Sensitivity of surface fluxes in the ECMWF land surface model to the remotely sensed leaf area index and root distribution: Evaluation with tower flux data

The surface-atmosphere turbulent exchanges couple the water, energy and carbon budgets in the Earth system. The biosphere plays an important role in the evaporation process, and vegetation related parameters such as the leaf area index (LAI), vertical root distribution and stomatal resistance are poorly constrained due to sparse observations at the spatio-temporal scales at which land surface models (LSMs) operate. In this study, we use the Carbon Hydrology Tiled European Center for Medium-Range Weather Forecasts (ECMWF) Scheme for Surface Exchanges over Land (CHTESSEL) model and investigate the sensitivity of the simulated turbulent fluxes to these vegetation related parameters. Observed data from 17 FLUXNET towers were used to force and evaluate model simulations with different vegetation parameter configurations. The replacement of the current LAI climatology used by CHTESSEL, by a new high-resolution climatology, representative of the station’s location, has a small impact on the simulated fluxes. Instead, a revision of the root profile considering a uniform root distribution reduces the underestimation of evaporation during water stress conditions. Despite the limitations of using only one model and a limited number of stations, our results highlight the relevance of root distribution in controlling soil moisture stress, which is likely to be applicable to other LSMs

Subnational Map Of Poverty Generated From Remote-Sensing Data In Africa: Using Machine Learning Models And Advanced Regression Methods For Poverty Estimation

According to the 2020 poverty estimates from the World Bank, it is estimated that 9.1% - 9.4% of the global population lived on less than $1.90 per day. It is estimated that the Covid-19 pandemic further aggravated the issue by pushing more than 1$1.90 per day (WorldBank, 2020). To provide help and formulate effective measures, poverty needs to be located as exact as possible. For this purpose, it was investigated whether regression methods with aggregated remote-sensing data could be used to estimate poverty in Africa. Therefore, five distinct regression frameworks were compared regarding their R2 value and the mean absolute relative percentage error when estimating poverty from aggregated remote-sensing data in continental Africa. A total of 12 regression models were developed at the three poverty rates at the $1.90,$3.20, and $5.50 income level per day and can be divided into direct models, two-step models, and ensemble models. It was found that ensemble methods perform better than simpler models, with an R2 value of 0.74 for the ensemble neural net and 0.80 for the ensemble xgboost model. The best performing one step model is the kernel ridge regression with an R2 of 0.72, while the remaining frameworks of this type all perform worse. Bayesian ridge regression models consistently performed the worst compared to the other frameworks under investigation. It was found that it the model estimations were most stable at the daily income level of$1.90 and $3.20, which can be explained by the increasingly skewed distribution of target values for higher poverty thresholds. Overall, it was found that xgboost, kernel ridge regression and artificial neural networks perform better than the other models

Assimilation de données satellitaires pour le suivi des ressources en eau dans la zone Euro-Méditerranée

