Passive Microwave Remote Sensing of Snow Layers Using Novel Wideband Radiometer Systems and RFI Mitigation

Climate change can reduce the availability of water resources in many regions, and it will affect agriculture, industry, and energy supply. Snowpack monitoring is important in water resource management as well as flood and avalanche protection. The rapid melting process due to global warming changes the snowpacks' annual statistics, including the extent, and the snow water equivalent (SWE) of seasonal snowpacks, which results in non-stationary annual statistics that should be monitored in nearly daily intervals. The development of advanced radiometric sensors capable of accurately measuring the snowpack thickness and SWE is needed for the long-term study of the snowpack parameters' statistical changes. Passive microwave radiometry provides a means for measuring the microwave emission from a scene of snow and ice. A Wideband Autocorrelation Radiometer (ac{WiBAR}) operating from 1-2~GHz measures spontaneous emission from snowpack at long wavelengths where the scattering is minimized, but the snow layer coherent effects are preserved. By using a wide bandwidth to measure the spacing between frequencies of constructive and destructive interference of the emission from the soil under the snow, it can reveal the microwave travel time through the snow, and thus the snow depth. However, narrowband radio frequency interference (RFI) in the WiBAR's frequency of operations reduces the ability of the WiBAR to measure the thickness accurately. In addition, the current WiBAR system is a frequency domain, FD-WiBAR, system that uses a field-portable spectrum analyzer to collect the data and suffers from high data acquisition time which limits its applications for spaceborne and airborne technologies. In this work, a novel frequency tunable microwave comb filter is proposed for RFI mitigation. The frequency response of the proposed filter has a pattern with many frequencies band-pass and band rejection that preserves the frequency span while reducing the RFI. Moreover, we demonstrate time-domain WiBAR, TD-WiBAR, which presented as an alternative method for FD-WiBAR, and is capable of providing faster data acquisition. A new time-domain calibration is also developed for TD-WiBAR and evaluated with the frequency domain calibration. To validate the TD-WiBAR method, simulated laboratory measurements are performed using a microwave scene simulator circuit. Then the WiBAR instrument is enhanced with the proposed comb filter and showed the RFI mitigation in time-domain mode on an instrument bench test. Furthermore, we analyze the effects of an above snow vegetation layer on brightness temperature spectra, particularly the possible decay of wave coherence arising from volume scattering in the vegetation canopy. In our analysis, the snow layer is assumed to be flat, and its upward emission and surface reflectivities are modeled by a fully coherent model, while an incoherent radiative transfer model describes the volume scattering from the vegetation layer. We proposed a unified framework of vegetation scattering using radiative transfer (RT) theory for passive and active remote sensing of vegetated land surfaces, especially those associated with moderate-to-large vegetation water contents (VWCs), e.g., forest field. The framework allows for modeling passive and active microwave signatures of the vegetated field with the same physical parameters describing the vegetation structure. The proposed model is validated with the passive and active L-band sensor (PALS) acquired in SMAPVEX12 measurements in 2012, demonstrating the applicability of this model.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169653/1/maryamsa_1.pd

Sensors Application in Agriculture

Novel technologies are playing an important role in the development of crop and livestock farming and have the potential to be the key drivers of sustainable intensification of agricultural systems. In particular, new sensors are now available with reduced dimensions, reduced costs, and increased performances, which can be implemented and integrated in production systems, providing more data and eventually an increase in information. It is of great importance to support the digital transformation, precision agriculture, and smart farming, and to eventually allow a revolution in the way food is produced. In order to exploit these results, authoritative studies from the research world are still needed to support the development and implementation of new solutions and best practices. This Special Issue is aimed at bringing together recent developments related to novel sensors and their proved or potential applications in agriculture

Modélisation 3D du transfert raidatif pour simuler les images et données de spectroradiomètres et Lidars satellites et aéroportés de couverts végétaux et urbains

