Yes / Dust storms are one of the natural phenomena, which have increased in frequency in recent years in North Africa, Australia and northern China. Satellite remote sensing is the common method for monitoring dust storms but its use for identifying dust storms over sandy ground is still limited as the two share similar characteristics. In this study, an artificial neural network (ANN) is used to detect dust storm using 46 sets of data acquired between 2001 and 2010 over North Africa by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites. The ANN uses image data generated from Brightness Temperature Difference (BTD) between bands 23 and 31 and BTD between bands 31 and 32 with three bands 1, 3, and 4, to classify individual pixels on the basis of their multiple-band values. In comparison with the manually detection of dust storms, the ANN approach gave better result than the Thermal Infrared Integrated Dust Index approach for dust storms detection over the Sahara. The trained ANN using data from the Sahara desert gave an accuracy of 0.88 when tested on data from the Gobi desert and managed to detect 90 out of the 96 dust storm events captured worldwide by Terra and Aqua satellites in 2011 that were classified as dusty images on NASA Earth Observatory. / IEEE Geoscience and Remote Sensing Society

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database.

Time series of brightness temperature (TB) measurement obtained at various frequencies by the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) are investigated to determine ice phenology parameters and ice thickness on Great Bear Lake (GBL) and Great Slave Lake (GSL), Northwest Territories, Canada. TB measurements from the 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz channels (H- and V- polarization) are compared to assess their potential for detecting freeze-onset (FO)/melt-onset (MO), ice-on/ice-off dates, and ice thickness on both lakes. The sensitivity of TB measurements at 6.9, 10.7, and 18.7 GHz to ice thickness is also examined using a previously validated thermodynamic lake ice model and the most recent version of the Helsinki University of Technology (HUT) model, which accounts for the presence of a lake-ice layer under snow. This study shows that 18.7 GHz H-pol is the most suitable AMSR-E channel for detecting ice phenology events, while 18.7 GHz V-pol is preferred for estimating lake ice thickness on the two large northern lakes. These two channels therefore form the basis of new ice cover retrieval algorithms. The algorithms were applied to map monthly ice thickness products and all ice phenology parameters on GBL and GSL over seven ice seasons (2002-2009). Through application of the algorithms much was learned about the spatio-temporal dynamics of ice formation, decay and growth rate/thickness on the two lakes. Key results reveal that: 1) both FO and ice-on dates occur on average 10 days earlier on GBL than on GSL; 2) the freeze-up process or freeze duration (FO to ice-on) takes a comparable amount of time on both lakes (two to three weeks); 3) MO and ice-off dates occur on average one week and approximately four weeks later, respectively, on GBL; 4) the break-up process or melt duration (MO to ice-off) lasts for an equivalent period of time on both lakes (six to eight weeks); 5) ice cover duration is about three to four weeks longer on GBL compared to its more southern counterpart (GSL); and 6) end-of-winter ice thickness (April) on GBL tends to be on average 5-15 cm thicker than on GSL, but with both spatial variations across lakes and differences between years.

Time series of brightness temperature (TB) measurement obtained at various frequencies by the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) are investigated to determine ice phenology parameters and ice thickness on Great Bear Lake (GBL) and Great Slave Lake (GSL), Northwest Territories, Canada. TB measurements from the 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz channels (H- and V- polarization) are compared to assess their potential for detecting freeze-onset (FO)/melt-onset (MO), ice-on/ice-off dates, and ice thickness on both lakes. The sensitivity of TB measurements at 6.9, 10.7, and 18.7 GHz to ice thickness is also examined using a previously validated thermodynamic lake ice model and the most recent version of the Helsinki University of Technology (HUT) model, which accounts for the presence of a lake-ice layer under snow. This study shows that 18.7 GHz H-pol is the most suitable AMSR-E channel for detecting ice phenology events, while 18.7 GHz V-pol is preferred for estimating lake ice thickness on the two large northern lakes. These two channels therefore form the basis of new ice cover retrieval algorithms. The algorithms were applied to map monthly ice thickness products and all ice phenology parameters on GBL and GSL over seven ice seasons (2002-2009). Through application of the algorithms much was learned about the spatio-temporal dynamics of ice formation, decay and growth rate/thickness on the two lakes. Key results reveal that: 1) both FO and ice-on dates occur on average 10 days earlier on GBL than on GSL; 2) the freeze-up process or freeze duration (FO to ice-on) takes a comparable amount of time on both lakes (two to three weeks); 3) MO and ice-off dates occur on average one week and approximately four weeks later, respectively, on GBL; 4) the break-up process or melt duration (MO to ice-off) lasts for an equivalent period of time on both lakes (six to eight weeks); 5) ice cover duration is about three to four weeks longer on GBL compared to its more southern counterpart (GSL); and 6) end-of-winter ice thickness (April) on GBL tends to be on average 5-15 cm thicker than on GSL, but with both spatial variations across lakes and differences between years.

