L'imagerie polarimétrique consiste à acquérir des images contenant des informations relatives à la polarisation de la lumière diffusée par une scène. L'objectif de cette thèse est d'utiliser les propriétés de ce type d'imagerie afin de d'améliorer le contraste entre plusieurs objets d'intérêt.Dans le cadre de l'optimisation du contraste entre deux objets d'intérêt, nous démontrons que, si l'on travaille à temps d'acquisition fixe, c'est l'acquisition d'une unique image avec des états d'illumination et d'analyse optimisés qui permet d'atteindre les meilleures performances. C'est pourquoi nous avons développé un imageur pouvant générer et analyser n'importe quel état de polarisation sur la sphère de Poincaré, en utilisant des cellules à cristaux liquides. Ces états peuvent être contrôlés afin de faire varier le contraste dans les images et nous montrons que les ``états optimaux" permettant de maximiser le contraste dépendent des conditions de mesure. En particulier, la valeur des états de polarisation maximisant le contraste entre deux objets d'intérêt dépend des bruits de mesure (bruit de détecteur, bruit de Poisson, Speckle) ainsi que des fluctuations spatiales des propriétés polarimétriques dans la scène. Une mauvaise estimation de la source de bruit peut donc amener à une perte significative de contraste.Nous nous intéressons ensuite à un scénario d'imagerie plus complexe où la scène peut être illuminée de manière non-uniforme. Nous proposons une méthode d'acquisition utilisant l'ensemble des degrés de liberté fournis par notre imageur et montrons que cette méthode permet d'augmenter significativement le contraste par rapport aux résultats obtenus avec d'autres types d'imagerie comme l'imagerie OSC (Orthogonal State Contrast).Nous étendons ensuite nos études à un cas ``multicibles" où plus de deux objets doivent être distingués. Nous montrons notamment que l'accroissement du nombre d'images peut dégrader le contraste et qu'il existe un nombre optimal d'images à acquérir si l'on travaille à temps d'acquisition fixe.Enfin, nous proposons une méthode visant à automatiser notre imageur pour l'optimisation du contraste en combinant de manière itérative l'acquisition d'images polarimétriques optimisées et un algorithme de segmentation par contours actifs statistiques. Des premiers résultats expérimentaux mettent en évidence l'avantage de cette intégration d'algorithmes de traitement numérique au c\oe ur du processus d'acquisition de l'image. / The polarimetric imaging consists in acquiring images containing information relating to the polarization of the scattered light from a scene. The objective of this thesis is to use the properties of this type of imaging to enhance the contrast between several objects of interest.Considering the optimization of the contrast between two objects of interest, we demonstrate that, if the time for the measurement is fixed, it is the acquisition of a single image with optimized states in illumination and analysis that achieves the best performance. That is why we have developed an imager that can generate and analyze any polarization state on the Poincaré sphere, using liquid crystal cells. These states can be controlled to modify the contrast in the images and we show that the ``optimal states" maximizing the contrast depend on the measurement conditions. Specifically, the value of the polarization states maximizing the contrast between two objects interest depends on the measurement noise (noise detector, Poisson noise, Speckle) and also of spatial fluctuations of polarimetric properties in the scene. Improper estimate of the noise source may therefore lead to a significant loss of contrast.We then consider a more complex imaging scenario where the scene can be illuminated non-uniformly. We propose a method of acquisition using all the degrees of freedom provided by our imaging and show that this method can significantly increase the contrast compared to results obtained with other types of polarimetric imaging such as OSC imaging (Orthogonal State Contrast).We then extend our studies to a ``multi-target case" where more than two objects must be distinguished. In particular, we show that increasing the number of images can degrade the contrast and that there is an optimum number of images to be acquired if one works with a fixed acquisition time.Finally, we propose a method to automate our imaging to optimize contrast by combining iteratively the acquisition of polarimetric images and optimized segmentation algorithm using statistical active contours. The first experimental results demonstrate the advantage of this integration of digital processing algorithms in the core of the image acquisition process.

