Snow cover estimation is a principal source of error for spring streamflow simulations in Québec, Canada. Optical and near infrared remote sensing can improve snow cover area (SCA) estimation due to high spatial resolution but is limited by cloud cover and incoming solar radiation. Passive microwave remote sensing is complementary by its near-transparence to cloud cover and independence to incoming solar radiation, but is limited by its coarse spatial resolution. The study aims to create an improved SCA product from blended passive microwave (AMSR-E daily L3 Brightness Temperature) and optical (MODIS Terra and Aqua daily snow cover L3) remote sensing data in order to improve estimation of river streamflow caused by snowmelt with Québec's operational MOHYSE hydrological model through direct-insertion of the blended SCA product in a coupled snowmelt module (SPH-AV). SCA estimated from AMSR-E data is first compared with SCA estimated with MODIS, as well as with in situ snow depth measurements. Results show good agreement (+95%) between AMSR-E-derived and MODIS-derived SCA products in spring but comparisons with Environment Canada ground stations and SCA derived from Advanced Very High Resolution Radiometer (AVHRR) data show lesser agreements (83 % and 74% respectively). Results also show that AMSR-E generally underestimates SCA. Assimilating the blended snow product in SPH-AV coupled with MOHYSE yields significant improvement of simulated streamflow for the aux Écorces et au Saumon rivers overall when compared with simulations with no update during thaw events. These improvements are similar to results driven by biweekly ground data. Assimilation of remotely-sensed passive microwave data was also found to have little positive impact on springflood forecast due to the difficulty in differentiating melting snow from snow-free surfaces. Considering the direct-insertion and Newtonian nudging assimilation methods, the study also shows the latter method to be superior to the former, notably when assimilating noisy data.

Cette thèse est consacrée à la détermination de l’émissivité des surfaces terrestres (LSE) à partir de données dans les domaines des micro-ondes et de l’infrarouge thermique. (1) Ce travail a permis de fournir une méthode de détermination du LSE à partir des données AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) et de développer un modèle de transfert radiatif sol-atmosphère (SARTM) utilisé pour simuler les températures de brillance au niveau du capteur. Le modèle SARTM est construit à partir du modèle MonoRTM (MONOchromatic Radiative Transfer Model) et du modèle AIEM (Advanced Integral Equation Model). Dans cette étude les émissivités micro-ondes sur toute la Chine pour l’année 2006 ont été estimées. (2) Cette thèse présente également les améliorations apportées à un algorithme de détermination des émissivités à partir du capteur SEVIRI (Spinning Enhanced Visible and Infrared Imager) à bord du satellite MSG-2. Cet algorithme perfectionné est appliqué à plusieurs images MSG-2/SEVIRI sur une région d’étude de la péninsule ibérique. Il est démontré sur des cas détaillés que les améliorations portées sur la méthode originale de détermination du LSE et de la température de surface étaient réelles et cohérentes. / This thesis focused on the retrievals of Land Surface Emissivity (LSE) from microwave data and thermal infrared data. (1) This thesis provides a method to retrieval LSE from the AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) and develops a Soil-Atmosphere Radiative Transfer Model (SARTM) to simulated brightness temperatures at satellite level. SARTM model is built from MonoRTM (MONOchromaticRadiative Transfer Model) and from AIEM (Advanced Integral Equation Model) models. In this study, the LSEs over whole China of year 2006 are estimated. (2) This thesis also presents an improved algorithm to retrieve LSE from SEVIRI (Spinning Enhanced Visible and Infrared Imager) data onboardthe MSG-2 satellite. Finally, this improved algorithm is applied to several MSG-2/SEVIRI datasets over a study area withinthe Iberian Peninsula region. It is demonstrated with some detailed cases that these improvements on the original LSE/land surface temperature (LST) retrieval methods are effective and reasonable.

