Abstract:Albedo products (MOD10A1, MYD10A1, and MCD43A3) from the Moderate Resolution Imaging Spectrometer (MODIS) have the potential to be integrated directly into lake ice models such as the Canadian lake ice model (CLIMo) to improve the simulation of freshwater break-up (ice-off). The high albedo of snow and lake ice has been shown to affect the timing of breakup. Therefore, the surface energy balance parameterization of CLIMo requires accurate estimates of albedo when modelling phenology. MOD10A1, MYD10A1, and MCD43A3 were evaluated against in situ snow and ice albedo observations taken over a partially snow-covered freshwater lake (Malcolm Ramsay Lake) near Churchill, Manitoba, during the ice growth period (15 February 2012 to 25 April 2012). The MODIS albedo products were then compared with the CLIMo's albedo parameterization during the ice break-up period. The MODIS albedo products MOD10A1, MYD10A1, and MCD43A3 retrieved snow and ice albedo with root mean square error values of 0.07, 0.08, and 0.06, respectively, compared with spatially averaged in situ albedo measurements during ice growth. MODIS albedo products compared with CLIMo's melting ice parameterization during the melt season indicate that CLIMo's albedo estimates have a mean difference of at least 0.14 compared with the MODIS retrievals during melt. The quality of the albedo retrievals over lake ice from MODIS and the need for more accurate albedo simulations during the melt season suggest that the assimilation of MODIS albedo products into CLIMo could be beneficial for the determination of break-up (ice-off) dates.

Accurate simulations of freshwater lake ice are integral for the study of climatic variability in northern environments. Surface albedo, a component often parameterized in lake ice models, has been shown to affect the timing of ice break-up during the melt season. In situ snow and ice albedo measurements were taken over a partially snow-covered freshwater lake near Churchill, Manitoba for the evaluation of the albedo parameterization of the Canadian Lake Ice Model (CLIMo) and the Moderate Resolution Imaging Spectrometer (MODIS) albedo products (MOD10A1/MYD10A1 and MCD43A3). The albedo simulations using CLIMo were performed with and without snow integrated into the model and evaluated against in situ albedo measurements recorded over clear ice, snow ice and snow-covered ice. The simulated snow albedo from CLIMo for the entire ice growth season evaluated against snow albedo observations had a root mean square error of 0.07, a mean absolute error of 0.06, and a mean bias error (MBE) of À0.01. With snow removed from CLIMo, the albedo parameterization overestimated albedo values measured in the field over snow-free clear ice and snow ice with MBE values of 0.13 and 0.10, respectively. These findings suggest that CLIMo's bare ice albedo parameterization needs to be revised to account for albedo differences between ice types. The evaluation of the MODIS albedo products with in situ snow and ice albedo observations and the comparison of these satellite products with CLIMo's albedo parameterization during the melt period, when in situ radiation measurement stations needed to be removed from the lake-ice surface, are addressed in paper Part II (this issue).

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.

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<p>The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atmospheric conditions, compared to optical sensors. RCM has been used operationally since 2020 and employs 3 satellites, enabling lower revisit times and increased imagery coverage. The team responsible for the production of flood maps in the context of emergency response are able to produce maps within four hours from the data acquisition. Although the results from their automated system are good, there are some limitations to it, requiring manual intervention to correct the data before publication. Main limitations are located in urban and vegetated areas. Work started in 2021 to make use of deep learning algorithms, namely convolutional neural networks (CNN), to improve the performances of the automated production of flood inundation maps. The training dataset make use of the former maps created by the emergency response team and is comprised of over 80 SAR images and corresponding digital elevation model (DEM) in multiple locations in Canada. The training and test images were split in smaller tiles of 256 x 256 pixels, for a total of 22,469 training tiles and 6,821 test tiles. Current implementation uses a U-Net architecture from NRCan geo-deep-learning pipeline (https://github.com/NRCan/geo-deep-learning). To measure performance of the model, intersection over union (IoU) metric is used. The model can achieve 83% IoU for extracting water and flood from background areas over the test tiles. Next steps include increasing the number of different geographical contexts in the training set, towards the integration of the model into production.</p>