Este estudo apresentou uma metodologia para inferir hidrometeoros a partir de medidas polarimétricas de um radar meteorológico de dupla polarização banda X. A metodologia consiste em uma abordagem teórica baseada em simulações numéricas com um modelo de espalhamento Mie (T M atrix e M ueller) e uma experimental pautada na aplicação de um algoritmo de classificação de hidrometeoros (Dolan and Rutledge [2009]). As si- mulações teóricas foram utilizadas para estudar os efeitos da distribuição de tamanho de gotas, temperatura dos hidrometeoros, ângulo de elevação e mistura de hidrometeoros a partir do fator de refletividade do radar (Z), refletividade diferencial (Z DR ), fase diferen- cial especifica (K DP ) e coeficiente de correlação( HV ). Os valores de Z DR são 0.5 dBZ maior para a frequência de banda X do que para um banda S. A partir de Z maior que 45 dBZ K DP começa a ficar maior que 0. Já HV começa a diminuir quando Z é maior que 25 dBZ. Não se observa variações significativas para o graupel, porém para granizo K DP é maior que 0 quando Z é maior que 15 dBZ, entretanto, para água, os valores são consideravelmente menores. Os efeitos de temperatura só são notados quando Z é maior que 60 dBZ. Ao analisar o efeito da elevação, observa-se que Z DR diminui com o aumento da elevação, sendo mais sensível para Z maiores, o mesmo efeito é observado para K DP e HV . Estas variações são mais sensíveis para água e granizo do que para o graupel. Comparando as distribuições exponencial e gama para considerar os efeitos da distribuição do tamanho de gotas para o caso da chuva, nota-se que a distribuição exponenciais é maior que a gama quando as gotas aumentam seu tamanho e diminui sua concentração, devido que na simulação teórica foi utilizado N 0 fixo.Ao analisar os efeitos da co-existência de água e graupel, temos que as gotas de água dominam o sinal de Z quando Z for maior que 30 dBZ, já K DP será positivo(negativo) quando Z for maior(menor) que 35 dBZ da água, desde que Z do graupel seja menor que 10 dBZ, já HV tende a ficar próximo de 1 quanto mais graupel é observado. Para a mistura de granizo e água, Z da água domina o do granizo quando Z é maior que 45 dBZ, K DP é maior(menor) que zero quando Z for maior (menor) que 25 dBZ desde que Z do granizo seja menor que 10 dBZ, já Z DR da água(granizo) domina o do granizo(água) quando Z for maior(menor) que 45 dBZ. Na parte experimental, dois casos observados durante o experimento de campo do Projeto CHUVA no Vale do Paraíba em 8 de Fevereiro e 22 de Março de 2012 foram utilizados. A classificação de hidrometeoros segundo Dolan and Rutledge [2009] indicaram a presença de chuva próximo da superfície proveniente de graupel e granizo. Acima dos 5 km foram identificados a presença de graupel,granizo e cristais de gelo. Ao examinar as regiões classificadas como granizo e graupel dentro da região de 0 e 15 C com os resultados teóricos, é possível explicar a presença concomitante de água e granizo e água e graupel nestas regiões. / This study presents a methodology for inferring hydrometeors from polarimetric mea- surements of a X band double polarization meteorological radar. The methodology consists of a theoretical approach based on numerical simulations with a Mie scattering model (T-Matrix and Mueller) and an experimental approach based on the application of a classification algorithm of hydrometeors (Dolan and Rutledge [2009]). The theoretical simulations were used to study the effects of droplet size distribution, hydrometeor tem- perature, elevation angle and mixture of hydrometeors from radar reflectivity factor (Z), differential reflectivity (Z DR ), specific differential phase (K DP ) and correlation coefficient ( HV ). The values of Z DR are 0.5 dBZ higher for the X band frequency than for the S band frequency. From Z greater than 45 dBZ, K DP starts to get higher than 0. When Z is greater than 25 dBZ, HV starts to decrease. No significant variations are observed for the graupel, however for hail, K DP is greater than 0 when Z is greater than 15 dBZ, but these values were much lower than for water. Temperature effects are only noticed when Z is greater than 60 dBZ. When analyzing the effect of elevation, it is observed that Z DR decreases with increasing elevation, being more sensitive to Z larger; the same effect is observed for K DP and HV . These variations are more sensitive to water and hail than to the graupel. Comparing the exponential and gamma distributions to consider the effects of droplet size distribution in the case of rain, it is noted that the exponential distribution is larger than the gamma when the droplets increase in size and decrease in concentration, due to the fact that in the simulation was used N 0 fixed. When analyzing the effects of co-existence of water and graupel, we have that the water droplets dominate the Z signal when Z is greater than 30 dBZ, K DP will be positive (negative) when Z is greater (lower) than 35 dBZ of water, since Z of the graupel is less than 10 dBZ and HV tends to be close to 1 when more graupel is observed. For the mixture of hail and water, Z of water dominates that of hail when Z is greater than 45 dBZ, K DP is larger (smaller) than zero when Z is larger (smaller) than 25 dBZ since Z of hail is less than 10 dBZ and Z DR of water (hail) dominates hail (water) when Z is greater (lower) than 45 dBZ. In the experimental part, two cases observed during the field experiment of the RAIN Project in Vale do Paraíba on February 8 and March 22, 2012 were used. The classification of hydrometeors according to Dolan and Rutledge [2009] indicated the presence of rain near the surface coming of graupel and hail. Above 5 km were identified the presence of graupel, hail and ice crystals. When examining the regions classified as hail and graupel within the region of 0 and 15 C with the theoretical results, it is possible to explain the concomitant presence of water and hail and water and graupel in these regions.