Une estimation plus précise de l'état des variables des surfaces terrestres est requise afin d'améliorer notre capacité à comprendre, suivre et prévoir le cycle hydrologique terrestre dans diverses régions du monde. En particulier, les zones méditerranéennes sont souvent caractérisées par un déficit en eau du sol affectant la croissance de la végétation. Les dernières simulations du GIEC (Groupe d'Experts Intergouvernemental sur l'Evolution du Climat) indiquent qu'une augmentation de la fréquence des sécheresses et des vagues de chaleur dans la région Euro-Méditerranée est probable. Il est donc crucial d'améliorer les outils et l'utilisation des observations permettant de caractériser la dynamique des processus des surfaces terrestres de cette région. Les modèles des surfaces terrestres ou LSMs (Land Surface Models) ont été développés dans le but de représenter ces processus à diverses échelles spatiales. Ils sont habituellement forçés par des données horaires de variables atmosphériques en point de grille, telles que la température et l'humidité de l'air, le rayonnement solaire et les précipitations. Alors que les LSMs sont des outils efficaces pour suivre de façon continue les conditions de surface, ils présentent encore des défauts provoqués par les erreurs dans les données de forçages, dans les valeurs des paramètres du modèle, par l'absence de représentation de certains processus, et par la mauvaise représentation des processus dans certaines régions et certaines saisons. Il est aussi possible de suivre les conditions de surface depuis l'espace et la modélisation des variables des surfaces terrestres peut être améliorée grâce à l'intégration dynamique de ces observations dans les LSMs. La télédétection spatiale micro-ondes à basse fréquence est particulièrement utile dans le contexte du suivi de ces variables à l'échelle globale ou continentale. Elle a l'avantage de pouvoir fournir des observations par tout-temps, de jour comme de nuit. Plusieurs produits utiles pour le suivi de la végétation et du cycle hydrologique sont déjà disponibles. Ils sont issus de radars en bande C tels que ASCAT (Advanced Scatterometer) ou Sentinel-1. L'assimilation de ces données dans un LSM permet leur intégration de façon cohérente avec la représentation des processus. Les résultats obtenus à partir de l'intégration de données satellitaires fournissent une estimation de l'état des variables des surfaces terrestres qui sont généralement de meilleure qualité que les simulations sans assimilation de données et que les données satellitaires elles-mêmes. L'objectif principal de ce travail de thèse a été d'améliorer la représentation des variables des surfaces terrestres reliées aux cycles de l'eau et du carbone dans le modèle ISBA grâce à l'assimilation d'observations de rétrodiffusion radar (sigma°) provenant de l'instrument ASCAT. Un opérateur d'observation capable de représenter les sigma° ASCAT à partir de variables simulées par le modèle ISBA a été développé. Une version du WCM (water cloud model) a été mise en œuvre avec succès sur la zone Euro-Méditerranée. Les valeurs simulées ont été comparées avec les observations satellitaires. Une quantification plus détaillée de l'impact de divers facteurs sur le signal a été faite sur le sud-ouest de la France. L'étude de l'impact de la tempête Klaus sur la forêt des Landes a montré que le WCM est capable de représenter un changement brutal de biomasse de la végétation. Le WCM est peu efficace sur les zones karstiques et sur les surfaces agricoles produisant du blé. Dans ce dernier cas, le problème semble provenir d'un décalage temporel entre l'épaisseur optique micro-ondes de la végétation et l'indice de surface foliaire de la végétation. Enfin, l'assimilation directe des sigma° ASCAT a été évaluée sur le sud-ouest de la France.More accurate estimates of land surface conditions are important for enhancing our ability to understand, monitor, and predict key variables of the terrestrial water cycle in various parts of the globe. In particular, the Mediterranean area is frequently characterized by a marked impact of the soil water deficit on vegetation growth. The latest IPCC (Intergovernmental Panel on Climate Change) simulations indicate that occurrence of droughts and warm spells in the Euro-Mediterranean region are likely to increase. It is therefore crucial to improve the ways of understanding, observing and simulating the dynamics of the land surface processes in the Euro-Mediterranean region. Land surface models (LSMs) have been developed for the purpose of representing the land surface processes at various spatial scales. They are usually forced by hourly gridded atmospheric variables such as air temperature, air humidity, solar radiation, precipitation, and are used to simulate land surface states and fluxes. While LSMs can provide a continuous monitoring of land surface conditions, they still show discrepancies due to forcing and parameter errors, missing processes and inadequate model physics for particular areas or seasons. It is also possible to observe the land surface conditions from space. The modelling of land surface variables can be improved through the dynamical integration of these observations into LSMs. Remote sensing observations are particularly useful in this context because they are able to address global and continental scales. Low frequency microwave remote sensing has advantages because it can provide regular observations in all-weather conditions and at either daytime or night-time. A number of satellite-derived products relevant to the hydrological and vegetation cycles are already available from C-band radars such as the Advanced Scatterometer (ASCAT) or Sentinel-1. Assimilating these data into LSMs permits their integration in the process representation in a consistent way. The results obtained from assimilating satellites products provide land surface variables estimates that are generally superior to the model estimates or satellite observations alone. The main objective of this thesis was to improve the representation of land surface variables linked to the terrestrial water and carbon cycles in the ISBA LSM through the assimilation of ASCAT backscatter (sigma°) observations. An observation operator capable of representing the ASCAT sigma° from the ISBA simulated variables was developed. A version of the water cloud model (WCM) was successfully implemented over the Euro-Mediterranean area. The simulated values were compared with those observed from space. A more detailed quantification of the influence of various factors on the signal was made over southwestern France. Focusing on the Klaus storm event in the Landes forest, it was shown that the WCM was able to represent abrupt changes in vegetation biomass. It was also found that the WCM had shortcomings over karstic areas and over wheat croplands. It was shown that the latter was related to a discrepancy between the seasonal cycle of microwave vegetation optical depth (VOD) and leaf area index (LAI). Finally, the direct assimilation of ASCAT sigma° observations was assessed over southwestern France

