Conference Agenda

This talk will highlight the future Canadian radar satellite mission, currently named the Terrestrial Snow Mission, under development by Environment and Climate Change Canada, in partnership with the Canadian Space Agency and Natural Resources Canada.

The mission concept will be presented, as well as recent scientific advancements made in the field of snow radar remote sensing, modeling and data assimilation, to continue the advancement of the mission's science readiness level.

This Canadian radar mission will provide weekly coverage of the northern hemisphere with Ku-band SAR data, and, coupled with modeled data, will provide daily snow water equivalent data, to assist hydrological applications and decision-making.

River systems originating from the Southern Alberta Canadian Rocky Mountains provide snowpack meltwater to an extensive downstream reservoir and irrigation network. Future water supplies have the potential to be significantly decreased due to changes in climate and reduced winter snowpack melt regimes. Estimating accurate water volumes in mountain regions is especially challenging. Current practices for estimating snow water equivalent (SWE) over a large mountain region use single point field-based snow measurements generally at valley or sub-alpine elevations. These field measurements are not spatially representative of basin-wide snowpack variability. The Alberta River Forecast Centre uses the Raven hydrological modelling framework to estimate daily winter snow water equivalent (SWE). To address the need for more accurate simulations of spatially explicit SWE, a combined airborne lidar and field snowpack sampling and modelling framework was compared with a Raven Model simulation. “Single point in time” SWE estimates were obtained between 2014 to 2021 using a combination of a) airborne lidar snow depth models, and b) public field sampled snow density. However, annual water yields cannot be generated from this type of snow sampling. The goal of this study was to improve spatialized Raven modelled SWE using the spatially-explicit lidar-based gridded SWE estimates across the West Castle Watershed (WCW, approximately 100 km^2). Results indicated Raven modelled SWE outputs were underestimated in comparison to the lidar-derived SWE with the largest deviation in the sub-alpine forested and grassland areas. Further research aims to use these comparative data to improve Raven-simulated wintertime headwater SWE estimates.

This study evaluates water surface elevation (WSE) retrievals from the Surface Water and Ocean Topography (SWOT) mission and investigates lake dynamics in the karst influenced environment of Bruce Peninsula National Park, Ontario. SWOT derived WSE measurements are validated against high frequency in situ depth logger data referenced to a consistent vertical datum using GNSS. The analysis compares multiple SWOT products, quality filtering approaches, and pixel aggregation methods to determine optimal workflows and assess performance under varying surface conditions, including open water, small surface area (<1km2), vegetation, and ice cover. Results demonstrate that SWOT accuracy is strongly dependent on surface conditions and lake characteristics, with reduced performance in smaller or vegetated systems. The study also examines spatial correlations in lake level variability to identify potential karst influences on hydrological connectivity. These findings provide guidance for the effective use of SWOT in monitoring inland water systems and highlight its potential and limitations for hydrological applications in complex environments.

Comparing airborne lidar datasets collected during snow-free and snow-covered ground conditions enables snow depth mapping at high accuracy and resolution (Hopkinson et al. 2004, Deems et al. 2013). Imputation of snow depth samples combined with field-based or modeled density can produce SWE for small to meso-scale (~100 km2) watersheds (Barnes et al, Submitted, Cartwright et al. 2020, Hopkinson et al. 2012). The goal of this study was to test lidar-based sampling and imputation in an operational regional (>20,000 km2) basin-scale SWE and runoff forecasting framework. Following initial tests in the winter of 2023, two lidar sensors were flown in March (Teledyne Optech Galaxy) and April (Teledyne Optech Titan) 2024 (and again in 2025 and 2026 – results not reported here), to collect 76 snow depth transects (~1 km wide, >2,000 km2) over the Bow and Oldman River Basin headwaters (>400 km north-south, >50 km east-west) near coincident with field samples at 28 sites. For 85 transect intersections, snow depth covariance was high (r2 0.70, RMSE 0.12m), with a small but acceptable bias of -0.04m or -5% (r2 0.94, n 198). An online digital twin platform is being developed to host the snow depth modeling results, as well as real-time weather telemetry and landscape change for public education and data dissemination purposes.