Este estudo apresentou uma metodologia para inferir hidrometeoros a partir de medidas polarimétricas de um radar meteorológico de dupla polarização banda X. A metodologia consiste em uma abordagem teórica baseada em simulações numéricas com um modelo de espalhamento Mie (T M atrix e M ueller) e uma experimental pautada na aplicação de um algoritmo de classificação de hidrometeoros (Dolan and Rutledge [2009]). As si- mulações teóricas foram utilizadas para estudar os efeitos da distribuição de tamanho de gotas, temperatura dos hidrometeoros, ângulo de elevação e mistura de hidrometeoros a partir do fator de refletividade do radar (Z), refletividade diferencial (Z DR ), fase diferen- cial especifica (K DP ) e coeficiente de correlação( HV ). Os valores de Z DR são 0.5 dBZ maior para a frequência de banda X do que para um banda S. A partir de Z maior que 45 dBZ K DP começa a ficar maior que 0. Já HV começa a diminuir quando Z é maior que 25 dBZ. Não se observa variações significativas para o graupel, porém para granizo K DP é maior que 0 quando Z é maior que 15 dBZ, entretanto, para água, os valores são consideravelmente menores. Os efeitos de temperatura só são notados quando Z é maior que 60 dBZ. Ao analisar o efeito da elevação, observa-se que Z DR diminui com o aumento da elevação, sendo mais sensível para Z maiores, o mesmo efeito é observado para K DP e HV . Estas variações são mais sensíveis para água e granizo do que para o graupel. Comparando as distribuições exponencial e gama para considerar os efeitos da distribuição do tamanho de gotas para o caso da chuva, nota-se que a distribuição exponenciais é maior que a gama quando as gotas aumentam seu tamanho e diminui sua concentração, devido que na simulação teórica foi utilizado N 0 fixo.Ao analisar os efeitos da co-existência de água e graupel, temos que as gotas de água dominam o sinal de Z quando Z for maior que 30 dBZ, já K DP será positivo(negativo) quando Z for maior(menor) que 35 dBZ da água, desde que Z do graupel seja menor que 10 dBZ, já HV tende a ficar próximo de 1 quanto mais graupel é observado. Para a mistura de granizo e água, Z da água domina o do granizo quando Z é maior que 45 dBZ, K DP é maior(menor) que zero quando Z for maior (menor) que 25 dBZ desde que Z do granizo seja menor que 10 dBZ, já Z DR da água(granizo) domina o do granizo(água) quando Z for maior(menor) que 45 dBZ. Na parte experimental, dois casos observados durante o experimento de campo do Projeto CHUVA no Vale do Paraíba em 8 de Fevereiro e 22 de Março de 2012 foram utilizados. A classificação de hidrometeoros segundo Dolan and Rutledge [2009] indicaram a presença de chuva próximo da superfície proveniente de graupel e granizo. Acima dos 5 km foram identificados a presença de graupel,granizo e cristais de gelo. Ao examinar as regiões classificadas como granizo e graupel dentro da região de 0 e 15 C com os resultados teóricos, é possível explicar a presença concomitante de água e granizo e água e graupel nestas regiões. / This study presents a methodology for inferring hydrometeors from polarimetric mea- surements of a X band double polarization meteorological radar. The methodology consists of a theoretical approach based on numerical simulations with a Mie scattering model (T-Matrix and Mueller) and an experimental approach based on the application of a classification algorithm of hydrometeors (Dolan and Rutledge [2009]). The theoretical simulations were used to study the effects of droplet size distribution, hydrometeor tem- perature, elevation angle and mixture of hydrometeors from radar reflectivity factor (Z), differential reflectivity (Z DR ), specific differential phase (K DP ) and correlation coefficient ( HV ). The values of Z DR are 0.5 dBZ higher for the X band frequency than for the S band frequency. From Z greater than 45 dBZ, K DP starts to get higher than 0. When Z is greater than 25 dBZ, HV starts to decrease. No significant variations are observed for the graupel, however for hail, K DP is greater than 0 when Z is greater than 15 dBZ, but these values were much lower than for water. Temperature effects are only noticed when Z is greater than 60 dBZ. When analyzing the effect of elevation, it is observed that Z DR decreases with increasing elevation, being more sensitive to Z larger; the same effect is observed for K DP and HV . These variations are more sensitive to water and hail than to the graupel. Comparing the exponential and gamma distributions to consider the effects of droplet size distribution in the case of rain, it is noted that the exponential distribution is larger than the gamma when the droplets increase in size and decrease in concentration, due to the fact that in the simulation was used N 0 fixed. When analyzing the effects of co-existence of water and graupel, we have that the water droplets dominate