Résumé: La mesure de l’équivalent en eau de la neige (EEN) sur le terrain permet de prédire la quantité d’eau libérée par la fonte de la neige. La télédétection dans les micro-ondes passives offre le potentiel d’estimer I’EEN et peut complémenter ces observations de façon synoptique pour l’ensemble du territoire. Un produit de cartographie de I’EEN couvrant l’ensemble du globe a été élaboré par le NSIDC basé sur le capteur AMSR-E. Cet instrument, lancé en 2002, a une résolution améliorée par rapport aux capteurs antérieurs. L’estimation de I’EEN se base sur la différence entre un canal peu affecté (19 GHz) et un canal affecté (37 GHz) par la diffusion de volume de la neige. La précision de ce produit a été évaluée pour la province de Québec à l’hiver 2003 et à l’hiver 2004 qui ont un EEN moyen de 170 mm. Des sous-estimations importantes ont été révélées et une certaine difficulté à détecter la présence de neige. Des modèles régionaux de régressions linéaires ont été développés pour le Québec. Des corrections pour la fraction d’eau et de forêt ont été appliquées à la combinaison T19v.37v et ont permis d’améliorer les résultats. Ces corrections sont basées sur la température de l’air du modèle GEM. Les meilleurs résultats sont pour la classe de neige taïga à l’hiver 2003 avec une erreur relative de 24 % tandis que l’erreur relative est d’environ 40 % pour la région maritime. Les erreurs élevées dans la classe taïga ont été attribuées à des couverts de neige plus épais que la capacité de pénétration des micro-ondes tandis que les erreurs de la classe maritime a des fractions forêt élevées et à la neige mouillée. La présence d’importante quantité de neige et la forêt dense de la province de Québec compliquent l’estimation de I’EEN au Québec avec un modèle de régression. || Abstract: Snow water equivalent (SWE) measurements in the field allow estimation of the quantity of released water from the melting of snow. This is useful to predict the water reserve available for production of hydro-electricity. Remote sensing with microwave can estimate SWE and complement those observations synoptically for whole territories. A SWE mapping products was developed by NSIDC based on the AMSR-E sensor launched in 2002 with an improved resolution compared to previous sensors. SWE estimation is based on difference between a channel weakly affected (19 GHz) and a channel strongly affected by volume scattering. The precision of this product was evaluated for the province of Quebec in winter 2003 and winter 2004 with a mean SWE of 170 mm. Important underestimation and some difficulty of detecting the snow was revealed. Regional linear regression models were developed for the province of Quebec. Corrections for forest and water fraction were applied on T19V-37V combination and permit to improve the results. Those corrections were based on air temperature from the GEM model. Best results were found for taiga snow class in winter 2003 with a relative error of 28% and approximately 40% for maritime snow class. High errors in the taiga region were attributed to snow depth higher than the penetration depth of the microwave and errors in the maritime region to high forest density and wet snow. The important snow amount and high density forest of the province of Quebec hampers the estimation of SWE with a regression model.

Soil freezing and thawing processes are of particular importance for agricultural areas. For example, frozen soils can increase the runoff during snowmelt in the spring. Freezing and thawing also have a direct influence on the sowing and harvesting dates, as well as on the crop yield. A better understanding of those phenomena is therefore important, and several researchers focused on this topic in the past. Due to its sensitivity to changes in the state of water, microwave remote sensing is an appropriate tool for that purpose. The main objective of this study is to monitor soil freezing and thawing processes using AMSR-E and RADARSAT-2 polarimetric data acquired over an agricultural area located near Saskatoon (Saskatchewan). With AMSR-E data, the goals are to compare different combinations of frequencies for the spectral gradient's algorithm regarding their capacity for detecting frozen soils, and to analyze the temporal dynamics of the brightness temperature in order to find a new indicator of soil freezing. As for RADARSAT-2 data, several polarimetric parameters and techniques are tested in order to identify soil freezing. For the first part concerning AMSR-E data, a global precision for the discrimination of frozen and thawed soils higher than 90% was obtained with the spectral gradient's algorithm, for the combinations including high (18.7 and 36.5 GHz) and low (6.9 and 10.7 GHz) frequencies as well as for the one using only high frequencies. It is shown that, for the combination based on the 18.7 and 36.5 GHz frequencies, results are improved when a negative threshold is used for the spectral gradient. When high and low AMSR-E frequencies are combined, a null threshold is on the contrary appropriate, which constitutes an operational advantage. A new algorithm for detecting frozen soils, based on a thresholding approach applied to the spectral gradient of polarization difference and the brightness temperature at 36.5 GHz, was also proposed. The performances of the new algorithm to discriminate frozen and thawed soils are very similar to those obtained using the spectral gradient of brightness temperature (global precision around 90% and probability of detecting frozen soils between 70% and 85%). The performances are also slightly higher for the combinations including the lower AMSR-E frequencies. However, annual statistics for the spectral gradient of polarization difference are required to calculate the thresholds. The results obtained with AMSR-E data highlight the relevance of including SMOS L-band brightness temperatures for the calculation of brightness temperature and polarization difference spectral gradients. The qualitative analysis of the results obtained using RADARSAT-2 data shows that surface scattering dominates volume scattering for frozen soils, which can be explained by the rough fields in the study area, as compared to the signal's wavelength (C-band). Nevertheless, several polarimetric parameters indicate a slight increase of the volume scattering in frozen soils, which is theoretically expected. This was observed for the linear and circular depolarization ratios, the amplitude of the HHVV, RLLL and RLRR correlation coefficients, as well as for the pedestal height. Also, the entropy and [alpha overline]-angle of the Cloude-Pottier target decomposition increase slightly in frozen soils ; the same is true for the volume scattering component of the Freeman-Durden and Yamaguchi target decompositions, with an equivalent decrease of the surface scattering component. Despite these interesting observations, a quantitative analysis of the results is necessary in order to evaluate the usefulness of polarimetry regarding the detection of frozen soils. This would allow the validation of the behavior, possibly caused by soil freezing, of the mean value and the standard deviation of the HHVV phase difference and the standard deviation of the RLLL and RLRR phase differences.