Les mesures de télédétection (MT) dépendent de l'interaction du rayonnement avec les paysages terrestres et l'atmosphère ainsi que des configurations instrumentales (bande spectrale, résolution spatiale, champ de vue: FOV,...) et expérimentales (structure et propriétés optiques du paysage et atmosphère,...). L'évolution rapide des techniques de télédétection requiert des outils appropriés pour valider leurs principes et améliorer l'emploi des MT. Les modèles de transfert radiatif (RTM) simulent des quantités (fonctions de distribution de la réflectance (BRDF) et température (BTDF), forme d'onde LiDAR, etc.) plus ou moins proches des MT. Ils constituent l'outil de référence pour simuler les MT, pour diverses applications : préparation et validation des systèmes d'observation, inversion de MT,... DART (Discrete Anisotropic Radiative Transfer) est reconnu comme le RTM le plus complet et efficace. J'ai encore nettement amélioré son réalisme via les travaux de modélisation indiqués ci-dessous. 1. Discrétisation de l'espace des directions de propagation des rayons. DART simule la propagation des rayons dans les paysages terrestres et l'atmosphère selon des directions discrètes. Les méthodes classiques définissent mal le centroïde et forme des angles solides de ces directions, si bien que le principe de conservation de l'énergie n'est pas vérifié et que l'obtention de résultats précis exige un grand nombre de directions. Pour résoudre ce problème, j'ai conçu une méthode originale qui crée des directions discrètes de formes définies. 2. Simulation d'images de spectroradiomètre avec FOV fini (caméra, pushbroom,...). Les RTMs sont de type "pixel" ou "image". Un modèle "pixel" calcule une quantité unique (BRDF, BTDF) de toute la scène simulée via sa description globale (indice foliaire, fraction d'ombre,...). Un modèle "image" donne une distribution spatiale de quantités (BRDF,...) par projection orthographique des rayons sur un plan image. Tous les RTMs supposent une acquisition monodirectionnelle (FOV nul), ce qui peut être très imprécis. Pour pouvoir simuler des capteurs à FOV fini (caméra, pushbroom,...), j'ai conçu un modèle original de suivi de rayons convergents avec projection perspective. 3. Simulation de données LiDAR. Beaucoup de RTMs simulent le signal LiDAR de manière rapide mais imprécise (paysage très simplifié, pas de diffusions multiples,...) ou de manière précis mais avec de très grands temps de calcul (e.g., modèles Monte-Carlo: MC). DART emploie une méthode "quasi-MC" originale, à la fois précise et rapide, adaptée à toute configuration instrumentale (altitude de la plateforme, attitude du LiDAR, taille de l'empreinte,...). Les acquisitions multi-impulsions LiDAR (satellite, avion, terrestre) sont simulées pour toute configuration (position du LiDAR, trajectoire de la plateforme,...). Elles sont converties dans un format industriel pour être traitées par des logiciels dédiés. Un post-traitement convertit les formes d'onde LiDAR simulées en données LiDAR de comptage de photons. 4. Bruit solaire et fusion de données LiDAR et d'images de spectroradiomètre. DART peut combiner des simulations de LiDAR multi-impulsions et d'image de spectro-radiomètre (capteur hyperspectral,...). C'est une configuration à 2 sources (soleil, laser LiDAR) et 1 capteur (télescope du LiDAR). Les régions mesurées par le LiDAR, dans le plan image du sol, sont segmentées dans l'image du spectro-radiomètre, elle aussi projetée sur le plan image du sol. Deux applications sont présentées : bruit solaire dans le signal LiDAR, et fusion de données LiDAR et d'images de spectro-radiomètre. Des configurations d'acquisition (trajectoire de plateforme, angle de vue par pixel du spectro-radiomètre et par impulsion LiDAR) peuvent être importées pour encore améliorer le réalisme des MT simulées, De plus, j'ai introduit la parallélisation multi-thread, ce qui accélère beaucoup les calculsRemote Sensing (RS) data depend on radiation interaction in Earth landscapes and atmosphere, and also on instrumental (spectral band, spatial resolution, field of view (FOV),...) and experimental (landscape/atmosphere architecture and optical properties,...) conditions. Fast developments in RS techniques require appropriate tools for validating their working principles and improving RS operational use. Radiative Transfer Models (RTM) simulate quantities (bidirectional reflectance; BRDF, directional brightness temperature: BTDF, LiDAR waveform...) that aim to approximate actual RS data. Hence, they are celebrated tools to simulate RS data for many applications: preparation and validation of RS systems, inversion of RS data... Discrete Anisotropic Radiative Transfer (DART) model is recognized as the most complete and efficient RTM. During my PhD work, I further improved its modeling in terms of accuracy and functionalities through the modeling work mentioned below. 1. Discretizing the space of radiation propagation directions.DART simulates radiation propagation along a finite number of directions in Earth/atmosphere scenes. Classical methods do not define accurately the solid angle centroids and geometric shapes of these directions, which results in non-conservative energy or imprecise modeling if few directions are used. I solved this problem by developing a novel method that creates discrete directions with well-defined shapes. 2. Simulating images of spectroradiometers with finite FOV.Existing RTMs are pixel- or image-level models. Pixel-level models use abstract landscape (scene) description (leaf area index, overall fraction of shadows,...) to calculate quantities (BRDF, BTDF,...) for the whole scene. Image-level models generate scene radiance, BRDF or BTDF images, with orthographic projection of rays that exit the scene onto an image plane. All models neglect the multi-directional acquisition in the sensor finite FOV, which is unrealistic. Hence, I implemented a sensor-level model, called converging tracking and perspective projection (CTPP), to simulate camera and cross-track sensor images, by coupling DART with classical perspective and parallel-perspective projection. 3. Simulating LiDAR data.Many RTMs simulate LiDAR waveform, but results are inaccurate (abstract scene description, account of first-order scattering only...) or require tremendous computation time for obtaining accurate results (e.g., Monte-Carlo (MC) models). With a novel quasi-MC method, DART can provide accurate results with fast processing speed, for any instrumental configuration (platform altitude, LiDAR orientation, footprint size...). It simulates satellite, airborne and terrestrial multi-pulse laser data for realistic configurations (LiDAR position, platform trajectory, scan angle range...). These data can be converted into industrial LiDAR format for being processed by LiDAR processing software. A post-processing method converts LiDAR waveform into photon counting LiDAR data, through modeling single photon detector acquisition. 4. In-flight Fusion of LiDAR and imaging spectroscopy.DART can combine multi-pulse LiDAR and cross-track imaging spectroscopy (hyperspectral sensor...). It is a 2 sources (sun, LiDAR laser) and 1 sensor (LiDAR telescope) system. First, a LiDAR multi-pulse acquisition and a sun-induced spectro-radiometer radiance image are simulated. Then, the LiDAR FOV regions projected onto the ground image plane are segmented in the spectro-radiometer image, which is also projected on the ground image plane. I applied it to simulate solar noise in LiDAR signal, and to the fusion of LiDAR data and spectro-radiometer images. To further improve accuracy when simulating actual LiDAR and spectro-radiometer, DART can also import actual acquisition configuration (platform trajectory, view angle per spectro-radiometer pixel / LiDAR pulse). Moreover, I introduced multi-thread parallelization, which greatly accelerates DART simulation