the Z signal when Z is greater than 30 dBZ, K DP will be positive (negative) when Z is greater (lower) than 35 dBZ of water, since Z of the graupel is less than 10 dBZ and HV tends to be close to 1 when more graupel is observed. For the mixture of hail and water, Z of water dominates that of hail when Z is greater than 45 dBZ, K DP is larger (smaller) than zero when Z is larger (smaller) than 25 dBZ since Z of hail is less than 10 dBZ and Z DR of water (hail) dominates hail (water) when Z is greater (lower) than 45 dBZ. In the experimental part, two cases observed during the field experiment of the RAIN Project in Vale do Paraíba on February 8 and March 22, 2012 were used. The classification of hydrometeors according to Dolan and Rutledge [2009] indicated the presence of rain near the surface coming of graupel and hail. Above 5 km were identified the presence of graupel, hail and ice crystals. When examining the regions classified as hail and graupel within the region of 0 and 15 C with the theoretical results, it is possible to explain the concomitant presence of water and hail and water and graupel in these regions.

Este trabalho compreende a estimativa de taxa de precipitação (R) por meio de variáveis polarimétricas do radar meteorológico MXPOL. As variáveis polarimétricas refletividade efetiva (Z), refletividade diferencial (ZDR) e fase diferencial específica (KDP) e os dados de precipitação de dezenove pluviômetros da rede telemétrica do Alto Tietê foram utilizadas para obter relações entre taxa de precipitação e estas variáveis polarimétricas. A variável KDP foi obtida da fase diferencial e, a atenuação das variáveis refletividade efetiva e diferencial foi corrigida por meio do método de auto consistência. A correção da atenuação é mais significativa para além das células convectivas mais próximas ao MXPOL com Z> 40 dBZ. De fato, Z> 55 dBZ, associado a granizo, pode extinguir o sinal completamente. Cinco relações de transformação (R(Z), R(Z, ZDR), R(ZDR, KDP), R(KDP), R(Z, ZDR, KDP)) foram ajustadas aos dados de precipitação medidos pela rede de pluviômetros. Os resultados sugerem que os ajustes diferem pouco, estatisticamente. O erro relativo para as análises com totais de precipitação horárias oscilou entre 27,5 % e 30,6 %. O coeficiente de variação (R2) das cinco relações variou entre 0,87 e 0,92 ou mais de 85% da variância foi explicada pelos ajustes das cinco relações indicadas. Os erros aumentaram com a diminuição de tempo de acumulação de precipitação. O erro relativo médio variou de 34,4 % para acumulação de 30 minutos a 46,1 % para 10 minutos. Em geral, as relações R(Z) e R(Z, ZDR, KDP) subestimam a taxa de precipitação com aumento da distância ao radar meteorológico MXPOL. O ajuste da relação R(KDP) melhorou com o aumento da taxa de precipitação. Por outro lado, o desempenho da relação R(Z) melhorou para taxas de precipitação R< 5 mmh-1, o que corrobora com resultados de outros estudos na literatura. / This research encloses precipitation rate (R) estimation using polarimetric variables of the MXPOL meteorological radar. The polarimetric variables effective reflectivity (Z), differential reflectivity (Z DR ) and the specific differential phase (K DP ) and the precipitation data of nineteen rain gauges of the Alto Tietê telemetric network were used to obtain relations between precipitation rate and these polarimetric variables. The K DP was obtained from the differential phase and, the effective reflectivity and differential reflectivity attenuation was corrected, by the self-consistency method. The attenuation correction is more significant beyond of the convective cells closest to MXPOL with Z &gt; 40 dBZ. Indeed, Z &gt; 55 dBZ, associated with hail, can completely extinguish the signal. Five transformational relations (R(Z), R(Z, Z DR ), R(Z DR , K DP ), R(K DP ), R(Z, Z DR , K DP )) were adjusted to the precipitation data measured by the rain gauge network. The results suggest that the adjustments differ little, statistically. The relative error for the analyzes with rainfall hourly totals oscillated between 27,5 and 30,6 %. The variation coefficient (R 2 ) of the five relations ranged between 0,87 and 0,92 or more than 85 % of the variance was explained by the adjustments of the five relations indicated. Errors increased with decreasing precipitation accumulation time. The average relative error varied from 34,4 % in 30 minutes to 46,1 % in 10 minutes. In general, the R(Z) and R(Z, Z DR , K DP ) relations underestimate the precipitation rate with increasing distance to the meteorological radar MXPOL. Adjustment of the R(K DP ) relation improved with increasing precipitation rate. On the other hand, the performance of the R(Z) relation improved for precipitation rates R &lt; 5 mmh -1 , which corroborates with results of other studies in the literature.

