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

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

1D-Var multilayer assimilation of X-band SAR data into a detailed snowpack model

International audienceThe structure and physical properties of a snow-pack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential diver-gences and accumulated errors, to a limited spatial resolu-tion, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and im-prove its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parame-ters, like its depth, snow grain size and density. SAR acqui-sitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectiv-ity of a snowpack from a set of physical descriptors, in or-der to constrain a physical snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the snowpack evolution model Crocus. The physical properties of a snowpack, such as snow density and optical diameter of each layer, are simu-lated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snow-pack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensi-tivity of the EBM to snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observa-tions obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated snowpack. The algorithm then re-initializes Crocus with the modified snowpack physical parameters, allowing it to continue the simulation of snowpack evolution, with adjustments based on remote sensing information. This method is evaluated us-ing multi-temporal TerraSAR-X images acquired over the specific site of the Argentière glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of snowpack simulated by Crocus

Vegetation, topography and snow melt at the Forest-Tundra Ecotone in arctic Europe: a study using synthetic aperture radar

This research was conducted as part of DART (Dynamic Response of the Forest-Tundra Ecotone to Environmental Change), a four year (1998-2002) European Commission funded international programme of research addressing the potential dynamic response of the (mountain birch) forest-tundra ecotone to environmental change. Satellite remote sensing was used to map landscape scale (lO(^1)-lO(^3) m) patterns of vegetation and spatial dynamics of snow melt at the forest-tundra ecotone at three sites along ca. an 8º latitudinal gradient in the Fermoscandian mountain range. Vegetation at the forest-tundra ecotone was mapped using visible -near infrared (VIR) satellite imagery, with class definition dependent on the timing of the acquisition of imagery (related to highly dynamic vegetation phenology) and spatial variation in the FTE. Multi-temporal spacebome ERS-2 synthetic aperture radar (SAR) was used for mapping snow melt. Comprehensive field measurements of snow properties and meteorological data combined with a physically based snow backscatter model indicated potential for mapping wet snow cover at each site. Significant temporal backscatter signatures enabled a classification algorithm to be developed to map the pattern of snow melt across the forest- tundra ecotone. However, diurnal and seasonal melt-freeze effects relative to the timing of ERS-2 SAR image acquisition effectively reduce the temporal resolution of data. Further, the study sites with large topographic variation and complex vegetative cover, provided a challenging operating environment and problems were identified with the robustness of classification during the later stages of snow melt because of the effects of vegetation. Significant associations were identified between vegetation, topography, and snow melt despite limitations in the snow mapping

NASA sea ice and snow validation plan for the Defense Meteorological Satellite Program special sensor microwave/imager

This document addresses the task of developing and executing a plan for validating the algorithm used for initial processing of sea ice data from the Special Sensor Microwave/Imager (SSMI). The document outlines a plan for monitoring the performance of the SSMI, for validating the derived sea ice parameters, and for providing quality data products before distribution to the research community. Because of recent advances in the application of passive microwave remote sensing to snow cover on land, the validation of snow algorithms is also addressed

On the estimation of temporal changes of snow water equivalent by spaceborne SAR interferometry : a new application for the Sentinel-1 mission

In this work we present a methodology for the mapping of Snow Water Equivalent (SWE) temporal variations based on the Synthetic Aperture Radar (SAR) Interferometry technique and Sentinel-1 data. The shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer is isolated and exploited to generate maps of temporal variation of SWE from coherent SAR interferograms. The main advantage of the proposed methodology with respect to those based on the inversion of microwave SAR backscattering models is its simplicity and the reduced number of required in-situ SWE measurements. The maps, updated up to every 6 days, can attain a spatial resolution up to 20 m with sub-centimetre ASWE measurement accuracy in any weather and sun illumination condition. We present results obtained using the proposed methodology over a study area in Finland. These results are compared with in-situ measurements of ASWE, showing a reasonable match with a mean accuracy of about 6 mm.Peer reviewe

Snow Depth Variability in the Northern Hemisphere Mountains Observed from Space

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change