The impact of Land Surface Model vegetation parameterization on the terrestrial water and carbon cycles

The surface-atmosphere turbulent exchanges couple the water, energy, and carbon cycles in the earth system. The biosphere plays an important role in the evaporation process, and vegetation related parameters such as the leaf area index (LAI), vertical root distribution and stomatal resistance are poorly constrained due to sparse observations at the spatial-temporal scales at which land surface models (LSMs) operate. Considering the central role of the land surface vegetation in the climate system, various networks and methods of observational data and processes are used to constrain different ECMWF (European Center for Medium-Range Weather Forecasts) model configurations to better understand underlying conditions of the land surface energy, water and carbon budgets. Observed data from the FLUXNET network are used to perform offline point simulations with a strong emphasis on the representation of evaporation and its link with water stress conditions. The close relationship between LAI and the minimum canopy resistance is investigated and shows some model performance improvements potential but failed to solve other issues such as excessive evaporative drought conditions. However, the replacement of the exponential roots profile by a uniform roots distribution and associated maximum rooting depth reduced the underestimation of evaporation during water stress conditions. This result highlights the importance of root distribution in controlling soil moisture resistance in water stress conditions. The revised uniform root profile also has a positive effect on the model carbon cycle representation and brings model output closer to observations. Moreover, the positive effect of this new root scheme on model performance is intensified when coupled with the A-gs photosynthesis-conductance scheme as opposed to the traditionally used Jarvis approach, which implies that a more physiologically based model vegetation parameterization could benefit land surface models performance. The same findings also apply at the grid scale, where the replacement of the current vegetation types, cover, characteristics and LAI, by a new high resolution remote sensing derived global vegetation dataset did not significantly affect the simulated water budgets. However, the deeper and uniform root scheme allows plants to use soil water more efficiently and induce a more intense and positive response from land surface models. Even if an increase in model resolution could improve the simulations, the core of LSMs discrepancies reflect parameterization uncertainty to represent and capture smaller-scale unresolved processes and integrate them to the model grid-scale

3D microwave tomography with huber regularization applied to realistic numerical breast phantoms

Quantitative active microwave imaging for breast cancer screening and therapy monitoring applications requires adequate reconstruction algorithms, in particular with regard to the nonlinearity and ill-posedness of the inverse problem. We employ a fully vectorial three-dimensional nonlinear inversion algorithm for reconstructing complex permittivity profiles from multi-view single-frequency scattered field data, which is based on a Gauss-Newton optimization of a regularized cost function. We tested it before with various types of regularizing functions for piecewise-constant objects from Institut Fresnel and with a quadratic smoothing function for a realistic numerical breast phantom. In the present paper we adopt a cost function that includes a Huber function in its regularization term, relying on a Markov Random Field approach. The Huber function favors spatial smoothing within homogeneous regions while preserving discontinuities between contrasted tissues. We illustrate the technique with 3D reconstructions from synthetic data at 2GHz for realistic numerical breast phantoms from the University of Wisconsin-Madison UWCEM online repository: we compare Huber regularization with a multiplicative smoothing regularization and show reconstructions for various positions of a tumor, for multiple tumors and for different tumor sizes, from a sparse and from a denser data configuration

Artefact Reduction Methods for Iterative Reconstruction in Full-fan Cone Beam CT Radiotherapy Applications

A cone beam CT (CBCT) system acquires two-dimensional projection images of an imaging object from multiple angles in one single rotation and reconstructs the object geometry in three dimensions for volumetric visualization. It is mounted on most modern linear accelerators and is routinely used in radiotherapy to verify patient positioning, monitor patient contour changes throughout the course of treatment, and enable adaptive radiotherapy planning. Iterative image reconstruction algorithms use mathematical methods to iteratively solve the reconstruction problem. Iterative algorithms have demonstrated improvement in image quality and / or reduction in imaging dose over traditional filtered back-projection (FBP) methods. However, despite the advancement in computer technology and growing availability of open-source iterative algorithms, clinical implementation of iterative CBCT has been limited. This thesis does not report development of codes for new iterative image reconstruction algorithms. It focuses on bridging the gap between the algorithm and its implementation by addressing artefacts that are the results of imperfections from the raw projections and from the imaging geometry. Such artefacts can severely degrade image quality and cannot be removed by iterative algorithms alone. Practical solutions to solving these artefacts will be presented and this in turn will better enable clinical implementation of iterative CBCT reconstruction