Este trabalho compreende a estimativa de taxa de precipitação (R) por meio de variáveis polarimétricas do radar meteorológico MXPOL. As variáveis polarimétricas refletividade efetiva (Z), refletividade diferencial (ZDR) e fase diferencial específica (KDP) e os dados de precipitação de dezenove pluviômetros da rede telemétrica do Alto Tietê foram utilizadas para obter relações entre taxa de precipitação e estas variáveis polarimétricas. A variável KDP foi obtida da fase diferencial e, a atenuação das variáveis refletividade efetiva e diferencial foi corrigida por meio do método de auto consistência. A correção da atenuação é mais significativa para além das células convectivas mais próximas ao MXPOL com Z> 40 dBZ. De fato, Z> 55 dBZ, associado a granizo, pode extinguir o sinal completamente. Cinco relações de transformação (R(Z), R(Z, ZDR), R(ZDR, KDP), R(KDP), R(Z, ZDR, KDP)) foram ajustadas aos dados de precipitação medidos pela rede de pluviômetros. Os resultados sugerem que os ajustes diferem pouco, estatisticamente. O erro relativo para as análises com totais de precipitação horárias oscilou entre 27,5 % e 30,6 %. O coeficiente de variação (R2) das cinco relações variou entre 0,87 e 0,92 ou mais de 85% da variância foi explicada pelos ajustes das cinco relações indicadas. Os erros aumentaram com a diminuição de tempo de acumulação de precipitação. O erro relativo médio variou de 34,4 % para acumulação de 30 minutos a 46,1 % para 10 minutos. Em geral, as relações R(Z) e R(Z, ZDR, KDP) subestimam a taxa de precipitação com aumento da distância ao radar meteorológico MXPOL. O ajuste da relação R(KDP) melhorou com o aumento da taxa de precipitação. Por outro lado, o desempenho da relação R(Z) melhorou para taxas de precipitação R< 5 mmh-1, o que corrobora com resultados de outros estudos na literatura. / This research encloses precipitation rate (R) estimation using polarimetric variables of the MXPOL meteorological radar. The polarimetric variables effective reflectivity (Z), differential reflectivity (Z DR ) and the specific differential phase (K DP ) and the precipitation data of nineteen rain gauges of the Alto Tietê telemetric network were used to obtain relations between precipitation rate and these polarimetric variables. The K DP was obtained from the differential phase and, the effective reflectivity and differential reflectivity attenuation was corrected, by the self-consistency method. The attenuation correction is more significant beyond of the convective cells closest to MXPOL with Z &gt; 40 dBZ. Indeed, Z &gt; 55 dBZ, associated with hail, can completely extinguish the signal. Five transformational relations (R(Z), R(Z, Z DR ), R(Z DR , K DP ), R(K DP ), R(Z, Z DR , K DP )) were adjusted to the precipitation data measured by the rain gauge network. The results suggest that the adjustments differ little, statistically. The relative error for the analyzes with rainfall hourly totals oscillated between 27,5 and 30,6 %. The variation coefficient (R 2 ) of the five relations ranged between 0,87 and 0,92 or more than 85 % of the variance was explained by the adjustments of the five relations indicated. Errors increased with decreasing precipitation accumulation time. The average relative error varied from 34,4 % in 30 minutes to 46,1 % in 10 minutes. In general, the R(Z) and R(Z, Z DR , K DP ) relations underestimate the precipitation rate with increasing distance to the meteorological radar MXPOL. Adjustment of the R(K DP ) relation improved with increasing precipitation rate. On the other hand, the performance of the R(Z) relation improved for precipitation rates R &lt; 5 mmh -1 , which corroborates with results of other studies in the literature.

