Wet Snow Mapping in Southern Ontario with Sentinel-1A Observations

Wet snow is defined as snow with liquid water present in an ice-water mix. It can be an indicator for the onset of the snowmelt period. Knowledge about the extent of wet snow area can be of great importance for the monitoring of seasonal snowmelt runoff with climate-induced changes in snowmelt duration. Moreover, effective monitoring wet snow cover has implications for operational hydrological and ecological applications. Spaceborne microwave remote sensing has been used to observe seasonal snow under all-weather conditions. Active microwave observations of snow at C-band are sensitive to wet snow due to the high dielectric contrast with non-wet snow surfaces. Synthetic aperture radar (SAR) is now openly available to identify and map the wet snow areas globally at relatively fine spatial resolutions (~100m). In this study, a semi-automated workflow was developed from the change detection thresholding method of Nagler et al. (2016) using multi-temporal Sentinel-1A (S1A) dual-polarization observations of Southern Ontario. Regions of Interest (ROIs) were created for agricultural lands to analyze the factors influencing backscatter responses from wet snow. To compare with the thresholding method, logistic regression and Support Vector Machine (SVM) classifications were applied on the datasets. Weather station data and visible-infrared satellite observations were used as ground reference to evaluate the wet snow area estimates. Even though the study merely focused on agricultural land, the results indicated the feasibility of the change detection method with a threshold of -2dB on non-mountainous areas and addressed the usefulness of Sentinel-1A data for wet snow mapping. However, with the capability of identifying non-linear characteristics of the datasets, classification methods tended to be a more accurate method for wet snow mapping. Moreover, this study has suggested using Sentinel-1A data with large incidence for wet snow mapping is feasible

Stochastic Approach in Wet Snow Detection Using Multitemporal SAR Data

International audienceThis paper introduces an alternative strategy for wet snow detection using multitemporal SAR data. The proposed change detection method is primarily based on the comparison between two X band SAR images acquired during the accumulation (winter) and the melting (spring) seasons, in the French Alps. The new decision criterion relies on the local intensity statistics of the SAR images by considering the backscattering ratio as a stochastic process: the probability that "the intensity ratio fits into the predetermined range of values" is larger than a defined confidence level. Both the conducted snow backscattering simulations and the state of the art measurements indicate more complex relation between the backscattering properties of the two snow types, with respect to the conventional assumption of the augmented electromagnetic absorption associated to the wet snow. Therefore, rather than adopting the standard hypothesis, we analyse the wet/dry snow backscattering ratio as a function of the local incidence angle (LIA). After employing the multi-layer snow backscattering simulator, calibrated with scatterometer measurements in C band, we modify, to some extent, the range of ratio values indicating the presence of the wet snow, by including positive ratio values for lower LIA. By simultaneously accounting for the speckle noise, the proposed stochastic approach derives the refined wet snow probability map. The performance analyses are carried out both through the comparison with the ground air temperature map and by comparing two co-polarized channels processed separately

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources