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

Processing of optic and radar images.Application in satellite remote sensing of snow, ice and glaciers

Ce document présente une synthèse de mes activités de recherche depuis la soutenance de ma thèse en 1999. L'activité rapportée ici est celle d'un ingénieur de recherche, et donc s'est déroulée en parallèle d'une activité ``technique'' comprenant des taches d'instrumentation en laboratoire, d'instrumentation de plateformes en montagne, de raids scientifiques sur les calottes polaires, d'élaboration de projets scientifiques, d'organisation d'équipes ou d'ordre administratif. Je suis Ingénieur de recherche CNRS depuis 2004 affecté au laboratoire Gipsa-lab, une unité mixte de recherche du CNRS, de Grenoble-INP, de l'université Joseph Fourier et de l'université Stendhal. Ce laboratoire (d'environ 400 personnes), conventionné avec l'INRIA, l'Observatoire de Grenoble et l'université Pierre Mendès France, est pluridisciplinaire et développe des recherches fondamentales et finalisées sur les signaux et les systèmes complexes.}Lors de la préparation de ma thèse (mi-temps 1995-99) au LGGE, je me suis intéressé au traitement des images de microstructures de la neige, du névé et de la glace. C'est assez naturellement que j'ai rejoint le laboratoire LIS devenu Gipsa-lab pour y développer des activités de traitement des images Radar à Synthèse d'Ouverture (RSO) appliqué aux milieux naturels neige, glace et glaciers. Etant le premier à générer un interférogramme différentiel des glaciers des Alpes, j'ai continué à travailler sur la phase interférométrique pour extraire des informations de déplacement et valider ces méthodes sur le glacier d'Argentière (massif du Mont-Blanc) qui présente l'énorme avantage de se déplacer de quelques centimètres par jour. Ces activités m'ont amené à développer, en collaboration avec les laboratoires LISTIC, LTCI et IETR, des méthodes plus générales pour extraire des informations dans les images RSO.Ma formation initiale en électronique, puis de doctorat en physique m'ont amené à mettre à profit mes connaissances en traitement d'images et des signaux, en électromagnétisme, en calcul numérique, en informatique et en physique de la neige et de la glace pour étudier les problèmes de traitement des images RSO appliqués à la glace, aux glaciers et à la neige

Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X- and Ku-Bands

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient R ≃ 0.77 between estimated and target SD, a root-mean-square error (RMSE) ≃ 13 cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with R = 0.74, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount of in situ data required for training the algorithm and of extending the algorithm exportability to other test sites

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

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

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

Forward Modelling of Multifrequency SAR Backscatter of Snow-Covered Lake Ice: Investigating Varying Snow and Ice Properties Within a Radiative Transfer Framework

