Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modeling

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size and temperature) were used as inputs to the multi-layer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (-0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical SSA to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%

Seasonal Sea Ice Conditions Affect Caribou Crossing Areas Around Qikiqtaq, Nunavut: Uqsuqtuurmiut Knowledge Guides Ice Chart Analysis

Though polar ecologists consider sea ice primarily as a habitat for marine mammals, caribou use sea ice to complete their reproductive cycles, to access areas with preferred climatic and vegetation conditions, and to avoid predators seasonally and sporadically. Building on previous caribou research in Uqsuqtuuq (Gjoa Haven, Nunavut), we explored the connections between caribou and sea ice phenology in 5 community-identified caribou crossing areas around Qikiqtaq (King William Island). We defined freeze-up and breakup based on Uqsuqtuurmiut (people of Uqsuqtuuq) knowledge of caribou habitat requirements, to orient our analysis to the complex and multifaceted hazards that caribou can encounter while moving through their dynamic and unpredictable sea ice habitat. We investigated the reliability of caribou sea ice habitat surrounding Qikiqtaq, prioritizing key transitional periods with intensified caribou movement. We use regional ice charts produced by the Canadian Ice Service (CIS) and held workshops with Uqsuqtuurmiut to understand how sea ice phenology and caribou mobility have changed over time. The high spatial and temporal variability of sea ice phenology around Qikiqtaq facilitates caribou moving across sea ice should they need to respond to seasonal or unpredictable changes in ecological conditions or anthropogenic disturbance. Therefore, these localized sea ice conditions may increase caribou resiliency to changes or extreme events by providing alternative options for movement across the sea ice. We encourage others to consider the needs of wildlife sea ice users when assessing or providing ice information. Bien que les écologistes polaires considèrent que la glace de mer est principalement un habitat de mammifères marins, les caribous s’en servent pour leurs cycles de reproduction, pour accéder à des lieux dont les conditions climatiques et la végétation conviennent à leurs préférences et pour éviter les prédateurs, en fonction des saisons et de manière sporadique. En nous appuyant sur des recherches antérieures sur les caribous à Uqsuqtuuq (Gjoa Haven, Nunavut), nous avons exploré les liens entre le caribou et la phénologie de cinq points de franchissement des caribous dans la région de Qikiqtaq (île King William), tels que déterminés par la communauté. Nous avons défini l’englacement et la débâcle en nous fondant sur les connaissances des Uqsuqtuurmiut (le peuple d’Uqsuqtuuq) concernant les besoins du caribou en matière d’habitat afin d’éclairer notre analyse des dangers complexes et multidimensionnels auxquels les caribous peuvent faire face quand ils se déplacent dans leur habitat de glace de mer dynamique et imprévisible. Nous avons étudié la fiabilité de l’habitat de glace de mer du caribou dans les alentours de Qikiqtaq, en accordant une attention particulière aux périodes de transition pendant lesquelles les déplacements des caribous sont plus intenses. Nous avons utilisé les cartes des glaces régionales produites par le Service canadien des glaces (SCG) et organisé des ateliers avec les Uqsuqtuurmiut pour comprendre comment la phénologie de la glace de mer et la mobilité des caribous ont évolué au fil du temps. La grande variabilité spatiale et temporelle de la phénologie de la glace de mer des environs de Qikiqtaq facilite le déplacement des caribous sur la glace de mer s’ils devaient réagir aux changements saisonniers et imprévisibles des conditions écologiques et de la perturbation anthropique. Par conséquent, ces conditions de glace de mer localisées peuvent avoir pour effet d’augmenter la résilience du caribou aux changements ou aux événements extrêmes, car elles présentent des options de rechange en matière de déplacements sur la glace de mer. Nous incitons d’autres personnes à considérer les besoins de la faune utilisant la glace de mer lorsqu’elles doivent évaluer ou fournir de l’information sur la glace de mer.

