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

Investigation of Sea Ice Using Multiple Synthetic Aperture Radar Acquisitions

The papers of this thesis are not available in Munin. Paper I: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Tomographic imaging of fjord ice using a very high resolution ground-based SAR system. Available in IEEE Transactions on Geoscience and Remote Sensing, 55 (2):698-714. Paper II: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Lake and fjord ice imaging using a multifrequency ground-based tomographic SAR system. Available in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10):4457-4468. Paper III: Yitayew, T. G., Divine, D. V., Dierking, W., Eltoft, T., Ferro-Famil, L., Rosel, A. & Negrel, J. Validation of Sea ice Topographic Heights Derived from TanDEMX Interferometric SAR Data with Results from Laser Profiler and Photogrammetry. (Manuscript).The thesis investigates imaging in the vertical direction of different types of ice in the arctic using synthetic aperture radar (SAR) tomography and SAR interferometry. In the first part, the magnitude and the positions of the dominant scattering contributions within snow covered fjord and lake ice layers are effectively identified by using a very high resolution ground-based tomographic SAR system. Datasets collected at multiple frequencies and polarizations over two test sites in Tromsø area, northern Norway, are used for characterizing the three-dimensional response of snow and ice. The presented experimental results helped to improve our understanding of the interaction between radar waves and snow and ice layers. The reconstructed radar responses are also used for estimating the refractive indices and the vertical positions of the different sub-layers of snow and ice. The second part of the thesis deals with the retrieval of the surface topography of multi-year sea ice using SAR interferometry. Satellite acquisitions from TanDEM-X over the Svalbard area are used for analysis. The retrieved surface height is validated by using overlapping helicopter-based stereo camera and laser profiler measurements, and a very good agreement has been found. The work contributes to an improved understanding regarding the potential of SAR tomography for imaging the vertical scattering distribution of snow and ice layers, and for studying the influence of both sensor parameters such as its frequency and polarization and scene properties such as layer stratification, air bubbles and small-scale roughness of the interfaces on snow and ice backscattered signal. Moreover, the presented results reveal the potential of SAR interferometry for retrieving the surface topography of sea ice

Estimating Snow Accumulation and Ablation with L-Band Interferometric Synthetic Aperture Radar (InSAR)

Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions

Advancing the Monitoring Capabilities of Mountain Snowpack Fluctuations at Various Spatial and Temporal Scales

Snow is a critical water resource for the western US and many regions across the globe. However, our ability to accurately monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge in mountain regions. No single sensor currently has the ability to directly measure snow water equivalent (SWE) from space at a spatial scale suitable for water supply forecasting in mountain environments. This knowledge gap calls for the innovative use of remote sensing techniques, computational tools, and data science methods to advance our ability to estimate mountain snowpacks across a range of spatial and temporal scales. The goal of this dissertation is to advance our capabilities for understanding snowpack across watershed-relevant spatial and temporal scales. Two research approaches were used to accomplish this goal: quantifying the physiographic controls and sensitivities of hydrologically important snow metrics and progressing our ability to use L-band interferometric synthetic aperture radar (InSAR) to measure SWE changes. First, we quantify the physiographic controls and various snowpack metrics in the Sierra Nevada using a novel gridded SWE reanalysis dataset. Such work demonstrates the complexity of snowpack processes and the need for fine-resolution snowpack information. Next, using L-band Interferometric Synthetic Aperture Radar (InSAR) from the NASA SnowEx campaign, both snow ablation and accumulation are estimated in the Jemez Mountains, NM. The radar-derived retrievals are evaluated utilizing a combination of optical snow-cover data, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). Lastly, we compare multisensor optical-radar approaches for SWE retrievals and find that moderate-resolution legacy satellite products provide sufficient results. The results of this work show that L-band InSAR is a suitable technique for global SWE monitoring when used synergistically with optical SCA data and snowpack modeling. While two distinctive methods are present in this research, they both work towards advancing our ability to understand the dynamics of mountain snowpack

SAR (Synthetic Aperture Radar). Earth observing system. Volume 2F: Instrument panel report

The scientific and engineering requirements for the Earth Observing System (EOS) imaging radar are provided. The radar is based on Shuttle Imaging Radar-C (SIR-C), and would include three frequencies: 1.25 GHz, 5.3 GHz, and 9.6 GHz; selectable polarizations for both transmit and receive channels; and selectable incidence angles from 15 to 55 deg. There would be three main viewing modes: a local high-resolution mode with typically 25 m resolution and 50 km swath width; a regional mapping mode with 100 m resolution and up to 200 km swath width; and a global mapping mode with typically 500 m resolution and up to 700 km swath width. The last mode allows global coverage in three days. The EOS SAR will be the first orbital imaging radar to provide multifrequency, multipolarization, multiple incidence angle observations of the entire Earth. Combined with Canadian and Japanese satellites, continuous radar observation capability will be possible. Major applications in the areas of glaciology, hydrology, vegetation science, oceanography, geology, and data and information systems are described

Re-evaluating Scattering Mechanisms in Snow-Covered Freshwater Lake Ice Containing Bubbles Using Polarimetric Ground-based and Spaceborne Radar Data