Surface soil moisture (SSM), representing the moisture content within the top layer of soil, provides valuable information and plays an important role in agricultural management. This study presents a deep learning (DL)-based method to estimate field-scale SSM time series over vegetated agricultural areas in Manitoba, Canada, by combining microwave and optical remote sensing (RS) data with auxiliary information. The input dataset was built using Sentinel-1 Synthetic Aperture Radar (SAR) and Harmonized Landsat Sentinel-2 (HLS S30) optical imagery, together with meteorological variables, soil temperature, crop type, topography, and soil texture. Since Sentinel-1 and HLS images were not acquired simultaneously, temporal interpolation was applied to align optical feature values with SAR acquisition times. Features were extracted at 30 m around nine Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) stations.

A one-dimensional convolutional neural network (1D-CNN) was developed to learn local temporal patterns from the multi-source input dataset. The model was trained on multi-year data from 2016 to 2024 and externally validated on 2017 and 2021. On the validation dataset, the model achieved strong accuracy, with R² = 0.815, RMSE = 0.036 m³/m³, and MAE = 0.026 m³/m³. Model interpretation using Shapley additive explanations (SHAP) highlighted a physically coherent set of predictors, including vegetation cover and structure indices, radar backscatter features, solar radiation, minimum air temperature, and precipitation. Overall, the proposed DL framework provides accurate and interpretable field-scale SSM estimates suitable for agricultural monitoring and downstream water-management applications.

Surface water monitoring is critical due to increasing climate impacts, yet small reservoirs (0.01–1 km²) often lack the in-situ infrastructure required for consistent observation. This study evaluates the reliability of the Surface Water and Ocean Topography (SWOT) satellite mission for monitoring such water bodies by integrating UAV-based Digital Elevation Models (DEMs) and traditional gauge station data. A UAV survey was conducted at Recentino Lake (Umbria, Italy) in December 2024 to generate a high-resolution DEM (1.56 cm/pixel) with a vertical accuracy of 3.4 cm. Parallelly, SWOT data were processed by strictly retaining high-quality flags and applying a temporal outlier removal filter based on water level change velocity. The water surface elevation (WSE) derived from the DEM was compared with the processed SWOT data and in-situ gauge records. Results indicated high consistency between the UAV-DEM and SWOT-derived levels (110.78 m and 110.76 m, respectively) after harmonizing height reference frames. Conversely, comparisons with the gauge station revealed significant systematic biases (+18 cm vs. DEM; +44 cm vs. SWOT), attributed to the gauge’s undefined vertical datum. Despite this bias, the SWOT and gauge time series showed a reasonable correlation. These findings demonstrate the applicability of SWOT data for monitoring small reservoirs but underscore the critical challenge of vertical inconsistency across observing systems. Also, the study highlights the urgent need for unified vertical reference frames to ensure the accurate integration of heterogeneous hydrological data from different sources (satellite, aerial, and ground).

This presentation focuses on the challenge of atmospheric correction for high-resolution optical satellites (Sentinel-2) in the presence of adjacency effects, a major source of radiometric bias over small inland water bodies. Because water reflectance is extremely low in the visible and near-infrared, even small contributions of photons scattered from surrounding land surfaces can distort surface reflectance estimates of the observed water body. Traditional physics-based models such as 6SV offer radiative consistency but are limited by assumptions of atmospheric homogeneity and Lambertian surfaces, while empirical and semi-empirical approaches struggle to generalize across diverse atmospheric and geometric conditions.

This project addresses these limitations by developing a Physics-Informed Machine Learning (PIML) pipeline. We emulate heavy 3D Monte Carlo simulations to generate synthetic point-spread function (PSF) datasets. These datasets feed a tabular foundation model (TabPFN), leveraging In-Context Learning to capture the adjacency effect's non-linear dynamics without architectural retraining. We compare TabPFN against classical machine learning (XGBoost) using Sentinel-2 and in situ data. Results demonstrate TabPFN's superiority in resolving complex higher-order scattering, offering a rapid, physically consistent operational pipeline.