Rain-on-Snow and Ice Layer Formation Detection Using Passive Microwave Radiometry: An Arctic Perspective

No abstract availabl

Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization

Simulated compact polarimetry from the RADARSAT Constellation Mission (RCM) is evaluated for sea ice classification. Compared to previous studies that evaluated the potential of RCM for sea ice classification, this study focuses on the High Resolution (HR) Synthetic Aperture Radar (SAR) mode of the RCM associated with a higher noise floor (Noise Equivalent Sigma Zero of −19 dB), which can prove challenging for sea ice monitoring. Twenty three Compact Polarimetric (CP) parameters were derived and analyzed for the discrimination between first year ice (FYI) and multiyear ice (MYI). The results of the RCM HR mode are compared with those previously obtained for other RCM SAR modes for possible CP consistency parameters in sea ice classification under different noise floors, spatial resolutions, and radar incidence angles. Finally, effective CP parameters were identified and used for the classification of FYI and MYI using the Random Forest (RF) classification algorithm. This study indicates that, despite the expected high noise floor of the RCM HR mode, CP SAR data from this mode are promising for the classification of FYI and MYI in dry ice winter conditions. The overall classification accuracies of CP SAR data over two test sites (96.13% and 96.84%) were found to be comparable to the accuracies obtained using Full Polarimetric (FP) SAR data (98.99% and 99.20%)

Correction: Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization. Remote Sensing 2018, 10, 594

In Figure 5 of [1], we detected a minor mistake in the visualization of the Spearman correlation related to the color bar.[...

Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada

Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from water on a large volume of data covering 11 years of ice and surface water conditions. An overall accuracy of 90.1% was achieved on 989 scenes of 100% ice cover or ice-free conditions. An accuracy of 86.3% was achieved on the last year of data (134 scenes) which was kept out of the training process to test the model on an independent dataset. A better accuracy is obtained at lower incidence angles and the HH polarization provides the most information to classify ice from water. To achieve the best accuracy, the incidence angle and the noise equivalent sigma-nought had to be included as input to the model. A comparison done with the ASI passive microwave product shows similar errors in total sea ice concentration when using the Canadian Ice Service regional charts as reference. Nonetheless, errors from both datasets differ and the CNN outputs show greater potential to reduce masked areas, given the better spatial resolution, enabling data classification closer to land and identify features not captured by the ASI dataset.This dataset has been processed from RADARSAT-2 image products and saved into a stacked Python Numpy array. These arrays are analysis ready data to train/test the CNN model used in the referenced publication. Original RADARSAT-2 image products could not be shared directly since they are government by a End-User Licence Agreement (EULA). "RADARSAT-2 Data and Products © MacDONALD, DETTWILER and \n ASSOCIATES LTD (2023) - All Rights Reserved" and " RADARSAT is an official mark of the Canadian Space Agency

Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada

Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from water on a large volume of data covering 11 years of ice and surface water conditions. An overall accuracy of 90.1% was achieved on 989 scenes of 100% ice cover or ice-free conditions. An accuracy of 86.3% was achieved on the last year of data (134 scenes) which was kept out of the training process to test the model on an independent dataset. A better accuracy is obtained at lower incidence angles and the HH polarization provides the most information to classify ice from water. To achieve the best accuracy, the incidence angle and the noise equivalent sigma-nought had to be included as input to the model. A comparison done with the ASI passive microwave product shows similar errors in total sea ice concentration when using the Canadian Ice Service regional charts as reference. Nonetheless, errors from both datasets differ and the CNN outputs show greater potential to reduce masked areas, given the better spatial resolution, enabling data classification closer to land and identify features not captured by the ASI dataset

Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data

Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study, we evaluate the potential of fully polarimetric L-band SAR imagery for monitoring and classifying sea ice during dry winter conditions compared to fully polarimetric C-band SAR. Twelve polarimetric SAR parameters are derived using sets of C- and L-band SAR imagery and the capabilities of the derived parameters for the discrimination between First Year Ice (FYI) and Old Ice (OI), which is considered to be a mixture of Second Year Ice (SYI) and Multiyear Ice (MYI), are investigated. Feature vectors of effective C- and L-band polarimetric parameters are extracted and used for sea ice classification. Results indicate that C-band SAR provides high classification accuracy (98.99%) of FYI and OI in comparison to the obtained accuracy using L-band SAR (82.17% and 81.85%), as expected. However, L-band SAR was found to classify only the MYI floes as OI, while merging both FYI and SYI into one separate class. This comes in contrary to C-band SAR, which classifies as OI both MYI and SYI. This indicates a new potential for discriminating SYI from MYI by combining C- and L-band SAR in dry ice winter conditions