Lakes are a prominent feature of the sub-Arctic and Arctic regions of North America, covering up to 40% of the landscape. Seasonal ice cover on northern lakes afford habitat for several flora and fauna species, and provide drinking water and overwintering fishing areas for local communities. The presence of lake ice influences lake-atmosphere exchanges by modifying the radiative properties of the lake surface and moderating the transfer of heat to the atmosphere. The thermodynamic aspects of lakes exhibit a pronounced effect on weather and regional climate, but are also sensitive to variability in climate forcings such as air temperature and snow fall, acting as proxy indicators of climate variability and change. To refine the understanding of lake-climate interactions, improved methods of monitoring lake ice properties are needed. Manual lake ice monitoring stations have dropped significantly since the 1990s and existing stations are restricted to populated and coastal regions. Recently, studies have indicated the use of radar remote sensing as a viable option for the monitoring of small lakes in remote regions due to its high spatial resolution and imaging capability independent of solar radiation or cloud cover. Active microwave radar in the frequency range of 5 – 10 GHz have successfully retrieved lake ice information pertaining to the physical status of the ice cover and areas that are frozen to bed, but have not been demonstrated as effective for the derivation of on-ice snow depth. In the 10 – 20 GHz range, radar has been shown to be sensitive to terrestrial snow cover, but has not been investigated over lakes. Utilizing a combination of spaceborne and ground-based radar systems spanning a range of 5 – 17 GHz, simulations from the Canadian Lake Ice Model (CLIMo), and ice thickness information from a shallow water ice profiler (SWIP), this research aimed to further our understanding of lake ice scattering sources and mechanisms for small freshwater lakes in the sub-Arctic. Increased comprehension of scattering mechanisms in ice advances the potential for the derivation of lake ice properties, including on-ice snow depth, lake ice thickness and identification of surface ice types. Field observations of snow-covered lake ice were undertaken during the winter seasons of 2009-2010 and 2010-2011 on Malcolm Ramsay Lake, near Churchill Manitoba. In-situ snow and ice observations were coincident with ground-based scatterometer (UW-Scat) and spaceborne synthetic aperture radar (SAR) acquisitions. UW-Scat was comprised of two fully polarimetric frequency modulated continuous wave (FMCW) radars with centre frequencies of 9.6 and 17.2 GHz (X- and Ku-bands, respectively). SAR observations included fine-beam fully polarimetric RADARSAT-2 acquisitions, obtained coincident to UW-Scat observations during 2009-2010. Three experiments were conducted to characterize and evaluate the backscatter signatures from snow-covered freshwater ice coincident to in-situ snow and ice observations. To better understand the winter backscatter (σ°) evolution of snow covered ice, three unique ice cover scenarios were observed and simulated using a bubbled ice σ° model. The range resolution of UW-SCAT provided separation of microwave interaction at the snow/ice interface (P1), and within the ice volume (P2). Ice cores extracted at the end of the observation period indicated that a considerable σ° increase at P2 of approximately 10 – 12 decibels (dB) HH/VV at X- and Ku-band occurred coincident to the timing of tubular bubble development in the ice. Similarly, complexity of the ice surface (high density micro-bubbles and snow ice) resulted in increased P1 σ° at X- and Ku-band at a magnitude of approximately 7 dB. P1 observations also indicated that Ku-band was sensitive to snowpack overlying lake ice, with σ° exhibiting a 5 (6) dB drop for VV (HH) when ~ 60 mm SWE is removed from the scatterometer field of view. Observations indicate that X-band was insensitive to changes in overlying snowpack within the field of view. A bubbled ice σ° model was developed using the dense medium radiative transfer theory under the Quasi-Crystalline Approximation (DMRT-QCA), which treated bubbles as spherical inclusions within the ice volume. Results obtained from the simulations demonstrated the capability of the DMRT model to simulate the overall magnitude of observed σ° using in-situ snow and ice measurements as input. This study improved understanding of microwave interaction with bubble inclusions incorporated at the ice surface or within the volume. The UW-Scat winter time series was then used to derive ice thickness under the assumption of interactions in range occurring at the ice-snow and ice-water interface. Once adjusted for the refractive index of ice and slant range, the distance between peak returns agreed with in-situ ice thickness observations. Ice thicknesses were derived from the distance of peak returns in range acquired in off-nadir incidence angle range 21 - 60°. Derived ice thicknesses were compared to in-situ measurements provided by the SWIP and CLIMo. Median ice thicknesses derived using UW-Scat X- and Ku-band observations agreed well with in-situ measurements (RMSE = 0.053 and 0.045 m), SWIP (RMSE = 0.082 and 0.088 m) and Canadian Lake Ice Model (CLIMo) simulations using 25% of terrestrial snowpack scenario (RMSE = 0.082 and 0.079), respectively. With the launch of fully polarimetric active microwave satellites and upcoming RADARSAT Constellation Mission (RCM), the utility of polarimetric measurements was observed for freshwater bubbled ice to further investigate scattering mechanisms identified by UW-Scat. The 2009-2010 time series of UW-Scat and RADARSAT-2 (C-band) fully polarimetric observations coincident to in-situ snow and ice measurements were acquired to identify the dominant scattering mechanism in bubbled freshwater lake ice. Backscatter time series at all frequencies show increases from the ice-water interface prior to the inclusion of tubular bubbles in the ice column based on in-situ observations, indicating scattering mechanisms independent of double-bounce scatter, contrary to the longstanding hypothesis of double-bounce scatter off tubular bubbles and the ice-water interface. The co-polarized phase difference of interactions at the ice-water interface from both UW-Scat and SAR observations were centred at 0°, indicating a scattering regime other than double bounce. A Yamaguchi three-component decomposition of the time series suggested the dominant scattering mechanism to be single-bounce off the ice-water interface with appreciable surface roughness or preferentially oriented facets. Overall, this work provided new insight into the scattering sources and mechanisms within snow-covered freshwater lake ice containing spherical and tubular bubbles

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

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

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