The assimilation process to produce daily Snow Water Equivalent (SWE) maps is modified by using Helsinki University of Technology (HUT) snow emission model and AMSR-E passive microwave data. The characteristics of HUT emission model is analyzed in-depth and discussed with respects to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed for snow over mountainous areas. Performance of the modified model is checked against original and other modified cases against ground truth data covering 2003-2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation is successfully carried out by means of snow data measured at ground stations during 2008-2010 winter periods. Validation results were less satisfactory for SWE smaller than 75.0 mm and greater than 200.0 mm. Overestimation is especially observed for stations located below 1750.0 m elevation where SWE is less than 75.0 mm. Applied methodology is fine tuned to improve its performance for shallow snow depths observed below 1750 m elevation using a relationship that integrates 10.7 GHz channel data. But an underestimation for SWE greater than 150 mm could not beresolved due to microwave signal saturation that is expected in dense snowpack.

Satellite observations provide information on land surface processes over a large spatial extent with a frequency dependent on the satellite revisit time. These observations are not subject to the spatial limitations of the traditional point measurements and are usually collected in a global scale. With a reasonable spatial resolution and temporal frequency, the Moderate Resolution Imaging Spectroradiometer (MODIS) is one of these satellite sensors which enables the study of land-atmospheric interactions and estimation of climate variables for over a decade from remotely sensed data. This research investigated the potential of remotely sensed land surface temperature (LST) data from MODIS for air temperature (Ta) and soil moisture (SM) estimation in New Zealand and how the satellite derived parameters relate to the numerical model simulations and the in-situ ground measurements. Additionally, passive microwave SM product from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) was applied in this research. As the first step, the MODIS LST product was validated using ground measurements at two test-sites as reference. Quality of the MODIS LST product was compared with the numerical simulations from the Weather Research and Forecasting (WRF) model. Results from the first validation site, which was located in the alpine areas of the South Island, showed that the MODIS LST has less agreement with the in-situ measurements than the WRF model simulations. It turned out that the MODIS LST is subject to sources of error, such as the effects of topography and variability in atmospheric effects over alpine areas and needs a careful pre-processing for cloud effects and outliers. On the other hand, results from the second validation site, which was located on the flat lands of the Canterbury Plains, showed significantly higher agreement with the ground truth data. Therefore, ground measurements at this site were used as the main reference data for the accuracy assessment of Ta and SM estimates. Using the MODIS LST product, Ta was estimated over a period of 10 years at several sites across New Zealand. The main question in this part of the thesis was whether to use LST series from a single MODIS pixel or the series of a spatially averaged value from multiple pixels for Ta estimation. It was found that the LST series from a single pixel can be used to model Ta with an accuracy of about ±1 ºC. The modelled Ta in this way showed r ≈ 0.80 correlation with the in-situ measurements. The Ta estimation accuracy improved to about ±0.5 ºC and the correlation to r ≈ 0.85 when LST series from spatially averaged values over a window of 9x9 to 25x25 pixels were applied. It was discussed that these improvements are due to noise reduction in the spatially averaged LST series. By comparison of LST diurnal