Networks of small, often mobile, polarimetric radars are gaining popularity in the hydrometeorology community due to their rainfall observing capabilities and relative low purchase cost. In recent years, a number of installations have become operational around the globe. The problem of signal attenuation by intervening rainfall has been recognized as the major source of error in rainfall estimation by short-wavelength (C-, X, K-band) radars. The simultaneous observation of precipitation by multiple radars creates new prospects for better and more robust attenuation correction algorithms and, consequently, yields more accurate rainfall estimation. The University of Iowa hydrometeorology group's acquisition of a network of four mobile, polarimetric, X-band radars has resulted in the need for a thoughtful evaluation of the instrument. In this work, we use computer simulations and the data collected by The University of Iowa Polarimetric Radar Network to study the performance of attenuation correction methods in single-radar and network-based arrangements. To support the computer simulations, we developed a comprehensive polarimetric radar network simulator, which replicates the essential aspects of the radar network rainfall observing process. The simulations are based on a series of physics- and stochastic-based simulated rainfall events occurring over the area of interest. The characteristics of the simulated radars are those of The University of Iowa Polarimetric Radar Network. We assess the correction methods by analyzing the errors in reflectivity and rainfall rate over the area of interest covered by the network's radars. To enable the implementation of the attenuation correction methods to the data collected by The University of Iowa Polarimetric Radar Network, we first developed a set of utilities to assist with efficient data collection and analysis. Next, we conducted a series of calibration tests to evaluate the relative calibration and channel balance of the 2 network's radars. Finally, in an attempt to verify the results obtained via computer simulations, we applied the set of attenuation correction algorithms to the data collected by The University of Iowa Polarimetric Radar Network.

Soil conditions under vegetation cover and their spatial and temporal variations from point to catchment scale are crucial for understanding hydrological processes within the vadose zone, for managing irrigation and consequently maximizing yield by precision farming. Soil moisture and soil roughness are the key parameters that characterize the soil status. In order to monitor their spatial and temporal variability on large scales, remote sensing techniques are required. Therefore the determination of soil parameters under vegetation cover was approached in this thesis by means of (multi-angular) polarimetric SAR acquisitions at a longer wavelength (L-band, lambda=23cm). In this thesis, the penetration capabilities of L-band are combined with newly developed (multi-angular) polarimetric decomposition techniques to separate the different scattering contributions, which are occurring in vegetation and on ground. Subsequently the ground components are inverted to estimate the soil characteristics. The novel (multi-angular) polarimetric decomposition techniques for soil parameter retrieval are physically-based, computationally inexpensive and can be solved analytically without any a priori knowledge. Therefore they can be applied without test site calibration directly to agricultural areas. The developed algorithms are validated with fully polarimetric SAR data acquired by the airborne E-SAR sensor of the German Aerospace Center (DLR) for three different study areas in Germany. The achieved results reveal inversion rates up to 99% for the soil moisture and soil roughness retrieval in agricultural areas. However, in forested areas the inversion rate drops significantly for most of the algorithms, because the inversion in forests is invalid for the applied scattering models at L-band. The validation against simultaneously acquired field measurements indicates an estimation accuracy (root mean square error) of 5-10vol.% for the soil moisture (range of in situ values: 1-46vol.%) and of 0.37-0.45cm for the soil roughness (range of in situ values: 0.5-4.0cm) within the catchment. Hence, a continuous monitoring of soil parameters with the obtained precision, excluding frozen and snow covered conditions, is possible. Especially future, fully polarimetric, space-borne, long wavelength SAR missions can profit distinctively from the developed polarimetric decomposition techniques for separation of ground and volume contributions as well as for soil parameter retrieval on large spatial scales. / Zur Verbesserung der hydrologischen Abflussmodellierung, der Flutvorhersage, der gezielten Bewässerung von landwirtschaftlichen Nutzflächen und zum Schutz vor Ernteausfällen ist die Bestimmung der Bodenfeuchte und der Bodenrauhigkeit von grosser Bedeutung. Aufgrund der hohen zeitlichen sowie räumlichen Dynamik dieser Bodenparameter ist eine flächenhafte Erfassung mit hoher Auflösung und in kurzen zeitlichen Abständen notwendig. In situ Messtechniken stellen eine sehr zeit- und personalaufwändige Alternative dar, deshalb werden innovative