Developing Parameter Constraints for Radar-based SWE Retrievals

Terrestrial snow is an important freshwater reservoir with significant influence on the climate and energy balance. It exhibits natural spatiotemporal variability which has been enhanced by climate change, thus it is important to monitor on a large scale. Active microwave, or radar remote sensing has shown frequency-dependent promise in this regard, however, interpretation remains a challenge. The aim of this thesis was to develop constraints for radar based SWE retrievals which characterize and limit uncertainty with a focus on the underlying physical processes, snowpack stratigraphy, the influence of vegetation, and effects of background scattering. The University of Waterloo Scatterometer (UWScat) was used to make measurements at 9.6 and 17.2 GHz of snow and bare ground in a series of field-based campaigns in Maryhill and Englehart, ON, Grand Mesa, CO (NASA SnowEx campaign, year 1), and Trail Valley Creek, NT. Additional measurements from Tobermory, ON, and Churchill, MB (Canadian Snow and Ice Experiment) were included. The Microwave Emission Model for Layered Snowpacks, Version 3, adapted for backscattering (MEMLS3&a) was used to explore snowpack parameterization and SWE retrieval and the Freeman-Durden three component decomposition (FD3c) was used to leverage the polarimetric response. Physical processes in the snow accumulation environment demonstrated influence on regional snowpack parameterization and constraints in a SWE retrieval context with a single-layer snowpack parameterization for Maryhill, ON and a two-layer snowpack parameterization for Englehart, ON resulting in a retrieval RMSE of 21.9 mm SWE and 24.6 mm SWE, respectively. Use of in situ snow depths improved RMSE to 12.0 mm SWE and 10.9 mm SWE, while accounting for soil scattering effects further improved RMSE by up to 6.3 mm SWE. At sites with vegetation and ice lenses, RMSE improved from 60.4 mm SWE to 21.1 mm SWE when in situ snow depths were used. These results compare favorably with the common accuracy requirement of RMSE ≤ 30 mm and underscore the importance of understanding the driving physical processes in a snow accumulation environment and the utility of their regional manifestation in a SWE retrieval context. A relationship between wind slab thickness and the double-bounce component of the FD3c in a tundra snowpack was introduced for incidence angles ≥ 46° and wind slab thickness ≥ 19 cm. Estimates of wind slab thickness and SWE resulted in an RMSE of 6.0 cm and 5.5 mm, respectively. The increased double-bounce scattering was associated with path length increase within a growing wind slab layer. Signal attenuation in a sub-canopy SWE retrieval was also explored. The volume scattering component of the FD3c yielded similar performance to forest fraction in the retrieval with several distinct advantages including a real-time description of forest condition, accounting for canopy geometry without ancillary information, and providing coincident information on forest canopy in remote locations. Overall, this work demonstrated how physical processes can manifest regional outcomes, it quantified effects of natural inclusions and background scattering on SWE retrievals, it provided a means to constrain wind slab thickness in a tundra environment, and it improved characterization of coniferous forest in a sub-canopy SWE retrieval context. Future work should focus on identifying ice and vegetation conditions prior to SWE retrieval, testing the spatiotemporal validity of the methods developed herein, and finally, improving the integration of snowpack attenuation within retrieval efforts

Remote Sensing Observations of Tundra Snow with Ku- and X-band Radar

Seasonal patterns of snow accumulation in the Northern Hemisphere are changing in response to variations in Arctic climate. These changes have the potential to influence global climate, regional hydrology, and sensitive ecosystems as they become more pronounced. To refine our understanding of the role of snow in the Earth system, improved methods to characterize global changes in snow extent and mass are needed. Current space-borne observations and ground-based measurement networks lack the spatial resolution to characterize changes in volumetric snow properties at the scale of ground observed variation. Recently, radar has emerged as a potential complement to existing observation methods with demonstrated sensitivity to snow volume at high spatial resolutions (< 200 m). In 2009, this potential was recognized by the proposed European Space Agency Earth Explorer mission, the Cold Regions High Resolution Hydrology Observatory (CoReH2O); a satellite based dual frequency (17.2 and 9.6 GHz) radar for observation of cryospheric variables including snow water equivalent (SWE). Despite increasing international attention, snow-radar interactions specific to many snow cover types remain unevaluated at 17.2 or 9.6 GHz, including those common to the Canadian tundra. This thesis aimed to use field-based experimentation to close gaps in knowledge regarding snow-microwave interaction and to improve our understanding of how these interactions could be exploited to retrieve snow properties in tundra environments. Between September 2009 and March 2011, a pair of multi-objective field campaigns were conducted in Churchill, Manitoba, Canada to collect snow, ice, and radar measurements in a number of unique sub-arctic environments. Three distinct experiments were undertaken to characterize and evaluate snow-radar response using novel seasonal, spatial, and destructive sampling methods in previously untested terrestrial tundra environments. Common to each experiment was the deployment of a sled-mounted dual-frequency (17.2 and 9.6 GHz) scatterometer system known as UW-Scat. This adaptable ground-based radar system was used to collect backscatter measurements across a range of representative tundra snow conditions at remote terrestrial sites. The assembled set of measurements provide an extensive database from which to evaluate the influence of seasonal processes of snow accumulation and metamorphosis on radar response. Several advancements to our understanding of snow-radar interaction were made in this thesis. First, proof-of-concept experiments were used to establish seasonal and spatial observation protocols for ground-based evaluation. These initial experiments identified the presence of frequency dependent sensitivity to evolving snow properties in terrestrial environments. Expanding upon the preliminary experiments, a seasonal observation protocol was used to demonstrate for the first time Ku-band and X-band sensitivity to evolving snow properties at a coastal tundra observation site. Over a 5 month period, 13 discrete scatterometer observations were collected at an undisturbed snow target where Ku-band measurements were shown to hold strong sensitivity to increasing snow depth and water equivalent. Analysis of longer wavelength X-band measurements was complicated by soil response not easily separable from the target snow signal. Definitive evidence of snow volume scattering was shown by removing the snowpack from the field of view which resulted in a significant reduction in backscatter at both frequencies. An additional set of distributed snow covered tundra targets were evaluated to increase knowledge of spatiotemporal Ku-band interactions. In this experiment strong sensitivities to increasing depth and SWE were again demonstrated. To further evaluate the influence of tundra snow variability, detailed characterization of snow stratigraphy was completed within the sensor field of view and compared against collocated backscatter response. These experiments demonstrated Ku-band sensitivity to changes in tundra snow properties observed over short distances. A contrasting homogeneous snowpack showed a reduction in variation of the radar signal in comparison to a highly variable open tundra site. Overall, the results of this thesis support the single frequency Ku-band (17.2 GHz) retrieval of shallow tundra snow properties and encourage further study of X-band interactions to aid in decomposition of the desired snow volume signal.4 month

