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Lake ice thickness (LIT), a thematic product of lakes as an essential climate variable (ECV), is sensitive to changes in air temperature and on-ice snow mass. Here, a novel and efficient analytic method (retracking approach) is presented for the estimation of LIT from Ku-band (13.6 GHz) radar altimetry data. The new retracker, referred to as ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ , is based on the physical modeling of the conventional radar echoes (also called low-resolution mode or LRM) over ice-covered lakes that show a characteristic step-like feature in their leading edge attributed to the reflection of radar waves at the snow–ice and ice–water interfaces. The method is applied to Jason-2 and Jason-3 data acquired over Great Slave Lake, Canada, over three ice seasons (2013–2016). As expected, the agreement between the Jason-2 and Jason-3 LIT estimates over their overlapping period (2016 ice season) is excellent with a mean bias error of 0.013 m and root mean square error (RMSE) of 0.024 m. LIT estimates from ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ are in good agreement with simulations from a thermodynamic lake ice model and in situ measurements with RMSE values of the order of a few centimeters for the three winter seasons. The retracker also provides a robust way to assess the accuracy of LIT estimates which is in the order of 0.10 m when the ice cover is well established and prior to melt onset. In addition, ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ captures the seasonal transitions during the freeze-up and breakup periods and ice growth over different winter seasons, making it a promising method for monitoring inter-annual variability and trends in LIT from past and current conventional radar altimetry missions. [ABSTRACT FROM AUTHOR]

In this article, an unsupervised clustering-based method for identifying rain-contaminated and low-backscatter regions in X-band marine radar images is presented. Rain blurs the wave signatures of radar images, and low-backscatter images caused by calibration errors or too-low wind speed contain little or no wave signatures. In both cases, ocean surface parameter measurement using X-band marine radar will be negatively affected. Four types of features can be extracted based on the distinct difference in texture and pixel intensity distribution between rain-free, rain-contaminated, and low-backscatter regions. Features extracted from each pixel are combined into a feature vector and mapped onto a $10\times 10$ -neuron self-organizing map (SOM). Then, the hierarchical agglomerative clustering algorithm is introduced, which clustered those neurons into three types (i.e., rain-free, rain-contaminated, and low-backscatter). The method is validated using the shipborne marine radar data collected on the East Coast of Canada. The good agreement between the pixel-based clustering results and manually segmented reference images indicates that both rain-contaminated and low-backscatter regions can be identified effectively using the proposed method. [ABSTRACT FROM AUTHOR]

The Canadian Department of National Defence (DND) is developing an experimental over-the-horizon radar (OTHR) with the potential for surveillance of Canada. Because of dynamically changing ionospheric conditions in the Earth’s high-latitude and polar regions, the operating OTHR transmission frequency and elevation angle need to be adjusted regularly to maintain constant illumination of downrange targets. In this paper, the feasible operating frequency and elevation angle radar parameters are determined for short- and long-range OTHR operation using 3-D ionosphere ray-tracing simulations. Together, the collection of all feasible radar configurations forms a characteristic profile which shifts and deforms as factors such as the time of day, season, and solar activity are varied. The range of operating frequencies and elevation angles obtained from this paper will aid developing the transmitter and receiver antenna layouts for experimental OTHR configurations in the poorly understood high-latitude and polar regions. These methods will also help to form the basis of the frequency monitoring systems (FMS) that will control the configuration of these polar OTHR systems in real time. [ABSTRACT FROM AUTHOR]

The objectives of this research study was to evaluate the spatiotriangulation applied to multisensor satellite images, which enabled the simultaneous geometric processing of a large number of images and strips together to reduce the control point requirement. The spatiotriangulation is based on the three-dimensional physical models developed for multisensor images at the Canada Centre for Remote Sensing, Natural Resources Canada and on a least squares block bundle adjustment process with orbital constraints. The spatiotriangulation was applied to 49 images in six blocks (Landsat-7 ETM+, panchromatic SPOT-4 HRV, multiband ASTER, multinode radar RADARSAT-1, and ERS-1) acquired over the Rocky Mountains, Canada, from different viewing/look angles. The first results of least squares block bundle adjustments showed that the same error residuals (around 20 m) were obtained with the different image blocks whether independently or simultaneously processed. In addition to ground control points (GCPs), elevation tie points (ETPs), with a known elevation value, instead of normal tie points were used in the overlaps because the viewing/look-angle differences of overlapping images were generally small (8°). The second and most important results were related to simultaneous bundle adjustments of the largest "master" Landsat-7 block (600 km x 500 km) using 25 GCPs in the two outer strips and the smallest "slave" block(s) using no GCP but only ETPs. The errors, verified by a large number of independent check points (ICPs) in the "slave" blocks, were between 15-35 m (1.5-2 resolutions), depending on the "slave" block. However, the combined image pointing and map errors of ICPs (25-30 m) are included in these 15-35-m error results, and the internal accuracy of the blocks should, thus, be better (around one resolution). The research study demonstrated, thus, the possibility to use the largest block with a reduced number of GCPs to simultaneously adjust single image(s)/strip(s) or smallest block(s) with only ETPs, and with no degradation in the accuracy. [ABSTRACT FROM AUTHOR]