The NASA SeaWinds scatterometer is a radar remote sensor which operates on two satellites; NASA's QuikSCAT launched in June 1999 and on Japan's ADEOS-II satellite launched in December 2002. The purpose of SeaWinds is to provide global measurements of the ocean surface wind vector. On QuikSCAT, a ground data processing algorithm was developed, which allowed the instrument to function as a QuikSCAT Radiometer (QRad) and measure the ocean microwave emissions (brightness temperature, Tb) simultaneously with the backscattered power. When SeaWinds on ADEOS was launched, this same algorithm was applied, but the results were anomalous. The initial SRad brightness temperatures exhibited significant, unexpected, ascending/descending orbit Tb biases. This thesis presents an empirical correction algorithm to correct the anomalous SeaWinds Radiometer (SRad) ocean brightness temperature measurements. I use the Advanced Microwave Scanning Radiometer (AMSR) as a brightness temperature standard to calibrate and then, with independent measurements, validate the corrected SRad Tb measurements. AMSR is a well-calibrated multi-frequency, dual-polarized microwave radiometer that also operates on ADEOS-II. These results demonstrate that, after tuning the Tb algorithm, good quality SRad brightness temperature measurements are obtained over the oceans.

Radiative Transfer Models (RTM) have many applications in the satellite microwave remote sensing field, such as the retrieval of oceanic and atmospheric environmental parameters, including surface wind vectors and sea surface temperatures, integrated water vapor, cloud liquid, and precipitation. A key component of the ocean RTM is the emissivity model used to determine the brightness temperature (Tb) at the ocean’s surface. A new wideband ocean emissivity RTM developed by the Central Florida Remote Sensing Laboratory (CFRSL) calculates ocean emissivity over a wide range of frequencies, incidence angles, sea surface temperatures (SST), and wind speed. This thesis presents the validation of this CFRSL model using independent WindSat Tb measurements collocated with Global Data Assimilation System (GDAS) Numerical weather model environmental parameters for frequencies between 6.8 to 37 GHz and wind speeds between 0 – 20 m/s over the July 2005 – June 2006 year. In addition, the CFRSL emissivity model is validated using WindSat derived ocean wind speeds and SST that are contained in the Environmental Data Record (EDR) and combined with the GDAS environmental parameters. Finally, the validation includes comparisons to the well-established XCAL ocean emissivity RTM. The focus of this validation and comparison is to assess performance of the emissivity model results with respect to a wide range of frequency and wind speeds but limited to a narrow range of incidence angles between approximately 50° - 55°