Modélisation de l’émission micro-onde hivernale en forêt boréale canadienne

La caractérisation du couvert nival en forêt boréale est un élément important pour la compréhension des régimes climatiques et hydrologiques. Depuis plusieurs années, l’utilisation des micro-ondes passives est étudiée pour l’estimation de l’équivalent en eau de la neige (SWE : Snow Water Equivalent) à partir de capteurs satellitaires. Les algorithmes empiriques traditionnels étant limités en forêt boréale, le couplage d’un modèle de transfert radiatif (MTR) micro-onde passive (qui prend en compte les contributions du sol, de la neige, de la végétation et de l’atmosphère) avec un modèle de neige pour l’inversion du SWE semble une avenue prometteuse. La thèse vise donc à coupler un MTR avec le schéma de surface du modèle climatique canadien (CLASS) dans une perspective d’application opérationnelle pour les estimations de SWE à partir de données satellitaires micro-onde à 10.7, 19 et 37 GHz. Dans ce contexte, certains aspects centraux du MTR, dont l’effet de la taille des grains ainsi que la contribution de la végétation sont développés et quantifiés. Le premier aspect étudié dans la thèse concerne l’adaptation du modèle d’émission micro-onde passive DMRT-ML (Dense media radiative transfer theory – multi layer) pour l’intégration d’une nouvelle métrique représentant la taille des grains (surface spécifique des grains de neige: SSA). L’étude basée sur des mesures radiométriques et de neige in situ, montre la pertinence de l’utilisation de la SSA dans DMRT-ML et permet d’analyser le sens physique de l’adaptation nécessaire pour amener le modèle à simuler les températures de brillance (T[indice inférieur B) de la neige avec une erreur quadratique moyenne minimale de l’ordre de 13 K. Dans un contexte du couplage entre le modèle de neige de CLASS et DMRT-ML, un modèle d’évolution de la SSA est ensuite implémenté dans CLASS. Les SSA simulées par le module développé sont validées avec des données in situ basées sur la réflectance de la neige dans l’infrarouge à courte longueur d’onde pour différents types d’environnement. Au niveau de la contribution de la végétation, le modèle γ-ω a été étudié à partir de différentes bases de données (satellite, avion et au sol) en forêt boréale dense. L’étude montre l’importance de la considération de la diffusion (ω) pour l’estimation de l’émission de la végétation, paramètre auparavant généralement négligé aux hautes fréquences. Ensuite, des relations entre les transmissivités et certains paramètres structuraux de la forêt, dont l’indice de surface foliaire (LAI), ont été établies pour des forêts boréales en été. Des valeurs d’albédo de diffusion (ω) ainsi que les paramètres définissant la réflectivité du sol (QH) en forêt boréale ont aussi été inversées. Finalement, les simulations de T [indice inférieur] B issues du couplage du MTR (DMRT-ML, modèle γ-ω, et modèle atmosphérique) avec CLASS (dont les SSA simulées) ont été comparées avec les données AMSR-E sur une série temporelle continue de sept ans. Les premières comparaisons montrent une différence entre les paramètres de végétation (γ-ω) d’été et d’hiver, ainsi qu’une importante contribution des croûtes de glace dans la neige au signal. Les simulations du modèle ajusté montrent une bonne correspondance avec les observations d’AMSR-E (de l’ordre de 3 à 7 K selon la fréquence et la polarisation). Des tests de sensibilité montrent par contre une faible sensibilité du MTR/CLASS au SWE pour des forêts denses et des couverts nivaux épais. Le MTR-CLASS développé pourrait permettre l’assimilation de températures de brillance satellitaires en forêt boréale dans des systèmes opérationnels pour l’amélioration de paramètres de surface, dont la neige, dans les modèles météorologiques et climatiques

Développement d’un système d’assimilation de mesures satellites micro-ondes passives dans un modèle de neige pour la prévision hydrologique au Québec