trends from MODIS with Ta diurnal trends from hourly measurements in a weather station, it was shown that the MODIS LST has a better agreement with Ta measurements at certain times of the day with changes over day and night. After estimation of Ta, the MODIS LST was applied to derive the near-surface SM using two Apparent Thermal Inertia (ATI) functions. The objective was to find out if more daily LST observations can provide a better SM derivation. It was also aimed to identify the potential of a land-atmospheric coupled model for filling the gaps in derived SM, which were due to cloud cover. The in-situ SM measurements and rainfall data from six stations were used for validation of SM derived from the two ATI functions and simulated by the WRF model. It was shown that the ATI function based on four LST observations has a better ability to derive SM temporal profiles and is better able to detect rainfall effects. Finally, the MODIS LST was applied for spatial and temporal adjustment of the near-surface SM product from AMSR-E passive microwave observations over the South Island of New Zealand. It was shown that the adjustment technique improves AMSR-E seasonal trends and leads to a better matching with rainfall events. Additionally, a clear seasonal variability was observed in the adjusted AMSR-E SM in the spatial domain. Findings of this thesis showed that the satellite observed LST has the potential for the estimation of the land surface variables, such as the near-surface Ta and SM. This potential is greatly important on remote and alpine areas where regular measurements from weather stations are not often available. According to the results from the first validation site, however, the MODIS LST needs a careful pre-processing on those areas. The concluding chapter included a discussion of the limitations of remotely sensed data due to cloud cover, dense vegetation and rugged topography. It was concluded that the satellite observed LST has the potential for SM and Ta estimations in New Zealand. It was also found that a land-atmospheric model (such as the WRF coupled with the Noah and surface model) can be applied for filling the gaps due to cloud cover in remotely sensed variables.

The soil temperature is an essential parameter for the energy balance of the earth. Many methods have been developed to determine summer surface temperature, but the determination in the presence of snow is an ill-conditioned problem since it requires the differentiation of several temperatures (surface of snow, temperature gradient within the snowpack and temperature at the snow/soil interface). Our project was motivated by the need to improve the estimation of soil temperature, within the first centimeters of soil, under the snowpack.The passive microwave remote sensing could provide this information. We showed the potential of the passive microwave brightness temperature inversion at 10 GHz (derived from AMSR-E, version V5) for the estimation of the soil temperature by using a physical multilayer snow model (SNTHERM) coupled with a snow microwave emission model (HUT).The snow model is driven with measurements from meteorological stations (air temperature, precipitation, air relative humidity, wind speed) and data generated by the NARR meteorological reanalysis.The coupled model is validated with in-situ measurements and the retrieved soil temperatures are compared to those derived from the snow model and NARR.The overall root mean square error in the soil temperature retrieval is 3.29 K, which is lower than the error derived from models without the use of remote sensing. This validation must consider the fact that we are comparing temperatures from a point station to that corresponding to an area of 25 x 25 km on the satellite scale. We also show the possibility of mapping the soil temperature. This original procedure constitutes a very promising tool to characterize the soil under snow (frozen or not), as well as its evolution in locations where measurements are unavailable

