Sea ice concentration (SIC) has been monitored with passive microwave (PM) observations for decades. Various techniques have been developed for its improvement. While techniques such as weather filters are commonly used, the necessity of combing radiative transfer model (RTM)-based atmospheric correction and dynamic tie points (DTP) remains an open question, particularly for near-90 GHz algorithm. This study investigates their respective influence on SIC retrieval using the FY-3D Microwave Radiation Imager (MWRI) data in 2019. The original and atmospherically corrected Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) algorithm (ASI and ASI2, respectively) are used in combination with fixed tie points (FTP) and DTP, resulting in four sets of ice concentration retrievals, namely ASI-FTP, ASI-DTP, ASI2-FTP, and ASI2-DTP. They are inter-compared with three PM-based ice concentration products and evaluated with a synthetic aperture radar (SAR)-based ice/water classification product and 20 clear-sky Moderate Resolution Imaging Spectroradiometer (MODIS) images from February to July 2019. The ASI2-based ice concentrations are overall higher and perform better, with the root mean square error (RMSE) and bias reduced by 5.4%–7.4% and 7.2%–8.0%, respectively. In comparison, the use of DTP has varying performances depending on the tie points extraction procedure. Good tie points work similarly to the atmospheric correction in mitigating SIC underestimations. The combined use of both varies substantially with seasons. During summer, it well captures the seasonal variability of tie points and effectively mitigates the atmospheric influence, thus significantly improving the retrievals. This highlights the necessity of combining both techniques for near-90 GHz algorithm, especially for summer.

Leads are almost linear fractures within the ice pack, which are commonly observed in polar regions. In wintertime, leads promote energy flux from the underlying ocean to the atmosphere. Synthetic aperture radar (SAR) can monitor leads at a finer spatial resolution than other spaceborne datasets, regardless of solar illumination and atmospheric conditions. However, the SAR-based lead detection methods proposed to date are restricted to some specific areas, instead of the entire Arctic. In this article, we present a generalized deep learning-based approach for automatic sea ice lead detection (SILDET) in the Arctic wintertime using Sentinel-1 SAR images. The validation results show that SILDET has the capability of detecting open and frozen leads at different stages of development. Compared with the visual interpretation of Sentinel-1 images, the overall detection accuracy is 97.80% and the Kappa coefficient is 0.88. The lead map of a regional study obtained from SILDET was compared to that from a previous SAR-based lead detection method and a lead dataset based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The lead map was also validated using Sentinel-2 images. The result shows that SILDET can provide a more detailed distribution of leads and a better estimation of lead width and area. SILDET was applied to present the Arctic-wide lead distribution from January to April 2023 with a spatial resolution of 40 m. The Arctic-wide lead width distribution follows a power law with an average exponent of 1.65. The SILDET approach can be expected to provide long-term high-resolution lead distribution records.

The neutral form drag coefficient is an important parameter when estimating surface turbulent fluxes over Arctic sea ice. The form drag caused by surface features ($C_{\text {dn},\text {fr}}$) dominates the total drag in the winter, but long-term pan-Arctic records of$C_{\text {dn},\text {fr}}$are still lacking for Arctic sea ice. In this study, we first developed an improved surface feature detection algorithm and characterized the surface features (including height and spacing) over Arctic sea ice during the late winter of 2009–2019 using the full-scan laser altimeter data obtained in the Operation IceBridge mission.$C_{\text {dn},\text {fr}}$was then estimated using an existing parameterization scheme. This was followed by applying a satellite-derived backscatter coefficient (${\sigma }_{\text {vv}}^{o}$) to$C_{\text {dn},\text {fr}}$regression model to extrapolate, for the first time,$C_{\text {dn},\text {fr}}$to the pan-Arctic scale for the entire winter season over two decades (from 1999 to 2021). We found that the surface features have a larger height and smaller spacing over multiyear ice (1.15 ± 0.21 and 142 ± 49 m) than over first-year ice (0.90 ± 0.16 and 241 ± 129 m). The monthly mean$C_{\text {dn},\text {fr}}$increases through the winter from$0.2\times 10^{-3}$in November to 0.4–$0.5\times 10^{-3}$in April. The central Arctic has the largest$C_{\text {dn},\text {fr}}$(up to$2\times 10^{-3}$) but experienced a drop of ~50% in the period from 2001/2002 to 2008/2009. The interannual fluctuations in$C_{\text {dn},\text {fr}}$are strongly linked to the variability of sea ice thickness and deformation, and the latter has become increasingly important for$C_{\text {dn},\text {fr}}$since 2009.