Dans le contexte québécois (Est du Canada), une bonne gestion de la ressource en eau est devenue un enjeu économique majeur et permet également d’éviter d’importantes catastrophes naturelles lors des crues printanières. La plus grande incertitude des modèles de prévision hydrologique résulte de la méconnaissance de la quantité de neige au sol accumulée durant l’hiver. Pour optimiser la gestion de ses barrages hydroélectriques, l'entreprise Hydro-Québec veut pouvoir mieux quantifier et anticiper l'apport en eau que représentera la fonte des neiges au printemps. Cet apport est estimé à partir de l’équivalent en eau de la neige (‘ÉEN’, ou Snow Water Equivalent, ‘SWE’) extrapolé sur l’ensemble d’un territoire. Cette étude se concentre sur la zone subarctique et boréale du Québec (58° - 45°N) incluant les bassins hydrographiques du complexe de la Baie James et du sud du Québec. Ces territoires représentent des régions immenses et hétérogènes difficiles d’accès. Le faible nombre de stations météorologiques permanentes et de relevés nivométriques entrainent de fortes incertitudes dans l’extrapolation de l’équivalent en eau de la neige, que ce soit à partir de mesures au sol ou de modèles de neige pilotés par des forçages météorologiques. La couverture quasi - quotidienne et globale des observations satellitaires est donc une source d’information au potentiel certain, mais encore peu utilisée pour ajuster les estimations de l’ÉEN dans les modèles hydrologiques. Utilisant les observations satellitaires micro-ondes passives (MOP) et des mesures de hauteurs de neige au sol pour ajuster les cartes de neige interpolées, le produit ÉEN GlobSnow2 est actuellement considéré comme un des plus performants à l’échelle globale. En comparant ce produit à une série temporelle de 30 ans de données au sol sur l’Est du Canada (1980 – 2009, avec un total de 38 990 mesures d’ÉEN), nous avons montré que sa précision n'était pas adaptée pour les besoins d'Hydro-Québec, avec une erreur quadratique moyenne (RMSE) relative de l'ordre de 36%. Une partie des incertitudes provient de la non représentativité des mesures de hauteur de neige au sol. Ce travail de thèse s'est donc concentré sur l'amélioration de la prédiction du couvert nival au Québec par l’assimilation des observations satellitaires MOP sans utilisation de relevés au sol. Les observations, décrites comme des températures de brillance (TB), sont fournies par les radiomètres AMSR-2 (Advanced Microwave Scanning Radiometer – 2) embarqués sur le satellite Jaxa (10 x 10 km2). L’approche développée propose de coupler un modèle de neige (Crocus de Météo-France) avec un modèle de transfert radiatif (DMRT-ML du LGGE, Grenoble) pour simuler l’émission du manteau neigeux modélisé. Des modèles de transfert radiatifs de végétation, de sol et d’atmosphère sont ajoutés et calibrés pour représenter le signal MOP au niveau des capteurs satellitaires. Les observations MOP d’AMSR-2 sont alors assimilées en réajustant directement les forçages atmosphériques pilotant le modèle de neige. Ces forçages sont dérivés du modèle de prévision atmosphérique canadien GEM à 10 km de résolution spatiale. Le système d’assimilation implémenté est un filtre particulaire par rééchantillonnage d’importance. La chaîne de modèles a été calibrée et validée avec des mesures au sol de radiométrie micro-onde et des relevés continus d’ÉEN et de hauteurs de neige. L’assimilation des TB montre d'excellents résultats avec des observations synthétiques simulées, améliorant la RMSE sur l’ÉEN de 82% comparé aux simulations d’ÉEN sans assimilation. Les experiences préliminaires de l’assimilation des observations satellitaires d’AMSR-2 en 11, 19 et 37 GHz (verticale polarization) montrent une amélioration significative des biais sur les ÉEN simulés sur un important jeu de données ponctuelles (12 stations de mesures d’ÉEN continues sur 4 années). La moyenne des biais inversés des valeurs d’ÉEN moyens et maximums sont réduits respectivement de 71 % et 32 % par rapport aux simulations d’ÉEN sans assimilation. Avec l’assimilation des observations d’AMSR-2 et pour les sites avec moins de 75 % de couverts forestiers, le pourcentage d'erreur relative sur l’ÉEN par rapport aux observations est de 15 % (contre 20 % sans assimilation), soit une précision significativement améliorée pour des applications hydrologiques. Ce travail ouvre de nouvelles perspectives très prometteuses pour la cartographie d’ÉEN à des fins hydrologiques sur une base journalière

Modelling lake ice cover under contemporary and future climate conditions

Lakes comprise a large portion of the surface cover in northern North America, forming an important part of the cryosphere. Further alterations to the present day ice regime could result in major ecosystem changes, such as species shifts and the disappearance of perennial ice cover. Lake ice has been shown to both respond to, and play a role in the local/regional climate. The timing of lake ice phenological events (e.g. break-up/freeze-up) is a useful indicator of climate variability and change. Trends in ice phenology have typically been associated with variations in air temperatures while trends found in ice thickness tend to be associated more with changes in snow cover. The inclusion of lakes and lake ice in climate modelling is an area of increased attention in recent studies and the ability to accurately represent ice cover on lakes will be an important step in the improvement of global circulation models, regional climate models and numerical weather forecasting. This thesis aimed to further our understanding of lake ice and climate interactions, with an emphasis on ice cover modelling. The Canadian Lake Ice Model (CLIMo) was used throughout for lake ice simulations. To validate and improve the model results, in situ measurements of the ice cover for two seasons in Churchill, MB were obtained using an upward-looking sonar device Shallow Water Ice Profiler (SWIP) installed on the bottom of the lake. The SWIP identified the ice-on/off dates as well as collected ice thickness measurements. In addition, a digital camera was installed on shore to capture images of the ice cover through the seasons and field measurements were obtained of snow depth on the ice, and both the thickness of snow ice (if present) and total ice cover. Altering the amounts of snow cover on the ice surface to represent potential snow redistribution affected simulated freeze-up dates by a maximum of 22 days and break-up dates by a maximum of 12 days, highlighting the importance of accurately representing the snowpack for lake ice modelling. The late season ice thickness tended to be under estimated by the simulations with break-up occurring too early, however, the evolution of the ice cover was simulated to fall between the range of the full snow and no snow scenario, with the thickness being dependent on the amount of snow cover on the ice surface. CLIMo was then used to simulate lake ice phenology across the North American Arctic from 1961–2100 using two climate scenarios produced by the Canadian Regional Climate Model (CRCM). Results from the 1961–1990 time period were validated using 15 locations across the Canadian Arctic, with both in situ ice cover observations from the Canadian Ice Database as well as additional ice cover simulations using nearby weather station data. Projected changes to the ice cover using the 30-year mean data between 1961–1990 and 2041–2070 suggest a shift in break-up and freeze-up dates for most areas ranging from 10–25 days earlier (break-up) and 0–15 days later (freeze-up). The resulting ice cover durations show mainly a 10–25 day reduction for the shallower lakes (3 and 10 m) and 10–30 day reduction for the deeper lakes (30 m). More extreme reductions of up to 60 days (excluding the loss of perennial ice cover) were shown in the coastal regions compared to the interior continental areas. The mean maximum ice thickness was shown to decrease by 10–60 cm with no snow cover and 5–50 cm with snow cover on the ice. Snow ice was also shown to increase through most of the study area with the exception of the Alaskan coastal areas. While the most suitable way to undertake wide scale lake ice modeling is to force the models with climate model output or reanalysis data, a variety of different lake morphometric conditions could exist within a given grid cell leading to different durations of ice cover within the grid cell. Both the daily IMS product (4 km) and the MODIS snow product (500 m) were assessed for their utility at determining lake ice phenology at the sub-grid cell level throughout the province of Quebec. Both products were useful for detecting ice-off, however, the MODIS product was advantageous for detecting ice-on, mainly due to the finer resolution and resulting spatial detail of the lake ice. The sub-grid cell variability was typically less than 2%, although it ranged as high as 10% for some grid cells. An indication of whether or not the simulated ice-on/off dates were within the sub-grid cell variability was determined and on average across the entire province, were found to be within the variability 62% of the time for ice-off and 80% of the time for ice-on. Forcing the model with the future climate scenarios from CRCM predicts ice cover durations throughout the region will decrease by up to 50 days from the current 1981-2010 means to the 2041-2070 means, and decrease from 15 to nearly 100 days shorter between the contemporary and 2071-2100 means. Overall, this work examined the climate-lake-ice interactions under both contemporary and future climate conditions, as well as provided new insight into sub-grid cell variability of lake ice