Country-specific Ground-based Bistatic Radar Clutter Analysis of Rural Environments

This thesis presents a novel statistical analysis of bistatic radar rural ground clutter for different terrain types of German rural environments under low grazing angles. A country-specific clutter analysis for subgroups of rural environments rather than for the rural environment as a whole will be presented. Therefore, the rural environment is divided into four dominant subgroup terrain types, namely fields with low vegetation, fields with high vegetation, plantations of small trees and forest environments, representing a typical rural German or even Central European environment. The thesis will present the bistatic clutter characteristics for both the summer and the winter vegetation. Therefore, bistatic measurement campaigns have been carried out during the summer 2019 and the winter of 2019/20 in the aforementioned four different rural terrain types. The measurements were carried out according to a designed bistatic measurement methodology to obtain comparable results and to be used for different radar applications in the radar relevant X-band at a center frequency of 8.85 GHz and over a bandwidth of 100 MHz, according to available transmit permissions. The distinction of the rural terrain into different subgroups enables a more precise and accurate clutter analysis and modeling of the statistical properties as will be shown in the presented results. A clear separation of the different types of rural terrain and the influence of the seasons was worked out. Additionally, model functions for the relevant parameters, characterizing the the bistatic clutter, are presented for their analytical description. The statistical properties are derived from the clutter regions of processed range-Doppler domain data, using an improved range-Doppler processing approach, for each of the four terrain types and the corresponding seasons. The data basis for the clutter analysis are the processed range-Doppler maps from the bistatic radar measurements using a dual-channel measurement approach, with a separate reference and surveillance channel. According to the authors’ current knowledge, a similar investigation based on real bistatic radar measurement data with the division into terrain subgroups and additionally for different season has not yet been carried out and published for a German rural environment. The presented data and results therefore have a significant impact on the research field of bistatic ground clutter, in which there are currently only very few results in the frequency range discussed in this thesis

Health monitoring of trees and investigation of tree root systems using ground penetrating radar (GPR)

Evidence suggests that trees and forests around the world are constantly being threatened by disease and environmental pressures. Over the last decade, new pathogens spread rapidly in European forests, and quarantine measures have mostly been unable to contain outbreaks. As a result, millions of trees were infected, and many of these have already died. It is therefore vital to identify infected trees in order to track, control and prevent disease spread. In addressing these challenges, the available methods often include cutting of branches and trees or incremental coring of trees. However, not only do the tree itself and its surrounding environment suffer from these methods, but they also are costly, laborious and time-consuming. In recent years the application of non-invasive testing techniques has been accepted and valued in this particular area. Given its flexibility, rapidity of data collection and cost-efficiency, Ground Penetrating Radar (GPR) has been increasingly used in this specific area of research. Consequently, this PhD Thesis aims at addressing a major challenge within the context of early identification of tree decay and tree disease control using GPR. In more detail, two main topics are addressed, namely the characterisation of the internal structure of tree trunks, and the assessment of tree root systems’ architecture. As a result, a comprehensive methodology for the assessment of both tree trunks and roots using GPR is presented, which includes the implementation of novel algorithms and GPR signal processing approaches for the characterisation of tree trunks’ internal structure and the three-dimensional mapping of tree root systems. Results of this research project were promising and will contribute towards the establishment of novel tree evaluation approaches

A quantitative and qualitative approach to cuttlefish (Sepia officinalis) body patterning

Cuttlefish are renowned for their ability to quickly alter the colour and texture of their skin, for camouflage and communication. This is due to the presence of thousands of pigment-filled sacs, known as chromatophores, which are distributed across the skin. The chromatophores are innervated by motoneurons, which dilate the chromatophores to create the spots, stripes, and other markings, known as chromatic components. There are 34 recognized chromatic components, and it is an interesting question how cuttlefish coordinate the expression of these components to camouflage and communicate. The digital age has introduced new, powerful algorithms and methods to tease out subtle features in the coloration patterns, by means of image registration, segmentation, and identification, as well as methods for modeling the underlying control systems. These tools offer major new insights into the mechanisms of visual perception. In addition, powerful techniques have recently been developed that have yet to be applied to this complex visual motor control system. These methods have large potential in helping discover what features between the pattern and the environment are necessary to prevent detection. Here I present four laboratory experiments, that for the first time use machine learning models, to investigate cuttlefish pattern formation, implementation, and information. The first two experimental chapters investigate how cuttlefish orchestrate their chromatic components for camouflage patterns, and what strategies they employ on diverse backgrounds. I demonstrate that components are expressed more independently than previously believed, finding that the range of patterns expressed lie on a continuum, allowing us to suggest a revised classification scheme for cuttlefish body patterns. The diversity of patterns seem to imply that a cuttlefish could use its repertoire flexibly to display the maximally cryptic pattern for a given background, however I show that cuttlefish to not in fact select a single (possibly optimal) camouflage pattern, continually alter their appearance on a given background, and that the frequency of change increases in relation to the size of the objects in the environment. My third chapter investigated the language-like properties of cuttlefish communication using human speech recognition models. From our subset of cuttlefish patterns, I discovered cuttlefish utilize a lexicon of 10 patterns, with language-like properties such as: they obeyed Zipf’s law, contained around 1.6 bits per display, and interestingly, while 2 patterns were visually similar, they were displayed in separate contexts. By implementing a regression onto the patterns, I introduce a basic dictionary of cuttlefish terms and their meaning. From my investigations into cuttlefish intraspecific signaling, I discovered two previously undocumented patterns, used in agonistic encounters between cuttlefish. My final chapter describes these patterns and the contexts they are displayed

Insights into the biosphere-atmosphere exchange of organic gases from seasonal observations over a ponderosa pine forest