The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase, with the current performance evaluation. In this study, we provide results update on RCM performance for selected SAR applications. The RCM was designed to address three core applications-disaster management, maritime surveillance, and ecosystem monitoring. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba. Visual analysis of coincident RCM compact polarimetric and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87.

Lead is an important feature of the Arctic ice cover, with possible contents of thin ice /or open water. In this letter, we present an algorithm for lead detection based on brightness temperature observations from a single thermal infrared channel of MERSI-II onboard the Chinese FY-3D satellite. Lead contents is classified into open water and thin ice with support information from Sentinel-1 SAR data. Results are evaluated based on visual interpretation of MERSI-II TIR (Thermal infrared) and Sentinel-2 NIR (Near Infrared) data. The accuracy is found to be 85.6% for lead detection and 67% and 52% for thin ice and open water within the lead, respectively.

As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in the Arctic. Microwave scatterometers in the Ku-band and C-band can provide daily observations of sea ice type. However, their comparative capabilities in mapping ice type have not been thoroughly evaluated. We present a systematic intercomparison of the backscatter signature in VV polarization (${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$) and the sea ice classification from three scatterometer systems using the same ice classification approach. The systems are the Ku-band quick scatterometer (QSCAT) and the newly launched Chinese rotating fan-beam scatterometer (RFSCAT) and the C-band advanced scatterometer (ASCAT). Three freezing seasons are used, i.e., 2007/08 and 2008/09 for the QSCAT/ASCAT comparison and 2019/20 for the RFSCAT/ASCAT comparison. With reference to ASCAT,${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$bias between QSCAT and RFSCAT results from their different incidence angles. A continuous declining trend of${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$from MYI and FYI is observed during winter, with a greater difference between MYI and FYI in the Ku-band. The MYI and FYI extent derived from QSCAT/RFSCAT is highly consistent with that derived from ASCAT, with a difference less than 7% and 3% for MYI and FYI, respectively. The overall accuracy (OA) is around 77% and 80% for the RFSCAT results and ASCAT results, respectively, compared with Sentinel-1 SAR images. The classification results show high consistency (81%–89%) with ice charts from the Canadian Ice Service. The incorporation of${\mathrm {Tb}}_{36\mathrm {h}}$from AMSR-E/AMSR2 improves the OA of the classification when using ASCAT or RFSCAT by 7%–11%.

Synthetic aperture radar (SAR) is a valuable tool for lake ice monitoring. The recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric (FP) SAR for operational ice applications, including lake ice. Given its enhanced radar target information compared with conventional SAR systems over wider swath coverage compared with FP SAR, CP systems could play important role in the new generation of Earth observation systems. Herein, we study the evolution of CP SAR parameters from simulated CP SAR data in relation to early ice growth. Focus of the study is on four lakes located in Cornwallis Island, Canadian Central Arctic. We adopt parameters extracted from dual circular polarimetric and right circular transmit, linear (horizontal and vertical) receive configurations. In this study, we consider the ice thickness calculated from an established empirical model. Meteorological and ice climatological data were used to support the analysis. Results demonstrated a potential connection between a number of CP parameters and lake ice growth. Furthermore, we were able to highlight the relationship between the density of air bubbles in ice layer and the intensity of volume scattering mechanism, leading to the identification of lakes with increased gas production activities. Thus, differences between lakes in terms of density of air bubbles were detected and statistically evaluated.

Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for sea ice monitoring. A recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric SAR for operational sea ice observation. Given its enhanced radar target information compared to conventional SAR systems over wider swath coverage compared to fully polarimetric SAR systems, CP SAR systems could play important role in the new generation of Earth observation systems. In this study, fully polarimetric SAR images were collected over the Resolute Bay area during the fall of 2017. Acquired images are used for the simulation of CP SAR images and the derivation of a set of 23 CP SAR parameters from each image. The derived CP parameters were analysed in relation to the ice thickness and salinity of lake ice and fast sea ice. Results are compared against backscattering and decomposition parameters derived from the fully polarimetric SAR imagery.

Ice surface temperature (IST) is a key parameter for the study of polar ice sheets and ice shelves. In this study, an improved single-channel (ISC) algorithm based on the radiative transfer equation is proposed for IST retrieval from Landsat-8 band 10 data. The main steps in the proposed ISC algorithm include: 1) simulation of atmospheric radiative parameters by regression against the atmospheric water vapor content and the effective mean atmospheric temperature; 2) calculation of IST using Planck's equation, instead of using Taylor's approximation; and 3) implementation of an iterative scheme for IST calculation. The errors from using Taylor's approximation and the atmospheric radiative parameter simulation were quantitatively estimated. A sensitivity analysis of ISC to possible errors in atmospheric water vapor content, brightness temperature, and satellite observations was also conducted. The results of the sensitivity analysis showed that the proposed algorithm is robust to the atmospheric water vapor content, but is sensitive to the calibration precision of the thermal infrared sensor. Verification using a simulated approach showed better IST variability from ISC than the original SC algorithm [the root-mean-square errors (RMSEs) were 0.3252 and 0.7176 K, respectively]. When compared with near-surface air temperatures from 68 automatic weather stations data in Greenland and 25 data in the Antarctic, the bias and RMSE from the ISC algorithm were again better than those from the SC algorithm. The IST from Moderate Resolution Imaging Spectroradiometer (MODIS) was found to be underestimated with respect to the results of both the SC and ISC algorithms. Maps of the spatial distributions of IST derived from samples of Landsat-8 images are presented. The rationale of each step in the proposed ISC algorithm is also presented so that this can provide further support to the authenticity of the results.

Multiyear ice (MYI) characteristics can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combine both observations to identify partial concentrations of ice types (including MYI) is the Environment Canada Ice Concentration Extractor (ECICE). However, cycles of warm-cold air temperature trigger wet-dry cycles of the snow cover on MYI surface. Under wet snow conditions, anomalous brightness temperature and backscatter, similar to those of first-year ice (FYI), are observed. This leads to misidentification of MYI as being FYI, hence decreasing the estimated MYI concentration suddenly. The purpose of this paper is to introduce a correction scheme to restore the MYI concentration under this condition. The correction is based on air temperature records. It utilizes the fact that the warm spell in autumn lasts for a short period of time (a few days). The correction is applied to MYI concentration retrievals from ECICE using an input of combined QuikSCAT and AMSR-E data, acquired over the Arctic region in a series of autumn seasons from 2003 to 2008. The correction works well by replacing anomalous MYI concentrations with interpolated ones. For September of the six years, it introduces over 0.1×106km2MYI area, except for 2005. Due to the regional effect of warm air spells, the correction could be important in the operational applications where ice concentrations are crucial on small scale and mesoscale.

The recently observed reduction in perennial ice in the Arctic has given rise to a corresponding increase in seasonal ice, which includes young ice (YI). This type of ice has a major impact on the weather and climate systems. However, only a limited number of studies have been dedicated to explore its spatial coverage and duration. This is mainly due to the lack of remote sensing tools that can identify it. This study uses an ice type and concentration retrieval algorithm, namely, Environment Canada's Ice Concentration Extractor, to study YI distribution and duration in the Arctic during seven ice formation seasons: 2002-03 to 2008-09. Results on the YI area, peak period, duration, and its interannual variability are presented in six regions covering the Arctic Basin. Duration is presented in terms of two parameters that describe the peak period and the number of days when YI concentration exceeds 50%. Probability distribution of the latter parameter shows that YI survives very few days before it grows into first-year ice. The summer of the minimum ice record in 2007 did not leave a remarkable impact on the subsequent YI area or its duration, although a delay in ice formation is observed. YI in the North Water polynya is also studied and shows no particular trend, although it varies between years. Anomalies are explained in terms of modeled surface temperature and wind.