Effect of snow microstructure and subnivean water bodies on microwave radiometry of seasonal snow

Remote sensing using microwave radiometry is an acknowledged method for monitoring various environmental processes in the cryosphere, atmosphere, soil, vegetation and oceans. Several decades long time series of spaceborne passive microwave observations can be used to detect trends relating to climate change, while present measurements provide information on the current state of the environment. Unlike optical wavelengths, microwaves are mostly insensitive to atmospheric and lighting conditions and are therefore suitable for monitoring seasonal snow in the Arctic. One of the major challenges in the utilization of spaceborne passive microwave observations for snow measurements is the poor spatial resolution of instruments. The interpretation of measurements over heterogeneous areas requires sophisticated microwave emission models relating the measured parameters to physical properties of snow, vegetation and the subnivean layer. Especially the high contrast in the electrical properties of soil and liquid water introduces inaccuracies in the retrieved parameters close to coastlines, lakes and wetlands, if the subnivean water bodies are not accounted for in the algorithm. The first focus point of this thesis is the modelling of brightness temperature of ice- and snow-covered water bodies and their differences from snow-covered forested and open land areas. Methods for modelling the microwave signatures of water bodies and for using that information in the retrieval of snow parameters from passive microwave measurements are presented in this thesis. The second focus point is the effect of snow microstructure on its microwave signature. Even small changes in the size of scattering particles, snow grains, modify the measured brightness temperature notably. The coupling of different modelled and measured snow microstructural parameters with a microwave snow emission model and the application of those parameters in the retrieval of snow parameters from remote sensing data are studied

Measurements and modelling of seasonal snow characteristics for interpreting passive microwave observations

Information on snow water equivalent (SWE) of seasonal snow is used for various purposes, including longterm climate monitoring and river discharge forecasting. Global monitoring of SWE is made feasible through remote sensing. Currently, passive microwave observations are utilized for SWE retrievals. The main challenges in the interpretation of microwave observations include the spatial variability of snow characteristics and the inaccurate characterization of snow microstructure in retrieval algorithms. Even a minor variability in snow microstructure has a notable impact to microwave emission from the snowpack. This thesis work aims to improve snow microstructure modelling and measurement methods, and understanding the influence of snow microstructure to passive microwave observations, in order to enable a more accurate SWE estimation from remote sensing observations. The thesis work applies two types of models: physical snow models and radiative transfer models that simulate microwave emission. The physical snow models use meteorological driving data to simulate physical snow characteristics, such as SWE and snow microstructure. Models are used for different purposes such as hydrological simulations and avalanche forecasting. On the other hand, microwave emission models use physical snow characteristics for predicting microwave emission from a snowpack. Microwave emission models are applied for the interpretation of spaceborne passive microwave remote sensing observations, for example. In this study, physical snow model simulations and microwave emission model simulations are compared with field observations to investigate problems in characterizing snow for microwave emission models. An extensive set of manual field measurements of snow characteristics is used for the comparisons. The measurements are collected from taiga snow in Sodankylä, northern Finland. The representativeness of the measurements is defined by investigating the spatial and temporal variability of snow characteristics. The work includes studies on microwave emission modelling from natural snowpacks and from excavated snow slabs. Radiometric observations of microwave emission from natural snowpacks are compared with simulations from three microwave emission models coupled with three physical snow models. Additionally, homogenous snow samples are excavated from the natural snowpack during the Arctic Snow Microstructure Experiment, and the incident snow characteristics and microwave emission characteristics are measured with an experimental set-up developed for this study. Predictions of two microwave emission models are compared with the radiometric observations of collected snow samples. The results indicate that none of the model configurations can accurately simulate the microwave emission from natural snowpack or snow samples. The results also suggest that the characterization of microstructure in the applied microwave emission models is not adequate

Analyse de la modélisation de l'émission multi-fréquences micro-onde des sols et de la neige, incluant les croutes de glace à l'aide du modèle Microwave Emission Model of Layered Snowpacks (MEMLS).