Fernerkundungsverfahren mit aktivem Radar erprobt. Diese Aufnahmetechniken sind von Wetter- und Beleuchtungsverhältnissen unabhängig und besitzen zudem die Möglichkeit, abhängig von der Wellenlänge, in Medien einzudringen. Mit dem in dieser Arbeit verwendeten polarimetrischen Radar mit synthetischer Apertur (PolSAR) werden die Veränderungen der Polarisationen ausgewertet, da diese aufgrund der physikalischen Eigenschaften der reflektierenden Medien objektspezifisch verändert und gestreut werden. Es kann dadurch ein Bezug zwischen der empfangenen Radarwelle und den dielektrischen Eigenschaften (Feuchtegehalt) sowie der Oberflächengeometrie (Rauhigkeit) des Bodens hergestellt werden. Da vor allem in den gemässigten Klimazonen die landwirtschaftlichen Nutzflächen die meiste Zeit des Jahres mit Vegetation bestanden sind, wurden in dieser Dissertation Verfahren entwickelt, um die Bodenfeuchte und die Bodenrauhigkeit unter der Vegetation erfassen zu können. Um die einzelnen Rückstreubeiträge der Vegetation und des Bodens voneinander zu trennen, wurde die Eindringfähigkeit von längeren Wellenlängen (L-band, lambda=23cm) mit neu entwickelten (multi-angularen) polarimetrischen Dekompositionstechniken kombiniert, um die Komponente des Bodens zu extrahieren und auszuwerten. Für die Auswertung wurden polarimetrische Streumodelle benutzt, um die Bodenkomponente zu modellieren und dann mit der extrahierten Bodenkomponente der aufgenommenen Daten zu vergleichen. Die beste Übereinstimmung von Modell und Daten wurde als die gegebene Bodencharakteristik gewertet und dementsprechend invertiert. Die neu entwickelten, polarimetrischen Dekompositionstechniken für langwelliges polarimetrisches SAR basieren auf physikalischen Prinzipien, benötigen wenig Rechenzeit, erfordern keine Kalibrierung und sind ohne Verwendung von a priori Wissen analytisch lösbar. Um die entwickelten Algorithmen zu testen, wurden in drei verschiedenen Untersuchungsgebieten in Deutschland mit dem flugzeuggetragenen E-SAR Sensor des Deutschen Zentrums für Luft- und Raumfahrt (DLR) polarimetrische SAR Daten aufgenommen. Die Auswertungen der PolSAR Daten haben bestätigt, dass die besten Invertierungsergebnisse mit langen Wellenlängen erzielt werden können (L-Band). Des Weiteren konnten bei der Bestimmung der Bodenfeuchte und der Bodenrauhigkeit hohe Inversionsraten erreicht werden (bis zu 99% der Untersuchungsfläche). Es hat sich gezeigt, dass die polarimetrischen Streumodelle bei der gegebenen Wellenlänge nicht für bewaldete Gebiete geeignet sind, was die Anwendbarkeit des Verfahrens auf landwirtschaftliche Nutzflächen einschränkt. Die Validierung mit Bodenmessungen in den Untersuchungsgebieten, die zeitgleich zu den PolSAR Aufnahmen durchgeführt wurden, hat ergeben, dass eine kontinuierliche Beobachtung des Bodenzustandes (ausgenommen in Zeiten mit gefrorenem oder Schnee bedecktem Boden) mit einer Genauigkeit (Wurzel des mittleren quadratischen Fehlers) von 5-10vol.% für die Bodenfeuchte (in situ Messbereich: 1-46vol.%) und von 0.37-0.45cm für die Bodenrauhigkeit (in situ Messbereich: 0.5-4.0cm) möglich ist. Besonders künftige Fernerkundungsmissionen mit langwelligem, voll polarimetrischem SAR können von den entwickelten Dekompositionstechniken profitieren, um die Vegetationskomponente von der Bodenkomponente zu trennen und die Charakteristik des Oberbodens flächenhaft zu bestimmen.

The possible existence of extraterrestrial life forms has been of interest to humans for many millennia. In the past few decades space travel has provided an opportunity to search life outside of Earth. Chiral molecules are critical molecules in Earth-based life and are among the first chemical molecules sought after as proof of potential extraterrestrial life; however, identification of these chiral molecules is difficult due the lack of sensitive instruments. The objective of this work is to develop a benchmark reaction to be used as a guide in the development of instrumentation, such as a polarimeter, to be used in the search for extraterrestrial life. To achieve this objective, to investigate the enzyme mandelate racemase (MR), which catalyzes the racemization between the enantiomers of mandelate. MR is a member of the enolase superfamily, which contains a (alpha/beta)7-b barrel domain, the fold most frequently found among all known protein structures. Activity of the enzyme was measured at low temperatures and in non-aqueous media, as these are the conditions that represent extraterrestrial terrain. We find that mandelate racemase (MR) is active in concentrated ammonium salt solutions and water-in-oil microemulsions in a temperature range between 30C to 70C; however, the enzyme is not active in several organic cryosolvents. The stability of the structure of MR was also explored. Using differential scanning calorimetry (DSC) we observe the unfolding of the enzyme was irreversible and therefore kinetically controlled. We also found proof for divergent evolution of the enolase superfamily, providing evidence for divergent evolution across the MR and muconate lactonizing enzyme (MLE) subfamilies has been demonstrated. However, we also conclude that reactions yielding a polarimetric signal, such as racemizations employed in this work, are suitable as a tool to find signs of life.