Space adaptive and hierarchical Bayesian variational models for image restoration

The main contribution of this thesis is the proposal of novel space-variant regularization or penalty terms motivated by a strong statistical rational. In light of the connection between the classical variational framework and the Bayesian formulation, we will focus on the design of highly flexible priors characterized by a large number of unknown parameters. The latter will be automatically estimated by setting up a hierarchical modeling framework, i.e. introducing informative or non-informative hyperpriors depending on the information at hand on the parameters. More specifically, in the first part of the thesis we will focus on the restoration of natural images, by introducing highly parametrized distribution to model the local behavior of the gradients in the image. The resulting regularizers hold the potential to adapt to the local smoothness, directionality and sparsity in the data. The estimation of the unknown parameters will be addressed by means of non-informative hyperpriors, namely uniform distributions over the parameter domain, thus leading to the classical Maximum Likelihood approach. In the second part of the thesis, we will address the problem of designing suitable penalty terms for the recovery of sparse signals. The space-variance in the proposed penalties, corresponding to a family of informative hyperpriors, namely generalized gamma hyperpriors, will follow directly from the assumption of the independence of the components in the signal. The study of the properties of the resulting energy functionals will thus lead to the introduction of two hybrid algorithms, aimed at combining the strong sparsity promotion characterizing non-convex penalty terms with the desirable guarantees of convex optimization

Improvements in magnetic resonance imaging excitation pulse design

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 241-253).This thesis focuses on the design of magnetic resonance imaging (MRI) radio-frequency (RF) excitation pulses, and its primary contributions are made through connections with the novel multiple-system single-output (MSSO) simultaneous sparse approximation problem. The contributions are both conceptual and algorithmic and are validated with simulations, as well as anthropogenic-object-based and in vivo trials on MRI scanners. Excitation pulses are essential to MRI: they excite nuclear spins within a subject that are detected by a resonant coil and then reconstructed into images. Pulses need to be as short as possible due to spin relaxation, tissue heating, and main field inhomogeneity limitations. When magnetic spins are tilted by only a small amount, pulse transmission may be interpreted as depositing energy in a continuous three-dimensional Fourier-like domain along a one-dimensional contour to form an excitation in the spatial domain. Pulse duration is proportional to the length of the contour and inversely proportional to the rate at which it is traversed, and the rate is limited by system gradient hardware restrictions. Joint design of the contour and a corresponding excitation pulse is a difficult and central problem, while determining near-optimal energy deposition once the contour is fixed is significantly easier. We first pose the NP-Hard MSSO problem and formulate greedy and convex relaxation-based algorithms with which to approximately solve it. We find that second-order-cone programming and iteratively-reweighted least squares approaches are practical techniques for solving the relaxed problem and prove that single-vector sparse approximation of a complex-valued vector is an MSSO problem.(cont.) We then focus on pulse design, first comparing three algorithms for solving linear systems of multi-channel excitation design equations, presenting experimental results from a 3 Tesla scanner with eight excitation channels. Our aim then turns toward the joint design of pulses and trajectories. We take joint design in a novel direction by utilizing MSSO theory and algorithms to design short-duration sparsity-enforced pulses. These pulses are used to mitigate transmit field inhomogeneity in the human brain at 7 Tesla, a significant step towards the clinical use of high-field imaging in the study of cancer, Alzheimer's disease, and Multiple Sclerosis. Pulses generated by the sparsity-enforced method outperform those created via conventional Fourier-based techniques, e.g., when attempting to produce a uniform magnetization in the presence of severe RF inhomogeneity, a 5.7-ms 15-spoke pulse generated by the sparsity-enforced method produces an excitation with 1.28 times lower root-mean-square error than conventionally-designed 15-spoke pulses. To achieve this same level of uniformity, conventional methods must use 29-spoke pulses that are 1.4 times longer. We then confront a subset selection problem that arises when a parallel excitation system has more transmit modes available than hardware transmit channels with which to drive them. MSSO theory and algorithms are again applicable and determine surprising targetspecific mixtures of light and dark modes that yield high-quality excitations. Finally, we study the critical patient safety issue of specific absorption rate (SAR) of multi-channel excitation pulses at high field. We develop a fast SAR calculation algorithm and propose optimizing an individual pulse and time-multiplexing a set of pulses as ways to reduce SAR; the latter is capable of reducing maximum local SAR by 11% with no impact on pulse duration.by Adam Charles Zelinski.Ph.D