Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modeling

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size and temperature) were used as inputs to the multi-layer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (-0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical SSA to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%

Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

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

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

Theoretical Modeling and Analysis of L- and P-band Radar Backscatter Sensitivity to Soil Active Layer Dielectric Variations

Freeze-thaw (FT) and moisture dynamics within the soil active layer are critical elements of boreal, arctic and alpine ecosystems, and environmental change assessments. We evaluated the potential for detecting dielectric changes within different soil layers using combined L- and P-band radar remote sensing as a prerequisite for detecting FT and moisture profile changes within the soil active layer. A two-layer scattering model was developed and validated for simulating radar responses from vertically inhomogeneous soil. The model simulations indicated that inhomogeneity in the soil dielectric profile contributes to both L- and P-band backscatter, but with greater P-band sensitivity at depth. The difference in L- and P-band responses to soil dielectric profile inhomogeneity appears suitable for detecting associated changes in soil active layer conditions. Additional evaluation using collocated airborne radar (AIRSAR) observations and in situ soil moisture measurements over alpine tundra indicates that combined L- and P-band SAR observations are sensitive to soil dielectric profile heterogeneity associated with variations in soil moisture and FT conditions