Lakes are a key feature in the Northern Hemisphere landscape. The coverage of lakes by ice cover has important implications for local weather conditions and can influence energy balance. The presence of lake ice is also crucial for local economies, providing transportation routes, and acting as a source of recreation/tourism and local customs. Both lake ice cover, from which ice dates and duration can be derived (i.e., ice phenology), and ice thickness are considered as thematic variables of lakes as an essential climate variable by the Global Climate Observing System (GCOS) for understanding how climate is changing. However, the number of lake ice phenology ground observations has declined over the past three decades. Remote sensing provides a method of addressing this paucity in observations. Active microwave remote sensing, in particular synthetic aperture radar (SAR), is popular for monitoring ice cover as it does not rely on sunlight and the resolution allows for the monitoring of small and medium-sized lakes. In recent years, our understanding of the interaction between active microwave signals and lake ice has changed, shifting from a double bounce mechanism to single bounce at the ice-water interface. The single bounce, or surface scattering, at the ice-water interface is due to a rough surface and high dielectric contrast between ice and water. However, further work is needed to fully understand how changes in different lake ice properties impact active microwave signals. Radiative transfer modelling has been used to explore these interactions, but there are a variety of limitations associated with past experiments. This thesis aimed to faithfully represent lake ice using a radiative transfer framework and investigate how changes in lake ice properties impact active microwave backscatter. This knowledge was used to model backscatter throughout ice seasons under both dry and wet conditions. The radiative transfer framework used in this thesis was the Snow Microwave Radiative Transfer (SMRT) model. To investigate how broad changes in ice properties impact microwave backscatter, SMRT was used to conduct experiments on ice columns representing a shallow lake with tubular bubbles and a deep lake without tubular bubbles at L/C/X-band frequencies. The Canadian Lake Ice Model (CLIMo) was used to parameterize SMRT. Ice properties investigated included ice thickness, snow ice bubble radius and porosity, root mean square (RMS) height of the ice-water interface, correlation length of the ice-water interface, and tubular bubble radius and porosity. Modelled backscatter indicated that changes in ice thickness, snow ice porosity, and tubular bubble radius and porosity had little impact on microwave backscatter. The property that had the largest impact on backscatter was RMS height at the ice-water interface, confirming the results of other recent studies. L and C-band frequencies were found to be most sensitive to changes in RMS height. Bubble radius had a smaller impact on backscatter, but X-band was found to be most sensitive to changes in this property and would be a valuable frequency for studying surface ice conditions. From the results of these initial experiments, SMRT was then used to simulate the backscatter from lake ice for two lakes during different winter seasons. Malcolm Ramsay Lake near Churchill, Manitoba, represented a shallow lake with dense tubular bubbles and Noell lake near Inuvik, Northwest Territories, represented a deep lake with no tubular bubbles. Both field data and CLIMo simulations for the two lakes were used to parameterize SMRT. Because RMS height was determined to be the ice property that had the largest impact on backscatter, simulations focused on optimizing this value for both lakes. Modelled backscatter was validated using C-band satellite imagery for Noell Lake and L/C/X-band imagery for Malcolm Ramsay Lake. The root mean square error values for both lakes ranged from 0.38 to 2.33 dB and Spearman’s correlation coefficient (ρ) values >0.86. Modelled backscatter for Noell Lake was closer to observed values compared to Malcolm Ramsay Lake. Optimized values of RMS height provided a better fit compared to a stationary value and indicated that roughness likely increases rapidly at the start of the ice season but plateaus as ice growth slows. SMRT was found to model backscatter from ice cover well under dry conditions, however, modelling backscatter under wet conditions is equally important. Detailed field observations for Lake Oulujärvi in Finland were used to parameterize SMRT during three different conditions. The first was lake ice with a dry snow cover, the second with an overlying layer of wet snow, and the third was when a slush layer was present on the ice surface. Experiments conducted under dry conditions continued to support the dominance of scattering from the ice-water interface. However, when a layer of wet snow or slush layer was introduced the dominant scattering interface shifted to the new wet layer. Increased roughness at the boundary of these wet layers resulted in an increase in backscatter. The increase in backscatter is attributed to the higher dielectric constant value of these layers. The modelled backscatter was found to be representative of observed backscatter from Sentinel-1. The body of work of this thesis indicated that the SMRT framework can be used to faithfully represent lake ice and model backscatter from ice covers and improved understanding of the interaction between microwave backscatter and ice properties. With this improved understanding inversion models can be developed to retrieve roughness of the ice-water interface, this could be used to build other models to estimate ice thickness based on other remote sensing data. Additionally, insights into the impact of wet conditions on radar backscatter could prove useful in identifying unsafe ice locations

Modélisation en bandes C et X de la rétrodiffusion de couverts de neige sèche : évaluation de l’apport de l’approximation quasi-cristalline pour les milieux denses.

