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While many studies have been conducted regarding wind-driven Ka-band scattering on the ocean and sea surfaces, few have identified the impacts of Ka-band scattering on small inland water bodies, and fewer have identified the influence of wind on coherence over water. These previous studies have been limited in spatial scale, covering only large water bodies >25 km2. The recently launched Surface Water and Ocean Topography (SWOT) mission is the first Ka-band InSAR satellite designed for mapping water surface elevations and open water areas for rivers as narrow as 100 m and lakes as small as 0.0625 km2. Because measurements of these types are novel, there remains some uncertainty about expected backscatter amplitudes given wind-driven water surface roughness variability. A previous study using the airborne complement to SWOT, AirSWOT, found that low backscatter and low coherence values were indicative of higher errors in the water surface elevation products, recommending minimum thresholds for backscatter and coherence for filtering the data to increase the accuracy of averaged data for lakes and rivers. We determined that the global average wind speed over lakes is 4 m/s, and after comparing AirSWOT backscatter and coherence data with ERA-5 wind speeds, we found that the minimum required speed to retrieve high backscatter and coherence is 3 m/s. We examined 11,072 lakes across Canada and Alaska, with sizes ranging from 350 m2 to 156 km2, significantly smaller than what could be measured with previous Ka-band instruments in orbit. We found that small lakes (0.0625–0.25 km2) have significantly lower backscatter (3–5 dB) and 0.20–0.25 lower coherence than larger lakes (>1 km2). These results suggest that approximately 75% of SWOT observable lake areas around the globe will have consistently high-accuracy water surface elevations, though seasonal wind variability should remain an important consideration. Despite very small lakes presenting lower average backscatter and coherence, this study asserts that SWOT will be able to accurately resolve the water surface elevations and water surface extents for significantly smaller water bodies than have been previously recorded from satellite altimeters. This study additionally lays the foundation for future high-resolution inland water wind speed studies using SWOT data, when the data become available, as the relationships between wind speed and Ka-band backscatter reflect those of traditional scatterometers designed for oceanic studies. [ABSTRACT FROM AUTHOR]

• Ratios of E-CHAIM model-to-RISR radar measured electron density are close to one at heights near F2 peak. • On average, E-CHAIM underestimates electron density at ionospheric topside and bottomside by ∼ 20 % • E-CHAIM underestimations are strongest in summer and equinoctial nighttime at bottomside and in autumn nighttime at topside. In this study, predictions of the E-CHAIM ionospheric model are compared with measurements by the incoherent scatter radars RISR at Resolute Bay, Canada, in the northern polar cap. Reasonable coverage was available for all seasons except winter for which no conclusions were drawn. It is shown that ratios of the model-to measured electron densities are close to unity in the central part of the F layer, around its peak. This is particularly evident for summer daytime. Distributions of the ratios are wider for other seasons indicating larger number of cases when the model underestimates or overestimates. E-CHAIM underestimates the electron density at ionospheric topside and bottomside by ∼ 10–20 %. At the bottomside, the underestimations are strongest in summer and equinoctial nighttime. At the topside, the underestimations are strongest in autumn nighttime. Model overestimations are noticeable in the middle part of the F layer during dawn hours in autumn. Overall, the model tends to not predict highest-observed peak electron densities and the largest-observed heights of the peak. [ABSTRACT FROM AUTHOR]

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]

The performance of constant false alarm rate (CFAR) detectors is often severely degraded in clutter edge and under multiple target interference. CFAR detection in the above environments often uses generalised likelihood ratio method or mean ratio method to find out the clutter edge demarcation line and suppress the sharp increase of false alarm rate, or remove the interference and modify the threshold factor to suppress the interference and improve the detection performance, but this sacrifices the detection gain from the number of clutter samples. In this paper, the feature information is fused to construct feature vectors, and the edge location is judged by the modulus of the cross product of the feature vector pair (MCPFVP), this edge demarcation line detection method fusing with features can be implemented without the need of clutter statistical distribution information. In addition, this paper reconstructs the sea clutter masked by multiple target interference by fusing the sea clutter amplitude correlation information, and these reconstructed sea clutter samples are used for power estimation together with the unmasked sea clutter without changing the threshold factor. This method can not only suppress the target masking effect of multiple target interference as the censoring type CFAR detector, but also maintain the detection gain from the number of clutter samples, which is not possible with the traditional censoring type CFAR detector. In addition, the relative weighted ratio summation (RWRS) is proposed to detect the multiple target interference, which takes into account the asymmetry of the shape of the statistical distribution. Both simulated and real sea clutter data are used to validate the above methods. The measured data are obtained from radar data collected at Yantai Yangma Island, China, Darmouth, Canada and Grimsby, Canada. (The latter two datasets are collected by the IPIX radar - Ice Multiparameter Imaging X-Band Radar). [ABSTRACT FROM AUTHOR]

The Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014. [ABSTRACT FROM AUTHOR]

Weather radar provides real-time, spatially distributed precipitation estimates, whereas traditional gauge data are restricted in space. The use of radar quantitative precipitation estimates (QPEs) as an input of hydrological models for hydrometeorological applications has increased with the development of weather radar worldwide. New dual-polarization technology and algorithms are showing improvements to radar QPEs. This study evaluates radar QPEs from C-band radar at King City, Canada (WKR), and NEXRAD S-band radar at Buffalo, New York (KBUF), to verify the reliability and accuracy for operational use in the Humber River (semiurban) and Don River (urban) watersheds in the Greater Toronto Area (GTA), Canada. Twenty rainfall events that occurred from 2011 to 2017 were determined from hourly gauge measurements and compared with nine radar QPEs. Rain rates were estimated with different algorithms using three dualpolarized reflectivity values: horizontal reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP. The correlation coefficient, bias, detection, and root-mean-square error were calculated and averaged over all events for each gauge station to show the spatial distribution and in a similar pattern to represent the variation by the event. The quality of the results in terms of accuracy and reliability indicates that the radar QPEs from KBUF S-band and WKR C-band multiparameter rain rate estimators can be effectively used as precipitation forcing of hydrological models for hydrometeorological applications. The high spatiotemporal resolution, long-term data archive, and good percent detection of radar QPEs can facilitate hydrometeorological applications by providing a continuous time series for hydrological models. [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]

Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied. [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]

A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dual-polarized radar (WKR). Horizontal reflectivity Z and differential reflectivity ZDR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R( Z) and R( Z, ZDR) algorithms. Specific differential phase KDP was used to compute rain rates from three R( KDP) algorithms, one modified to use positive and negative KDP, and an R( KDP, ZDR) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R( A) algorithm. High-temporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R( Z) and S-band dual-polarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R( Z) and R( Z, ZDR) algorithms. Rainfall estimation from the Brandes et al. R( KDP) and R( A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and −6.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R( KDP) and R( A) algorithms were comparable to the NEXRAD S-band estimates. [ABSTRACT FROM AUTHOR]

Joint observations of the all-sky camera at Resolute Bay (Nunavut, Canada) and the Polar Dual Auroral Radar Network (PolarDARN) HF radars at Rankin Inlet and Inuvik (Canada) are considered to establish radar signatures of poleward moving polar cap arcs "detaching" from the auroral oval. Common features of the events considered are enhanced power or echo occurrence in the wake of the arcs and enhanced spectral width of these echoes. When the arcs were oriented along some of the radar beams, velocity reversals at the arc location were observed with the directions of the arc-associated flows corresponding to a converging electric field. For the event of 9 December 2007, two arcs were poleward progressing almost along the central beams of the Inuvik radar at the speed close to the E×B drift of the bulk of the F-region plasma as inferred from HF Doppler velocities and from independent measurements by the Resolute Bay ionosonde. In global-scale convection maps inferred from all Super Dual Auroral Radar Network (SuperDARN) radar measurements, the polar cap arcs were often seen close to the reversal line of additional mesoscale convection cells located poleward of the normal cells related to the auroral oval. [ABSTRACT FROM AUTHOR]

A method for operational monitoring of a weather radar receiving chain, including the antenna gain and the receiver, is presented. The “online” method is entirely based on the analysis of sun signals in the polar volume data produced during operational scanning of weather radars. The method is an extension of that for determining the weather radar antenna pointing at low elevations using sun signals, and it is suited for routine application. The solar flux from the online method agrees very well with that obtained from “offline” sun tracking experiments at two weather radar sites. Furthermore, the retrieved sun flux is compared with data from the Dominion Radio Astrophysical Observatory (DRAO) in Canada. Small biases in the sun flux data from the Dutch and Finnish radars (between -0.93 and +0.47 dB) are found. The low standard deviations of these sun flux data against those from DRAO (0.14–0.20 dB) demonstrate the stability of the weather radar receiving chains and of the sun-based online monitoring. Results from a daily analysis of the sun signals in online radar data can be used for monitoring the alignment of the radar antenna and the stability of the radar receiver system. By comparison with the observations from a sun flux monitoring station, even the calibration of the receiving chain can be checked. The method presented in this paper has great potential for routine monitoring of weather radars in national and international networks. [ABSTRACT FROM AUTHOR]