Résumé : L'étude du couvert nival est essentielle afin de mieux comprendre les processus climatiques et hydrologiques. De plus, avec les changements climatiques observés dans l'hémisphère nord, des événements de dégel-regel ou de pluie hivernale sont de plus en plus courants et produisent des croutes de glace dans le couvert nival affectant les moeurs des communautés arctiques en plus de menacer la survie de la faune arctique. La télédétection micro-ondes passives (MOP) démontre un grand potentiel de caractérisation du couvert nival. Toutefois, a fin de bien comprendre les mesures satellitaires, une modélisation adéquate du signal est nécessaire. L'objectif principal de cette thèse est d'analyser le transfert radiatif (TR) MOP des sols, de la neige et de la glace a fin de mieux caractériser les propriétés géophysiques du couvert nival par télédétection. De plus, un indice de détection des croutes de glace par télédétection MOP a été développé. Pour ce faire, le modèle Microwave Emission Model of Layered Snowpacks (MEMLS) a été étudié et calibré afin de minimiser les erreurs des températures de brillance simulées en présences de croutes de glace. La première amélioration faite à la modélisation du TR MOP de la neige a été la caractérisation de la taille des grains de neige. Deux nouveaux instruments, utilisant la réflectance dans le proche infrarouge, ont été développés afin de mesurer la surface spécifique de la neige (SSA). Il a été démontré que la SSA est un paramètre plus précis et plus objectif pour caractériser la taille des grains de neige. Les deux instruments ont démontré une incertitude de 10% sur la mesure de la SSA. De plus, la SSA a été calibré pour la modélisation MOP a n de minimiser l'erreur sur la modélisation de la température de brillance. Il a été démontré qu'un facteur multiplicatif [phi] = 1.3 appliqué au paramètre de taille des grains de neige dans MEMLS, paramètre dérivé de la SSA, est nécessaire afin de minimiser l'erreur des simulations. La deuxième amélioration apportée à la modélisation du TR MOP a été l'estimation de l'émission du sol. Des mesures radiométriques MOP in-situ ainsi que des profils de températures de sols organiques arctiques gelés ont été acquis et caractérisés a fin de simuler l'émission MOP de ces sols. Des constantes diélectriques effectives à 10.7, 19 et 37 GHz ainsi qu'une rugosité de surface effective des sols ont été déterminés pour simuler l'émission des sols. Une erreur quadratique moyenne (RMSE) de 4.65 K entre les simulations et les mesures MOP a été obtenue. Suite à la calibration du TR MOP du sol et de la neige, un module de TR de la glace a été implémenté dans MEMLS. Avec ce nouveau module, il a été possible de démontré que l'approximation de Born améliorée, déjà implémenté dans MEMLS, pouvait être utilisé pour simuler des croutes de glace pure à condition que la couche de glace soit caractérisée par une densité de 917 kg m[indice supérieur _3] et une taille des grains de neige de 0 mm. Il a aussi été démontré que, pour des sites caractérisés par des croutes de glace, les températures de brillances simulées des couverts de neige avec des croutes de glace ayant les propriétés mesurées in-situ (RMSE=11.3 K), avaient une erreur similaire aux températures de brillances simulées des couverts de neige pour des sites n'ayant pas de croutes de glace (RMSE=11.5 K). Avec le modèle MEMLS validé pour la simulation du TR MOP du sol, de la neige et de la glace, un indice de détection des croutes de glace par télédétection MOP a été développé. Il a été démontré que le ratio de polarisation (PR) était très affecté par la présence de croutes de glace dans le couvert de neige. Avec des simulations des PR à 10.7, 19 et 37 GHz sur des sites mesurés à Churchill (Manitoba, Canada), il a été possible de déterminer des seuils entre la moyenne hivernale des PR et les valeurs des PR mesurés indiquant la présence de croutes de glace. Ces seuils ont été appliqués sur une série temporelle de PR de 33 hivers d'un pixel du Nunavik (Québec, Canada) où les conditions de sols étaient similaires à ceux observés à Churchill. Plusieurs croutes de glace ont été détectées depuis 1995 et les mêmes événements entre 2002 et 2009 que (Roy, 2014) ont été détectés. Avec une validation in-situ, il serait possible de confirmer ces événements de croutes de glace mais (Roy, 2014) a démontré que ces événements ne pouvaient être expliqués que par la présence de croutes de glace dans le couvert de neige. Ces mêmes seuils sur les PR ont été appliqués sur un pixel de l'Île Banks (Territoires du Nord-Ouest, Canada). L'événement répertorié par (Grenfell et Putkonen, 2008) a été détecté. Plusieurs autres événements de croutes de glace ont été détectés dans les années 1990 et 2000 avec ces seuils. Tous ces événements ont suivi une période où les températures de l'air étaient près ou supérieures au point de congélation et sont rapidement retombées sous le point de congélation. Les températures de l'air peuvent être utilisées pour confirmer la possibilité de présence de croutes de glace mais seul la validation in-situ peut définitivement confirmer la présence de ces croutes.Abstract : Snow cover studies are essential to better understand climatic and hydrologic processes. With recent climate change observed in the northern hemisphere, more frequent rain-on-snow and meltrefreeze events have been reported, which affect the habits of the northern comunities and the survival of arctique wildlife. Passive microwave remote sensing has proven to be a great tool to characterize the state of snow cover. Nonetheless, proper modeling of the microwave signal is needed in order to understand how the parameters of the snowpack affect the measured signal. The main objective of this study is to analyze the soil, snow and ice radiative transfer in order to better characterize snow cover properties and develop an ice lens detection index with satellite passive microwave brightness temperatures. To do so, the passive microwave radiative transfer modeling of the Microwave Emission Model of Layered Snowpacks (MEMLS) was improved in order to minimize the errors on the brightness temperature simulations in the presence of ice lenses. The first improvement to passive microwave radiative transfer modeling of snow made was the snow grain size parameterization. Two new instruments, based on short wave infrared reflectance to measure the snow specific surface area (SSA) were developed. This parameter was shown to be a more accurate and objective to characterize snow grain size. The instruments showed an uncertainty of 10% to measure the SSA of snow. Also, the SSA of snow was calibrated for passive microwave modeling in order to reduce the errors on the simulated brightness temperatures. It was showed that a correction factor of φ = 1.3 needed to be applied to the grain size parameter of MEMLS, obtain through the SSA measurements, to minimize the simulation error. The second improvement to passive microwave radiative transfer modeling was the estimation of passive microwave soil emission. In-situ microwave measurements and physical temperature profiles of frozen organic arctic soils were acquired and characterized to improve the modeling of the soil emission. Effective permittivities at 10.7, 19 and 37 GHz and effective surface roughness were determined for this type of soil and the soil brightness temperature simulations were obtain with a minimal root mean square error (RMSE) of 4.65K. With the snow grain size and soil contributions to the emitted brightness temperature optimized, it was then possible to implement a passive microwave radiative transfer module of ice into MEMLS. With this module, it was possible to demonstrate that the improved Born approximation already implemented in MEMLS was equivalent to simulating a pure ice lens when the density of the layer was set to 917 kg m−3 and the grain size to 0 mm. This study also showed that by simulating ice lenses within the snow with there measured properties, the RMSE of the simulations (RMSE= 11.3 K) was similar to the RMSE for simulations of snowpacks where no ice lenses were measured (only snow, RMSE= 11.5 K). With the validated MEMLS model for snowpacks with ice lenses, an ice index was created. It is shown here that the polarization ratio (PR) was strongly affected by the presence of ice lenses within the snowpack. With simulations of the PR at 10.7, 19 and 37 GHz from measured snowpack properties in Chucrhill (Manitoba, Canada), thresholds between the measured PR and the mean winter PR were determined to detect the presence of ice within the snowpack. These thresholds were applied to a timeseries of nearly 34 years for a pixel in Nunavik (Quebec, Canada) where the soil surface is similar to that of the Churchill site. Many ice lenses are detected since 1995 with these thresholds and the same events as Roy (2014) were detected. With in-situ validation, it would be possible to confirm the precision of these thresholds but Roy (2014) showed that these events can not be explained by anything else than the presence of an ice layer within the snowpack. The same thresholds were applied to a pixel on Banks island (North-West Territories, Canada). The 2003 event that was reported by Grenfell et Putkonen (2008) was detected by the thresholds. Other events in the years 1990 and 2000’s were detected with these thresholds. These events all follow periods where the air temperature were warm and were followed by a quick drop in air temperature which could be used to validate the presence of ice layer within the snowpack. Nonetheless, without in-situ validation, these events can not be confirmed

