIV) estimates in order to anticipate potential multi-scale changes in the Antarctic sea ice. For that purpose, the main objectives of this work are: (1) to assess a new sea ice freeboard, thickness and volume altimetry dataset over 2003–2020 and (2) to identify first order impacts of the sea ice recent conditions. To produce these series, we use a neuronal network to calibrate Envisat radar freeboards onto CryoSat-2 (CS2). This method addresses the impacts of surface roughness on Low Resolution Mode (LRM) measurements. During the 2011 common flight period, we found a mean deviation between Envisat and CryoSat-2 radar freeboards by about 0.5 cm. Using the Advanced Microwave Scanning Radiometer (AMSR) and the dual-frequency Altimetric Snow Depth (ASD) data, our solutions are compared with the Upward looking sonar (ULS) draft data, some in-situ measurement of the SIMBA campaign, the total freeboards of 6 Operation Ice Bridge (OIB) missions and ICESat-2 total freeboards. Over 2003–2020, the global mean radar freeboard decreased by about −14% per decade and the SIT and SIV by about −10% per decade (considering a snow depth climatology). This is marked by a slight increase through 2015, which is directly followed by a strong decrease in 2016. Thereafter, freeboards generally remained low and even continued to decrease in some regions such as the Weddell sea. Considering the 2013–2020 period, for which the ASD data are available, radar freeboards and SIT decreased by about −40% per decade. The SIV decreased by about −60% per decade. After 2016, the low SIT values contrast with the sea ice extent that has rather increased again, reaching near-average values in winter 2020. The regional analysis underlines that such thinning (from 2016) occurs in all regions except the Amundsen-Bellingshausen sea sector. Meanwhile, we observed a reversal of the main regional trends from 2016, which may be the signature of significant ongoing changes in the Antarctic sea ice. Full article