Establishment of the Kamal Ewida Earth Observatory (KEEO) has been funded by the North Atlantic Treaty Organization (NATO) Science for Peace Program. KEEO is a joint initiative of two of Egypt's largest and most venerable institutions of higher learning, Cairo University and Al Azhar University, both based in Cairo, Egypt, in collaboration with established environmental observatories in two NATO countries, Turkey and the USA. Specifically, the Egyptian partners, based in their Departments of Meteorology and Astronomy, Faculty of Science, at the two Egyptian Universities, are engaging in applications development, research and instructional collaboration with partnering resources from Bogaziçi University's Kandilli Observatory and Earthquake Research Institute (Istanbul, Turkey), with expertise in disaster mitigation, and Purdue University's Rosen Center for Advanced Computing's Purdue Terrestrial Observatory (West Lafayette, Indiana, USA), with expertise in real-time remote sensing and multi-disciplinary applications of satellite data. The KEEO project provides an interdisciplinary approach to effective disaster management and facilitates collaborative research and decision support, within the Egyptian context, for disaster mitigation.

Simulated sea ice was grown in an outdoor tank during the early winter seasons of 2001/2002 and 2005/2006. Microwave radiation was sampled every 5 min from the following three channels: 19, 37, and 85 GHz. Surface physical conditions were measured or observed to help in the interpretation of the radiometric behavior. This paper reports on results related to the following objectives: 1) linking the observed radiation to surface properties and processes; 2) classifying thin ice into emissivity-based surface types, and 3) assessing thin-ice parameter retrieval algorithms. This paper shows that ice of less than 4-cm thickness exhibits cycles of a sharp decrease of microwave emission caused by surface wetness followed by a gradual increase as the surface refreezes. This ice is particularly linked to meteorological conditions. Snow accumulation on relatively thick ice (> 20 cm) affects only the radiation from the 85-GHz channel. Thin-ice surfaces can be grouped into two radiometrically distinguished categories - the first includes slushy and wet surfaces and the second includes wet snow, dry snow, and dry bare-ice surfaces. Radiation from the second category is higher. The radiation from a refrozen slush surface appears to fall between these two categories. The variability of emissivity increases as the radiation frequency increases, particularly for the horizontal polarization channels. Existing algorithms of ice thickness, snow depth, and ice concentration were examined against the current data to study their sensitivity to variations of surface conditions. Limitations on their applications have been established.

A new algorithm, called Environment Canada's Ice Concentration Extractor (ECICE), has been developed to calculate total ice concentration and partial concentration of each ice type from remote-sensing observations. It employs two new concepts. First, it obtains a best estimate of ice concentrations by minimizing the sum of squared difference between observed and estimated radiometric values based on a linear radiometric model for each ice type. Second, instead of employing a single radiometric value (tie point) for each ice type, it utilizes the probability density distribution of the radiometric values for each ice type. Then, in a Monte Carlo simulation, 1000 radiometric values are randomly selected, total and ice-type concentrations are calculated by solving the minimization problem, and finally, median values from the 1000 simulations are chosen. The algorithm was applied to the winter sea ice in the Gulf of St. Lawrence, Canada, using observations from Special Sensor Microwave Imager (SSM/I) 85-GHz channel. Results were evaluated against ice concentration estimates from the operational analysis of Radarsat images at the Canadian Ice Service (CIS). Statistics of the differences between the output concentration and CIS estimates show that ECICE can successfully identify open water and consolidated pack ice pixels better than the Enhanced NASA Team algorithm. However, in areas of ice concentrations between 20% and 70%, the algorithm's performance could not be precisely evaluated because the typical size of the CIS's analysis polygon is much larger than the footprint of the 85-GHz SSM/I channel. Hence, the algorithm captures information at a finer spatial scale. Examples of using one, two, and three radiometric parameters to calculate the concentrations are presented.