Characteristics of Taiga and Tundra Snowpack in Development and Validation of Remote Sensing of Snow

Remote sensing of snow is a method to measure snow cover characteristics without direct physical contact with the target from airborne or space-borne platforms. Reliable estimates of snow cover extent and snow properties are vital for several applications including climate change research and weather and hydrological forecasting. Optical remote sensing methods detect the extent of snow cover based on its high reflectivity compared to other natural surfaces. A universal challenge for snow cover mapping is the high spatiotemporal variability of snow properties and heterogeneous landscapes such as the boreal forest biome. The optical satellite sensor’s footprint may extend from tens of meters to a kilometer; the signal measured by the sensor can simultaneously emerge from several target categories within individual satellite pixels. By use of spectral unmixing or inverse model-based methods, the fractional snow cover (FSC) within the satellite image pixel can be resolved from the recorded electromagnetic signal. However, these algorithms require knowledge of the spectral reflectance properties of the targets present within the satellite scene and the accuracy of snow cover maps is dependent on the feasibility of these spectral model parameters. On the other hand, abrupt changes in land cover types with large differences in their snow properties may be located within a single satellite image pixel and complicate the interpretation of the observations. Ground-based in-situ observations can be used to validate the snow parameters derived by indirect methods, but these data are affected by the chosen sampling. This doctoral thesis analyses laboratory-based spectral reflectance information on several boreal snow types for the purpose of the more accurate reflectance representation of snow in mapping method used for the detection of fractional snow cover. Multi-scale reflectance observations representing boreal spectral endmembers typically used in optical mapping of snow cover, are exploited in the thesis. In addition, to support the interpretation of remote sensing observations in boreal and tundra environments, extensive in-situ dataset of snow depth, snow water equivalent and snow density are exploited to characterize the snow variability and to assess the uncertainty and representativeness of these point-wise snow measurements applied for the validation of remote sensing observations. The overall goal is to advance knowledge about the spectral endmembers present in boreal landscape to improve the accuracy of the FSC estimates derived from the remote sensing observations and support better interpretation and validation of remote sensing observations over these heterogeneous landscapes. The main outcome from the work is that laboratory-controlled experiments that exclude disturbing factors present in field circumstances may provide more accurate representation of wet (melting) snow endmember reflectance for the FSC mapping method. The behavior of snow band reflectance is found to be insensitive to width and location differences between visible satellite sensor bands utilized in optical snow cover mapping which facilitates the use of various sensors for the construction of historical data records. The results also reveal the high deviation of snow reflectance due to heterogeneity in snow macro- and microstructural properties. The quantitative statistics of bulk snow properties show that areal averages derived from in-situ measurements and used to validate remote sensing observations are dependent on the measurement spacing and sample size especially over land covers with high absolute snow depth variability, such as barren lands in tundra. Applying similar sampling protocol (sample spacing and sample size) over boreal and tundra land cover types that represent very different snow characteristics will yield to non-equal representativeness of the areal mean values. The extensive datasets collected for this work demonstrate that observations measured at various scales can provide different view angle to the same challenge but at the same time any dataset individually cannot provide a full understanding of the target complexity. This work and the collected datasets directly facilitate further investigation of uncertainty in fractional snow cover maps retrieved by optical remote sensing and the interpretation of satellite observations in boreal and tundra landscapes.Lumen kaukokartoitus on menetelmä, jolla mitataan lumen