Includes bibliographical references.2020 Summer.The biosphere-atmosphere exchange of organic gases over forests contributes to the formation of air pollution and the availability of forest nutrients. Forests can be both sources and sinks of volatile and semi-volatile organic compounds to the atmosphere. The role that forests play in controlling organic acid concentrations remains poorly understood, with multiple model-measurement comparisons reporting missing sources of formic acid. Large, missing sources of organic acids have been identified over different forested environments. Despite substantial seasonal variability in forest productivity and environmental conditions, a paucity of observations, during seasons other than summertime, is available. Although forest fires are a major source of hazardous organic gases and particulate matter, few measurements of semi-volatile organic compounds emitted by forest fires are available from within 1 km of the fire. Detection further-afield cannot disambiguate between chemistry at the source of the fire and chemical aging as a smoke plume traverses the atmosphere. Near-field observations are needed to characterize emissions attributable to combustion and pyrolysis processes. To improve understanding of processes that control the atmospheric budgets of organic acids, water-soluble pollutants with physicochemical properties similar to organic acids, and fire-emitted phenolic compounds, this dissertation reports measurements of the biosphere-atmosphere exchange of a suite of organic gases over a Rocky Mountain ponderosa pine forest in Colorado over four, seasonally-representative measurement campaigns. First, we report seasonally persistent, upward fluxes of organic acids, which are neither explained by direct emissions nor secondary production. Second, we present evidence for equilibrium partitioning into and out of water films on forest surfaces as both a missing source and sink of isocyanic acid and small alkanoic acids. Finally, we report significant enhancement of organic acids, phenolic compounds, and other nitrogen containing compounds during initiation of a controlled forest fire compared with the remainder of the burn. Nitrated phenols are rapidly produced and enhanced more than phenolic precursors during initial, higher temperature conditions. We attribute greater enhancement of nitrated phenols to high NOx emissions under higher temperature conditions

Acoustical Exploitation of Rough, Mixed Impedance and Porous Surfaces Outdoors

This thesis is a contribution towards developing cost-effective ways for reducing outdoor traffic noise in outdoor environments by exploiting the interaction between sound travelling directly to a listener from the source and sound reflected by the intervening ground. Sound propagation over different kinds of porous, rough and mixed impedance ground surfaces have been studied experimentally and numerically. Measurements of short-range acoustic level difference spectra over outdoor ground surfaces and artificially-created surfaces outdoors and in the laboratory have been compared with predictions to establish suitable impedance models. Sound propagation over mixed impedance ground having single or multiple impedance discontinuities has also been studied. Acoustic transmission loss through vegetation, crops and hedges has been investigated. The phenomenon of sound diffraction and periodicity due to rough periodic ground surfaces has been explored through artificially created rough surfaces in the laboratory and outdoors. The phenomenon of surface wave propagation over rough hard surfaces and porous surfaces has been explored through laboratory experiments. Measured data indoors and outdoors have been used to validate numerical (BEM and FEM), empirical and analytical (MST) prediction techniques. The validated numerical methods have been used to make predictions at scales suitable for attenuating traffic noise by means of carefully designed ground treatments. The work has also been extended to railway and tramway noise. It has been found that replacing hard ground with porous ground, introducing single or multiple impedance discontinuities, growing vegetation and introducing low height roughness can all contribute between 3 and 15 dB additional attenuation of traffic noise. In respect of replacing hard ground by porous ground, it is concluded that the ground with lowest flow resistivity i.e. grassland left untouched and allowed to grow wild gives the best attenuation performance. However, dividing a single width of soft ground into alternating strips of hard and soft surfaces does not improve the insertion loss. The overall width of the soft surface is the main factor. Cultivating crops over porous ground can enhance the attenuation but the effect is not very significant for A-weighted levels as most of additional attenuation occurs at higher frequencies above 3 kHz. A 0.3 m high and at least 3 m wide lattice structure design is found to be very useful for traffic noise attenuation since it offers greater insertion loss than the same width and height of parallel low walls and the resulting attenuation is azimuthal angle independent. It has been shown also that the potentially negative effect on insertion loss due to propagation of roughness-induced surface waves over rough surfaces can be reduced by introducing sound absorbing material in between the walls