La compréhension et la modélisation de l’interaction de l’onde électromagnétique avec la neige sont très importantes pour l’application des technologies radars à des domaines tels que l’hydrologie et la climatologie. En plus de dépendre des propriétés de la neige, le signal radar mesuré dépendra aussi des caractéristiques du capteur et du sol. La compréhension et la quantification des différents processus de diffusion du signal dans un couvert nival s’effectuent à travers les théories de diffusions de l’onde électromagnétique. La neige, dans certaines conditions, peut être considérée comme un milieu dense lorsque les particules de glace qui la composent y occupent une fraction volumique considérable. Dans un tel milieu, les processus de diffusion par les particules ne se font plus de façon indépendante, mais de façon cohérente. L’approximation quasi-cristalline pour les milieux denses est une des théories élaborées afin de prendre en compte ces processus de diffusions cohérents. Son apport a été démontré dans de nombreuses études pour des fréquences > 10 GHz où l’épaisseur optique de la neige est importante et où la diffusion de volume est prédominante. Par contre, les capteurs satellitaires radar présentement disponibles utilisent les bandes L (1-2GHz), C (4-8GHz) et X (8-12GHz), à des fréquences principalement en deçà des 10 GHz. L’objectif de la présente étude est d’évaluer l’apport du modèle de diffusion issu de l’approximation quasi-cristalline pour les milieux denses (QCA/DMRT) dans la modélisation de couverts de neige sèches en bandes C et X. L’approche utilisée consiste à comparer la modélisation de couverts de neige sèches sous QCA/DMRT à la modélisation indépendante sous l’approximation de Rayleigh. La zone d’étude consiste en deux sites localisés sur des milieux agricoles, près de Lévis au Québec. Au total 9 champs sont échantillonnés sur les deux sites afin d’effectuer la modélisation. Dans un premier temps, une analyse comparative des paramètres du transfert radiatif entre les deux modèles de diffusion a été effectuée. Pour des paramètres de cohésion inférieurs à 0,15 à des fractions volumiques entre 0,1 et 0,3, le modèle QCA/DMRT présentait des différences par rapport à Rayleigh. Un coefficient de cohésion optimal a ensuite été déterminé pour la modélisation d’un couvert nival en bandes C et X. L’optimisation de ce paramètre a permis de conclure qu’un paramètre de cohésion de 0,1 était optimal pour notre jeu de données. Cette très faible valeur de paramètre de cohésion entraîne une augmentation des coefficients de diffusion et d’extinction pour QCA/DMRT ainsi que des différences avec les paramètres de Rayleigh. Puis, une analyse de l’influence des caractéristiques du couvert nival sur les différentes contributions du signal est réalisée pour les 2 bandes C et X. En bande C, le modèle de Rayleigh permettait de considérer la neige comme étant transparente au signal à des angles d’incidence inférieurs à 35°. Vu l’augmentation de l’extinction du signal sous QCA/DMRT, le signal en provenance du sol est atténué d’au moins 5% sur l’ensemble des angles d’incidence, à de faibles fractions volumiques et fortes tailles de grains de neige, nous empêchant ainsi de considérer la transparence de la neige au signal micro-onde sous QCA/DMRT en bande C. En bande X, l’augmentation significative des coefficients de diffusion par rapport à la bande C, ne nous permet plus d’ignorer l’extinction du signal. La part occupée par la rétrodiffusion de volume peut dans certaines conditions, devenir la part prépondérante dans la rétrodiffusion totale. Pour terminer, les résultats de la modélisation de couverts de neige sous QCA/DMRT sont validés à l’aide de données RADARSAT-2 et TerraSAR-X. Les deux modèles présentaient des rétrodiffusions totales semblables qui concordaient bien avec les données RADARSAT-2 et TerraSAR-X. Pour RADARSAT-2, le RMSE du modèle QCA/DMRT est de 2,52 dB en HH et 2,92 dB en VV et pour Rayleigh il est de 2,64 dB en HH et 3,01 dB en VV. Pour ce qui est de TerraSAR-X, le RMSE du modèle QCA/DMRT allait de 1,88 dB en HH à 2,32 dB en VV et de 2,20 dB en HH à 2,71 dB en VV pour Rayleigh. Les valeurs de rétrodiffusion totales des deux modèles sont assez similaires. Par contre, les principales différences entre les deux modèles sont bien évidentes dans la répartition des différentes contributions de cette rétrodiffusion totale