ominaisuuksia ilmasta tai avaruudesta käsin ilman fyysistä kontaktia kohteeseen. Luotettavat arviot lumipeitteen laajuudesta ja lumen ominaisuuksista ovat elintärkeitä useille menetelmille mukaan lukien ilmastonmuutoksen tutkimus sekä hydrologinen ennustaminen ja sään ennustaminen. Optiset kaukokartoitusmenetelmät havaitsevat lumipeitteen laajuuden lumen korkean heijastavuuden perusteella. Lumen ominaisuuksien korkea ajallinen ja alueellinen vaihtelu sekä heterogeeniset maastotyypit ovat yleinen haaste lumipeitteen laajuuden kaukokartoitukselle. Satelliitin optisen sensorin jalanjälki voi ulottua muutamista kymmenistä metreistä kilometriin; sensorin mittaama signaali voi samanaikaisesti nousta useista eri kohteista saman satelliittipikselin sisällä. Käyttämällä metodeja, joissa pyritään ratkaisemaan erilaisten kohdetyyppien osuus mitatussa signaalissa tai käänteismallintamalla, lumen osuus satelliittipikselin sisällä voidaan ratkaista mitatusta elektromagneettisesta signaalista. Nämä menetelmät kuitenkin vaativat tietoa pikselissä olevien kohteiden – mallimuuttujien – spektraalisista ominaisuuksista. Tuotetun lumipeitekartan tarkkuus on suoraan riippuvainen näille muuttujille asetettujen arvojen käyttökelpoisuudesta. Toisaalta saman satelliittipikselin sisällä lumipeitteen ominaisuuksissa voi olla jyrkkiäkin muutoksia, jotka vaikeuttavat satelliittihavaintojen tulkintaa. Epäsuorilla menetelmillä havaittuja lumen estimaatteja voidaan varmentaa hyödyntämällä maanpinnalla kerättyjä maastohavaintoja, mutta myös nämä aineistot sisältävät epätarkkuutta ja virhettä. Tämä väitöskirja analysoi laboratoriossa useista boreaalisista lumityypeistä kerättyjä spektraalisia mittauksia, joiden tarkoitus on tarjota tarkempia lumen heijastusarvoja hyödynnettäväksi menetelmässä, jota käytetään lumipeitteen laajuuden kartoituksessa. Boreaalisella metsävyöhykkeellä olevia spektraalisia mallimuuttujia, joita tyypillisesti käytetään optisissa lumen kartoitusmenetelmissä, kuvataan väitöstyössä usean eri mittakaavan havainnoilla. Lisäksi mittavaa lumensyvyyden, lumen vesiarvon sekä lumen tiheyden maastomittausaineistoa hyödynnetään kaukokartoitushavaintojen tulkinnan tukemiseksi boreaalisella vyöhykkeellä sekä tundralla. Aineiston avulla kuvataan lumen ominaisuuksien alueellista ja ajallista vaihtelua sekä tutkitaan pistemäisesti kerättyjen maastohavaintojen epätarkkuutta sekä edustavuutta, kun niitä käytetään kaukokartoitushavaintojen validoinnissa. Väitöstyön yleisenä tarkoituksena on edistää tietoutta boreaalisen vyöhykkeen spektraalisista mallimuuttujista, jotta optisella kaukokartoituksella tuotettujen lumipeitehavaintojen tarkkuus paranee ja tukea kaukokartoitushavaintojen parempaa tulkintaa ja validointia epähomogeenisissa satelliittipikseleissä. Väitöstyön pääasiallinen viesti on, että laboratorio-olosuhteissa kerätyillä mittauksilla voidaan tuottaa tarkempia arvoja lumipeitteen kaukokartoitushavaintojen tulkinta-algoritmeille, koska maastomittauksissa läsnä olevia häiritseviä tekijöitä voidaan sulkea pois. Lumipeitteen kaukokartoituksessa hyödynnettävien sensorien hieman toisistaan poikkeavat optiset kaistat eivät näytä merkittävästi vaikuttavan lumen heijastusarvoon. Tämä tukee historiallisten aineistojen rakentamista eri sensoreilla kerätyistä havainnoista. Tulokset myös paljastavat, että lumen heijastusarvoissa on suurta hajontaa, joka liittyy lumen makro- ja mikrostruktruuristen ominaisuuksien vaihteluun. Lisäksi tulokset osoittivat, että maastomittauksista saadut alueelliset lumensyvyyden keskiarvot, joita usein käytetään karkeamman resoluution kaukokartoitushavaintojen validoinnissa, ovat riippuvaisia mittausten välisestä etäisyydestä sekä mittausten lukumäärästä. Näin on erityisesti maanpeiteluokissa, joilla lumensyvyyden vaihtelu on erityisen suurta, kuten paljakat tundralla. Soveltamalla samaa mittausprotokollaa boreaalisiin ja tundran maanpeiteluokkiin, jotka edustavat hyvin erilaisia lumiolosuhteita, saadaan keskenään eriävästi edustavia alueellisia keskiarvoja. Tässä työssä kerätyt laajamittaiset havaintoaineistot osoittavat, että eri mittakaavoilla kerätyt havainnot voivat tarjota eri näkökulman samaan ongelmaan, mutta samaan aikaan yksittäinen havaintoaineisto on riittämätön tarjotakseen täyden ymmärryksen tiettyyn haasteeseen, kuten epähomogeenisen satelliittipikselin tulkintaan. Tämä väitöstyö ja siinä kerätyt aineistot hyödyttävät suoraan tutkimusta, joka koskee lumipeitteen laajuuden optisen kaukokartoituksen epätarkkuuksia sekä satelliittihavaintojen tulkintaa boreaalisella metsävyöhykkeellä sekä tundralla