Ice scouring of lake and sea-floor substrates by the keels of drifting ice masses is a common geological process in modern northern lakes and continental shelves, and was widespread during the Pleistocene. Nonetheless, the importance of scouring as a geological process is not yet matched by many sedimentological studies of scour structures exposed in outcrop. This article presents an integrated study combining outcrop sedimentology and subsurface ground-penetrating radar (GPR) data from a relict late Pleistocene ice-scoured glacial lake floor now preserved below beach sediments in Ontario, Canada. Scours occur along a regressive sequence boundary where deep-water muddy facies are abruptly overlain by shallow-water sands resulting from an abrupt drop in water levels. This has allowed the keels of drifting ice masses to scour into muds. Three-dimensional data gained from the GPR survey show that scours are as much as 2.5 m deep and 7 m wide; they have berms of displaced sediment and are oriented parallel to the former shoreline. Scoured shoreface sediments that fill scours show abundant liquefaction structures, indicating substrate dewatering during repeated scouring events similar to that recently reported in the modern Beaufort Sea in Canada's far north. Marked changes in water depths are typical of glacially influenced lakes and seas, creating opportunities for drifting ice to scour into offshore muddy cohesive facies and be preserved. The data presented here may aid identification in ancient successions elsewhere. [ABSTRACT FROM AUTHOR]

A physically based method is developed to estimate the microphysical structure of the melting layer in stratiform rain using airborne observations by a dual-frequency radar and a 10.7-GHz radiometer. The method employs a nonlinear optimal estimation approach to find two parameters of the gamma drop size distribution (DSD) at each radar range gate from the Ku/Ka-band reflectivities. The DSD profile is used to determine the atmospheric absorption/extinction profile, which enables the surface contribution to the measured brightness temperature to be estimated. The surface wind speed is estimated from the surface emissivity by inverting the forward model, which relates the two. Retrievals in stratiform precipitation require a model to describe the thermodynamic and electromagnetic properties of melting hydrometeors. The melting layer can contribute a majority of the total atmospheric absorption, making it a key component for accurate retrievals in stratiform rain. Several melting layer models were evaluated based on their fit to the dual-frequency reflectivity measurements in the melting layer. A candidate model is selected and tuned to match the radar measurements. The melting layer model is then incorporated into the full forward model for the brightness temperature observed by the radiometer. The surface wind speed assumed in the forward model is forced by the radiometer observations. If the actual surface wind speed is known, this approach provides a powerful constraint on the possible melting layer model. A case study is presented from an airborne campaign over areas of precipitation off the coast of Vancouver Island, British Columbia, Canada. The estimated wind speeds are found to be uncorrelated with the reflectivity and their average value is within 1 m s-1 of that retrieved in a clear area adjacent to the rain. [ABSTRACT FROM AUTHOR]

Turbulence inhomogeneities at 3-m scales can be either isotropic or anisotropic, and the degree of anisotropy can be measured with VHF wind profiler radars. Studies over a period of two years in Montreal, Quebec, Canada, have shown that for this site during nonwinter months, the occurrence of isotropic turbulence at 3-m scales serves as a useful diagnostic for the occurrence of rainfall. Turbulent eddies are expected to be most isotropic when wind shears are weak and the atmosphere is either unstable or close to instability. The measurements show that when the turbulence is most isotropic, rainfall is very common. Furthermore, the development of quasi-isotropic turbulence usually occurs before the onset of precipitation. Lead times are typically of the order of 1 to 6 h. Correlation coefficients between the occurrence of strong isotropy and the development of rain generally exceed 0.6, and can be as high as 0.7. Further studies will be required to determine whether this phenomenon is specific to the Montreal area, or can be applied to other locations as well. [ABSTRACT FROM AUTHOR]

The Mid-Canada Radar Line (MCRL) was built during the 1950s in response to the perceived threat of a Soviet nuclear attack over the Arctic. The MCRL was an entirely Canadian project, consisting of 98 radar stations that stretched across the 55th parallel from Dawson Creek, British Columbia, to Hopedale, Labrador. Seventeen MCRL sites were located in Ontario, and by 1965, all had been closed for strategic and economic reasons. Since these sites were improperly decommissioned, they have become point sources of contaminants in northern Canada. In 2001, MCRL Site 050 was remediated. The Fort Albany First Nation (located near Site 050), the Department of National Defence, and the Ontario Ministry of Natural Resources had formed a "partnership" to undertake this. We determined that from an Aboriginal perspective, a true partnership (as we define it) did exist between these organizations; a partnership based on the essential elements of respect, equity, and empowerment. We show that these cornerstones of a true partnership were present in the initial documents that discussed remediation of this site. This evaluation will provide insight, guidance, and a potential framework to benefit future partnership endeavours, helping to foster stronger collaborative relationships between Aboriginal organizations and governments at all levels, especially with respect to the remediation of abandoned radar line sites. [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]

Presents information on a study which examined the use of radar in assessing populations and habitat associations of marbled murrelets in Vancouver Island, British Columbia. Background on marbled murrelets; Methodology; Results of the study.

The increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points. [ABSTRACT FROM AUTHOR]

Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality. [ABSTRACT FROM AUTHOR]