Résumé : La région de l'archipel canadien, située en Arctique, connaît actuellement d'importants changements climatiques, se traduisant notamment par une augmentation des températures, une réduction de l'étendue de la banquise marine et du couvert nival terrestre ou encore une perte de masse significative des calottes glaciaires disséminées sur les îles de l'archipel. Parmi ces calottes glaciaires, la calotte Barnes, située en Terre de Baffin, ne fait pas exception comme le montrent les observations satellitaires qui témoignent d'une importante perte de masse ainsi que d'une régression de ses marges, sur les dernières décennies. Bien que les calottes glaciaires de l'archipel canadien ne représentent que quelques dizaines de centimètres d'élévation potentielle du niveau des mers, leur perte de masse est une composante non négligeable de l'augmentation actuelle du niveau des mers. Les projections climatiques laissent à penser que cette contribution pourrait rester significative dans les décennies à venir. Cependant, afin d'estimer les évolutions futures de ces calottes glaciaires et leur impact sur le climat ou le niveau des mers, il est nécessaire de caractériser les processus physiques tels que les modifications du bilan de masse de surface. Cette connaissance est actuellement très limitée du fait notamment du sous-échantillonnage des régions arctiques en terme de stations météorologiques permanentes. Une autre particularité de certaines calottes de l'archipel canadien, et de la calotte Barnes en particulier, est de présenter un processus d'accumulation de type glace surimposée, ce phénomène étant à prendre en compte dans l'étude des processus de surface. Pour pallier au manque de données, l'approche retenue a été d'utiliser des données de télédétection, qui offrent l'avantage d'une couverture spatiale globale ainsi qu'une bonne répétitivité temporelle. En particulier les données acquises dans le domaine des micro-ondes passives sont d'un grand intérêt pour l'étude de surfaces enneigées. En complément de ces données, la modélisation du manteau neigeux, tant d'un point de vue des processus physiques que de l'émission électromagnétique permet d'avoir accès à une compréhension fine des processus de surface tels que l'accumulation de la neige, la fonte, les transferts d'énergie et de matière à la surface, etc. Ces différents termes sont regroupés sous la notion de bilan de masse de surface. L'ensemble du travail présenté dans ce manuscrit a donc consisté à développer des outils permettant d'améliorer la connaissance des processus de surface des calottes glaciaires du type de celles que l'on rencontre dans l'archipel canadien, l'ensemble du développement méthodologique ayant été réalisé sur la calotte Barnes à l'aide du schéma de surface SURFEX-CROCUS pour la modélisation physique et du modèle DMRT-ML pour la partie électromagnétique. Les résultats ont tout d'abord permis de mettre en évidence une augmentation significative de la durée de fonte de surface sur la calotte Barnes (augmentation de plus de 30% sur la période 1979-2010), mais aussi sur la calotte Penny, elle aussi située en Terre de Baffin et qui présente la même tendance (augmentation de l'ordre de 50% sur la même période). Ensuite, l'application d'une chaîne de modélisation physique contrainte par diverses données de télédétection a permis de modéliser de manière réaliste le bilan de masse de surface de la dernière décennie, qui est de +6,8 cm/an en moyenne sur la zone sommitale de la calotte, qui est une zone d'accumulation. Enfin, des tests de sensibilité climatique sur ce bilan de masse ont permis de mettre en évidence un seuil à partir duquel cette calotte voit disparaître sa zone d'accumulation. Les modélisations effectuées suggèrent que ce seuil a de fortes chances d'être atteint très prochainement, pour une augmentation de température moyenne inférieure à 1°C, ce qui aurait pour conséquence une accélération de la perte de masse de la calotte. // Abstract : Significant climate change is curently monitored in the Arctic, and especially in the region of the canadian arctic archipellago. This climate warming leads to recession of seaice extent and seasonnal snow cover, and also to large mass loss of the archipellago’s ice caps. One of the most southern ice cap, the Barnes Ice Cap, located on the Baffin Island, is no exception to significant mass loss and margins recession as satellite observations exhibited over the last decades. Despite the relative low sea level potential of the small ice caps located in the canadian arctic achipellago in regards to major ice sheets, Antarctica and Greenland, their contribution to the current sea level rise is significant. Climate projections show that this contribution could accelerate significant over the next decades. However, to estimate the future evolution of these ice caps and their impact on climate or sea level rise, a better characterisation of the surface processes such as the evolution of the surface mass balance is needed. This knowledge is currently very limited, mainly due to the sparse covering of automatic weather stations or in-situ measurements over the Arctic. Furthermore, several ice caps, among with the Barnes Ice Cap, present a superimposed ice accumulation area which particularities have to be taken into account in the surface processes studies. Given the lack of in-situ data, the approach choosen in this work is to use remote sensing data, that have the advantage to offer a good spatial and temporal coverage. In particular, passive microwave data are very suitable for snowy surfaces studies. To complement these data, physical and electromagnetic snowpack modeling provide a fine characterisation of surface processes such as snow accumulation. The whole work presented in this manuscript thus consisted in developping specific tools to improve the understanding of surface processes of small arctic ice caps. This methodological development was performed and applied on the Barnes Ice Cap using the surface scheme SURFEX-CROCUS and the electromagnetic model DMRT-ML. First results highlight a significant increase in surface melt duration over the past 3 decades on the Barnes Ice Cap (increase of more than 30% over 1979-2010 period). A similar trend is also monitored over the Penny Ice Cap, located in the south part of the Baffin Island (increase of more than 50% over the same period). Then, the surface mass balance over the last decade was modeled by using a physical based modeling chain constrained by remote sensing data. The results give a mean net accumulation of +6,8 cm y−1 on the summit area of the ice cap. Finaly, sensitivity tests, performed to investigate the climatic sensitivity of the surface mass balance, highlight a threshold effect that may lead to a complete disapearence of the accumulation area of the Barnes Ice Cap. With a temperature increase less than 1°C, modeling results suggest it is likely that the threshold will be reached rapidly leading to an increase in mass loss from the ice cap.

Цель работы: разработка информационной системы для моделирования особенностей распределения радиояркостной температуры головного мозга. Актуальность работы основывается на широком развитии рассматриваемых компонент магистерской. В настоящее время устройства измерения температуры (ИК и контактные методы) в общем случае могут измерять температуру поверхности тела, поэтому важна разработка методов моделирования для неинвазивных измерений температуры внутренних органов, особенно такого сложного объекта как головной мозг. Результаты работы: для оценки изменений распределения радиояркостной температуры разработана информационная система основанная на численном решений уравнений Пеннеса В процессе работы проведено моделирование при разных конфигурациях с помощью алгоритмов, позволяющих последовательно запускать расчет моделей при разных проекциях антенн-аппликаторов на поверхности модели головы. / The relevance of the work is based on the broad development of the considered components of the master's degree. At present, temperature measuring devices (IR and contact methods) can generally measure the temperature of the body surface, therefore, it is important to develop modeling methods for non-invasive measurements of the temperature of internal organs, especially of such a complex object as the brain. Results of the work: to assess changes in the distribution of radio brightness temperature, an information system was developed based on numerical solutions of the Pennes equations. In the process of work, modeling was carried out for different configurations using algorithms that allow sequentially launching the calculation of models with different projections of antenna-applicators on the surface of the head model. Modeling was carried out in Python environment using Paraview software. Figures and diagrams were created using Microsoft Office and Diagram.io software.