A new algorithm has been developed to calculate sea ice concentration from any set of passive microwave observations. It was applied to estimate total ice concentration and partial concentration of three ice types using SSM/I 85 GHz observations. The essence of the algorithm is a mathematical optimization technique to determine the best solution from multi-channel observations of a heterogeneous footprint that contains ice types of highly complex and overlapped brightness temperature. Results were validated against ice concentrations from Radarsat image analysis; an operational product from the Canadian Ice Service (CIS). They have proven the successful performance of the algorithm.

A three-dimensional variational data assimilation (3D-Var) system is developed as a first attempt to explore the potential use of data assimilation to improve a coupled ice-ocean model (CIOM) forecast of sea ice near the east coast of Canada. The accuracy of the resulting analysis is largely dependent upon the forecast-error covariance matrix. This study focuses on the estimation of forecast-error statistics required in a 3D-Var system and their effect on the ocean part of the CIOM. This is accomplished by comparing CIOM output according to different specifications of forecast-error statistics used during the data assimilation. The results show no improvement in the ice forecast with respect to persistence. It has been concluded that the assimilation system still needs significant improvement including assimilation of many different types of observations such as sea surface temperature, ice drift, ocean current, etc. An improved forecast-error covariance matrix is needed for a more accurate description of the system's behaviour.

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)-York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from SpecialSensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100%. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS's estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS's estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations

An important use of remote sensing data fusion is in retrieval of surface parameters form multisensor data. This requires using collocated observations from a least two sensors at pixel level. A method of multisensor data collocation has been developed to overlay each footprint form a coarse-resolution sensor onto a coincident finer resolution imagery data. Statistics of observations from the fine resolution data can then be correlated to the corresponding radiometric observation from the coarse resolution data. The method has been used to evaluate results from physical and empirical models to retrieve surface parameters (such as sea ice concentration), and also to develop /or modify models to calculate such parameters form a coarse resolution sensor, using subpixel information available from the fine resolution data, examples are presented to demonstrate these two applications and to emphasize the importance of using data fusion in physical modeling of remote sensoring data.

The sea ice concentration from the enhanced NASA Team (NT2) algorithm was evaluated against coincident Radarsat images. The evaluation uses a new data fusion technique accounting for the sensor's antenna pattern. Evaluation can be performed visually or statistically. This study involves cases from the Gulf of St. Lawrence, Canada during the winter of 2000. Results show good agreement between the data sets.

The complex dielectric constant of first-year and multiyear sea ice was measured during the Seasonal Ice Monitoring and Modeling (SIMMS) field experiments, conducted in the Arctic in the spring of 1992, 1993, and 1995. The dielectric constant was also computed based on an established dielectric mixing model by using different assumptions about inclusion shape. Computations were based on detailed measurements and observations of ice physical properties and crystalline structure. Comparison between measurements and model results was conducted to identify working models for first-year and multiyear ice. For first-year ice, models that employ the assumption of vertically oriented brine pockets are applicable to columnar ice and those with the assumption of randomly oriented brine pockets are applicable to frazil ice. The validity of the models are established only for ice temperatures less than -8/spl deg/C. For multiyear ice, there is no need to account for air bubble shape. The coexistence of brine and air inclusions in multiyear pond ice makes it characteristically different from hummock ice. Best results for pond ice were obtained from a simple model that accounts only for volume fractions of inclusions, rather than their shape. Physical parameters that can be retrieved directly from the dielectric constant are salinity of first-year ice at temperatures below -15/spl deg/C and density of multiyear hummock ice. Detailed measurements of permittivity and loss of first-year and multiyear ice are presented along with some insight into interactions between the dielectric constant and physical parameters.

The seasonal and diurnal changes in total distributions of three fast ice sites, validated during the Seasonal Sea Ice Monitoring Site (SIMS '90) experiment, are presented. Rationale for the observed changes are provided using previous scatterometer and SAR observations of the seasonally varying snow-covered sea ice surface. The implications of the stability and rapidity of the observed changes are considered important in the development of semiautomated feature extraction algorithms, required by the operational sea ice community, and in monitoring the seasonal evolution of a snow-covered sea ice surface.>