The SeaWinds scatterometers aboard the QuikSCAT and ADEOS II satellites were originally designed to measure wind vectors over the ocean by exploiting the relationship between wind-induced surface roughening and the normalized radar backscatter cross-section. Recently, an algorithm for simultaneously retrieving wind and rain (SWR) from scatterometer measurements was developed that enables SeaWinds to correct rain-corrupted wind measurements and retrieve rain rate data. This algorithm is based on co-locating Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and SeaWinds on QuikSCAT data. In this thesis, a new wind and rain radar backscatter model is developed for the SWR algorithm using a global co-located data set with rain data from the Advanced Microwave Scanning Radiometer (AMSR) and backscatter data from the SeaWinds scatterometer aboard the Advanced Earth Observing Satellite 2 (ADEOS II). The model includes the effects of phenomena such as backscatter due to wind stress, atmospheric rain attenuation, and effective rain backscatter. Rain effect parameters of the model vary with integrated rain rate, which is defined as the product of rain height and rain rate. This study accounts for rain height in the model in order to calculate surface rain rate from the integrated rain rate. A simple model for the mean rain height versus latitude and longitude is proposed based on AMSR data and methods of incorporating this model into the SWR retrieval process are developed. The performance of the new SWR algorithm is measured by comparison of wind vectors and rain rates to the previous SWR algorithm, AMSR rain rates, and NCEP numerical weather prediction winds. The new SWR algorithm produces accurate rain estimates and detects rain with a low false alarm rate. The wind correction capabilities of the SWR algorithm are effective at correcting rain-induced inaccuracies. A qualitative comparison of the wind and rain retrieval for Hurricane Isabel demonstrates these capabilities.

AMSR-E is a space-borne radiometer which measures Earth microwave emissions or brightness temperatures (Tb) over a wide swath. AMSR-E data and images are useful in mapping valuable Earth-surface and atmospheric phenomena. A modified version of the Scatterometer Image Reconstruction (SIR) algorithm creates Tb images from the collected data. SIR is an iterative algorithm with tuning parameters to optimize the reconstruction for the instrument and channel. It requires an approximate aperture function for each channel to be effective. This thesis presents a simulator-based optimization of SIR iteration and aperture function threshold parameters for each AMSR-E channel. A comparison of actual Tb images generated using the optimal and sub-optimal values is included. Tuned parameters produce images with sharper transitions between regions of low and high Tb for lower-frequency channels. For higher-frequency channels, the severity of artifacts due to temporal Tb variation of the input measurements decreases and coverage gaps are eliminated after tuning. A two-parameter Gaussian-like bell model is currently assumed in image reconstruction to approximate the AMSR-E aperture function. This paper presents a method of estimating the effective AMSR-E aperture function using Tb measurements and geographical information. The estimate is used as an input for image reconstruction. The resulting Tb images are compared with those produced with the previous Gaussian approximation. Results support the estimates found in this paper for channels 1h, 1v, and 2h. Images processed using the old or new aperture functions for all channels differed by a fraction of a Kelvin over spatially smooth regions.

The removal of systematic brightness temperature (Tb) biases is necessary when producing decadal passive microwave data sets for weather and climate research. It is crucial to achieve Tb measurement consistency among all satellites in a constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution; however, since radiometers operate at different frequencies and viewing angles, Tb normalizations are made before making intermediate comparisons of their near-simultaneous measurements. In this dissertation, a new approach is investigated to perform these normalizations from one satellite's measurements to another. It uses Taylor's series expansion around a source frequency to predict Tb of a desired frequency. The relationship between Tb's and frequencies are derived from simulations using an oceanic Radiative Transfer Model (RTM) over a wide variety of environmental conditions. The original RTM is built on oceanic radiative transfer theory. Refinements are made to the model by modifying and tuning algorithms for calculating sea surface emission, atmospheric emission and attenuations. Validations were performed with collocated WindSat measurements. This radiometric calibration approach is applied to establish an absolute brightness temperature reference using near-simultaneous pair-wise comparisons between a non-sun synchronous radiometer and two sun-synchronous polar-orbiting radiometers: the Tropical Rain Measurement Mission (TRMM) Microwave Imager (TMI), WindSat (on Coriolis) and Advanced Microwave Scanning Radiometer (AMSR) on Advanced Earth Observing System -II (ADEOSII), respectively. Collocated measurements between WindSat and TMI as well as between AMSR and TMI, within selected 10 weeks in 2003 for each pair, are collected, filtered and applied in the cross calibration. AMSR is calibrated to WindSat using TMI as a transfer standard. Accuracy prediction and error source analysis are discussed along with calibration results. This inter-satellite radiometric calibration approach provides technical support for NASA's Global Precipitation Mission which relies on a constellation of cooperative satellites with a variety of microwave radiometers to make global rainfall measurements.