Characteristics of Taiga and Tundra Snowpack in Development and Validation of Remote Sensing of Snow

Remote sensing of snow is a method to measure snow cover characteristics without direct physical contact with the target from airborne or space-borne platforms. Reliable estimates of snow cover extent and snow properties are vital for several applications including climate change research and weather and hydrological forecasting. Optical remote sensing methods detect the extent of snow cover based on its high reflectivity compared to other natural surfaces. A universal challenge for snow cover mapping is the high spatiotemporal variability of snow properties and heterogeneous landscapes such as the boreal forest biome. The optical satellite sensor’s footprint may extend from tens of meters to a kilometer; the signal measured by the sensor can simultaneously emerge from several target categories within individual satellite pixels. By use of spectral unmixing or inverse model-based methods, the fractional snow cover (FSC) within the satellite image pixel can be resolved from the recorded electromagnetic signal. However, these algorithms require knowledge of the spectral reflectance properties of the targets present within the satellite scene and the accuracy of snow cover maps is dependent on the feasibility of these spectral model parameters. On the other hand, abrupt changes in land cover types with large differences in their snow properties may be located within a single satellite image pixel and complicate the interpretation of the observations. Ground-based in-situ observations can be used to validate the snow parameters derived by indirect methods, but these data are affected by the chosen sampling. This doctoral thesis analyses laboratory-based spectral reflectance information on several boreal snow types for the purpose of the more accurate reflectance representation of snow in mapping method used for the detection of fractional snow cover. Multi-scale reflectance observations representing boreal spectral endmembers typically used in optical mapping of snow cover, are exploited in the thesis. In addition, to support the interpretation of remote sensing observations in boreal and tundra environments, extensive in-situ dataset of snow depth, snow water equivalent and snow density are exploited to characterize the snow variability and to assess the uncertainty and representativeness of these point-wise snow measurements applied for the validation of remote sensing observations. The overall goal is to advance knowledge about the spectral endmembers present in boreal landscape to improve the accuracy of the FSC estimates derived from the remote sensing observations and support better interpretation and validation of remote sensing observations over these heterogeneous landscapes. The main outcome from the work is that laboratory-controlled experiments that exclude disturbing factors present in field circumstances may provide more accurate representation of wet (melting) snow endmember reflectance for the FSC mapping method. The behavior of snow band reflectance is found to be insensitive to width and location differences between visible satellite sensor bands utilized in optical snow cover mapping which facilitates the use of various sensors for the construction of historical data records. The results also reveal the high deviation of snow reflectance due to heterogeneity in snow macro- and microstructural properties. The quantitative statistics of bulk snow properties show that areal averages derived from in-situ measurements and used to validate remote sensing observations are dependent on the measurement spacing and sample size especially over land covers with high absolute snow depth variability, such as barren lands in tundra. Applying similar sampling protocol (sample spacing and sample size) over boreal and tundra land cover types that represent very different snow characteristics will yield to non-equal representativeness of the areal mean values. The extensive datasets collected for this work demonstrate that observations measured at various scales can provide different view angle to the same challenge but at the same time any dataset individually cannot provide a full understanding of the target complexity. This work and the collected datasets directly facilitate further investigation of uncertainty in fractional snow cover maps retrieved by optical remote sensing and the interpretation of satellite observations in boreal and tundra landscapes.Lumen kaukokartoitus on menetelmä, jolla mitataan lumen ominaisuuksia ilmasta tai avaruudesta käsin ilman fyysistä kontaktia kohteeseen. Luotettavat arviot lumipeitteen laajuudesta ja lumen ominaisuuksista ovat elintärkeitä useille menetelmille mukaan lukien ilmastonmuutoksen tutkimus sekä hydrologinen ennustaminen ja sään ennustaminen. Optiset kaukokartoitusmenetelmät havaitsevat lumipeitteen laajuuden lumen korkean heijastavuuden perusteella. Lumen ominaisuuksien korkea ajallinen ja alueellinen vaihtelu sekä heterogeeniset maastotyypit ovat yleinen haaste lumipeitteen laajuuden kaukokartoitukselle. Satelliitin optisen sensorin jalanjälki voi ulottua muutamista kymmenistä metreistä kilometriin; sensorin mittaama signaali voi samanaikaisesti nousta useista eri kohteista saman satelliittipikselin sisällä. Käyttämällä metodeja, joissa pyritään ratkaisemaan erilaisten kohdetyyppien osuus mitatussa signaalissa tai käänteismallintamalla, lumen osuus satelliittipikselin sisällä voidaan ratkaista mitatusta elektromagneettisesta signaalista. Nämä menetelmät kuitenkin vaativat tietoa pikselissä olevien kohteiden – mallimuuttujien – spektraalisista ominaisuuksista. Tuotetun lumipeitekartan tarkkuus on suoraan riippuvainen näille muuttujille asetettujen arvojen käyttökelpoisuudesta. Toisaalta saman satelliittipikselin sisällä lumipeitteen ominaisuuksissa voi olla jyrkkiäkin muutoksia, jotka vaikeuttavat satelliittihavaintojen tulkintaa. Epäsuorilla menetelmillä havaittuja lumen estimaatteja voidaan varmentaa hyödyntämällä maanpinnalla kerättyjä maastohavaintoja, mutta myös nämä aineistot sisältävät epätarkkuutta ja virhettä. Tämä väitöskirja analysoi laboratoriossa useista boreaalisista lumityypeistä kerättyjä spektraalisia mittauksia, joiden tarkoitus on tarjota tarkempia lumen heijastusarvoja hyödynnettäväksi menetelmässä, jota käytetään lumipeitteen laajuuden kartoituksessa. Boreaalisella metsävyöhykkeellä olevia spektraalisia mallimuuttujia, joita tyypillisesti käytetään optisissa lumen kartoitusmenetelmissä, kuvataan väitöstyössä usean eri mittakaavan havainnoilla. Lisäksi mittavaa lumensyvyyden, lumen vesiarvon sekä lumen tiheyden maastomittausaineistoa hyödynnetään kaukokartoitushavaintojen tulkinnan tukemiseksi boreaalisella vyöhykkeellä sekä tundralla. Aineiston avulla kuvataan lumen ominaisuuksien alueellista ja ajallista vaihtelua sekä tutkitaan pistemäisesti kerättyjen maastohavaintojen epätarkkuutta sekä edustavuutta, kun niitä käytetään kaukokartoitushavaintojen validoinnissa. Väitöstyön yleisenä tarkoituksena on edistää tietoutta boreaalisen vyöhykkeen spektraalisista mallimuuttujista, jotta optisella kaukokartoituksella tuotettujen lumipeitehavaintojen tarkkuus paranee ja tukea kaukokartoitushavaintojen parempaa tulkintaa ja validointia epähomogeenisissa satelliittipikseleissä. Väitöstyön pääasiallinen viesti on, että laboratorio-olosuhteissa kerätyillä mittauksilla voidaan tuottaa tarkempia arvoja lumipeitteen kaukokartoitushavaintojen tulkinta-algoritmeille, koska maastomittauksissa läsnä olevia häiritseviä tekijöitä voidaan sulkea pois. Lumipeitteen kaukokartoituksessa hyödynnettävien sensorien hieman toisistaan poikkeavat optiset kaistat eivät näytä merkittävästi vaikuttavan lumen heijastusarvoon. Tämä tukee historiallisten aineistojen rakentamista eri sensoreilla kerätyistä havainnoista. Tulokset myös paljastavat, että lumen heijastusarvoissa on suurta hajontaa, joka liittyy lumen makro- ja mikrostruktruuristen ominaisuuksien vaihteluun. Lisäksi tulokset osoittivat, että maastomittauksista saadut alueelliset lumensyvyyden keskiarvot, joita usein käytetään karkeamman resoluution kaukokartoitushavaintojen validoinnissa, ovat riippuvaisia mittausten välisestä etäisyydestä sekä mittausten lukumäärästä. Näin on erityisesti maanpeiteluokissa, joilla lumensyvyyden vaihtelu on erityisen suurta, kuten paljakat tundralla. Soveltamalla samaa mittausprotokollaa boreaalisiin ja tundran maanpeiteluokkiin, jotka edustavat hyvin erilaisia lumiolosuhteita, saadaan keskenään eriävästi edustavia alueellisia keskiarvoja. Tässä työssä kerätyt laajamittaiset havaintoaineistot osoittavat, että eri mittakaavoilla kerätyt havainnot voivat tarjota eri näkökulman samaan ongelmaan, mutta samaan aikaan yksittäinen havaintoaineisto on riittämätön tarjotakseen täyden ymmärryksen tiettyyn haasteeseen, kuten epähomogeenisen satelliittipikselin tulkintaan. Tämä väitöstyö ja siinä kerätyt aineistot hyödyttävät suoraan tutkimusta, joka koskee lumipeitteen laajuuden optisen kaukokartoituksen epätarkkuuksia sekä satelliittihavaintojen tulkintaa boreaalisella metsävyöhykkeellä sekä tundralla