Land use and land cover (LULC) change information is essential in urban planning and management. With the rapid urbanization in China, many illegal land developments have emerged in some rapidly developing regions and have caused irreversible environmental problems, posing a threat to sustainable urban development. Short-interval monitoring of LULC change therefore is necessary in these regions to control and prevent illegal land developments at an early stage. Conventional optical remote sensing is limited by weather conditions and has difficulties collecting timely data in tropical regions characterized by frequent cloud cover. Radar remote sensing, not affected by clouds, is therefore a potential tool for collecting timely LULC information in these regions. Polarimetric SAR (PolSAR) is more suitable than single-polarization SAR for monitoring LULC change because it can discriminate different types of scattering mechanisms. The overall objective of this study is to conduct short-interval monitoring of LULC change using RADARSAT-2 PolSAR images. Classification methods that achieve high accuracy for PolSAR images are essential in monitoring LULC change. In this study, a new method, based on the integration of polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms, is proposed for LULC classification using RADARSAT-2 PolSAR data. A comparison between the proposed classification method and Wishart supervised classification which is commonly used for the classification of PolSAR data showed that the proposed method can significantly improve LULC classification accuracy. Polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms have been determined to contribute to the improvement achieved by the proposed classification method. Selection of appropriate incidence angle is important in LULC classification using PolSAR images because incidence angle influences the intensity and patterns of radar return. Based on the proposed classification method, the present study further investigates the influence of incidence angle on LULC classification using RADARSAT-2 PolSAR images. LULC classifications using incidence angles of 31.50 and 37.56° were conducted separately. The influence of incidence angle on the classification was investigated by comparing the results of the two independent classifications. The comparison showed that large incidence angle performs much better than small incidence angle in the classification of different vegetation types, whereas small incidence angle outperforms large incidence angle in reducing the confusion between urban/built-up areas and vegetation, that between vegetable and barren land, and that among barren land, water, and lawn. Considering that the detection of urban/built-up areas and barren land is important in monitoring illegal land developments, small incidence angle is more suitable than large incidence angle in monitoring illegal land developments. Change detection methods that achieve high accuracy for PolSAR data are also essential in monitoring LULC change. The current study proposes a new method for LULC change detection using RADARSAT-2 PolSAR images. The proposed change detection method combines change vector analysis (CVA) and post-classification comparison (PCC) to detect LULC changes using object-oriented image analysis. The classification of PolSAR images is based on the proposed classification method. Compared with the PCC based on Wishart supervised classification, the proposed change detection method can achieve much higher accuracy for LULC change detection. Further investigation indicated that CVA, PCC, and object-oriented image analysis all contribute to the higher accuracy achieved by the proposed change detection method. Short-interval monitoring of LULC change was carried out using a time series of RADARSAT-2 PolSAR images. The monitoring was based on monthly LULC change detection using the proposed change detection method and appropriate incidence angle. The influence of environmental factors on short-interval monitoring of LULC change was investigated by analyzing the monthly change detection results. Paddy harvesting and planting, seasonal crop growth, and change in soil moisture and surface roughness were found to exert significant influence on the short-interval monitoring of LULC change. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using RADARSAT-2 PolSAR images. However, paddy harvesting and growth still cause false alarms on the monitoring of these two LULC classes. The study indicated that conducting short-interval monitoring of LULC change using RADARSAT-2 PolSAR images is effective. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using the proposed change detection and classification methods, which can provide important information for the control and prevention of illegal land developments at an early stage. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy

Fully-polarimetric UAVSAR data of Hofsjokull Ice Cap, central Iceland, taken in June 2009 was decomposed using Pauli-based coherent decomposition as well as Cloude and H/A/alpha eigenvector-based decomposition methods. The goals of this research were to evaluate the effect of the near-surface conditions of temperate glaciers on polarized SAR data and investigate the potential of creating a model of the radar scattering mechanisms based on the decomposed elements and local temperature. The results of this data analysis show a strong relationship between the Pauli and H/A/alpha decomposition elements and the near-surface conditions. Fitting curves to the normalized Pauli decomposition elements shows consistent trends across several spatially independent regions of the ice cap suggesting that the Pauli elements might be useful for modeling the scattering mechanisms of temperate ice with various surface conditions. / text