Modelling of Multi-Frequency Microwave Backscatter and Emission of Land Surface by a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP)

Emission and backscattering signals of land surfaces at different frequencies have distinctive responses to soil and vegetation physical states. The use of multi-frequency combined active and passive microwave signals provides complementary information to better understand and interpret the observed signals in relation to surface states and the underlying physical processes. Such a capability also improves our ability to retrieve surface parameters and states such as soil moisture, freeze-thaw dynamics and vegetation biomass and vegetation water content (VWC) for ecosystem monitoring. We present here a prototype Community Land Active Passive Microwave Radiative Transfer Modelling platform (CLAP) for simulating both backscatter (&sigma;0) and emission (TB) signals of land surfaces, in which the CLAP is backboned by an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering, and the Tor Vergata model for modelling vegetation scattering and the interaction between vegetation and soil parts. The CLAP was used to simulate both ground-based and space-borne multi-frequency microwave measurements collected at the Maqu observatory on the eastern Tibetan plateau. The ground-based systems include a scatterometer system (1&ndash;10 GHz) and an L-band microwave radiometer. The space-borne measurements are obtained from the X-band and C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) radiation observations. The impacts of different vegetation properties (i.e., structure, water and temperature dynamics) and soil conditions (i.e., different moisture and temperature profiles) on the microwave signals were investigated by CLAP simulation for understanding factors that can account for diurnal variations of the observed signals. The results show that the dynamic VWC partially accounts for the diurnal variation of the observed signal at the low frequencies (i.e., S- and L-bands), while the diurnal variation of the observed signals at high frequencies (i.e., X- and C-bands) is more due to vegetation temperature changing, which implies the necessity to first disentangle the impact of vegetation temperature for the use of high frequency microwave signals. The model derived vegetation optical depth &tau; differs in terms of frequencies and different model parameterizations, while its diurnal variation depends on the diurnal variation of VWC regardless of frequency. After normalizing &tau; at multi-frequency by wavenumber, difference is still observed among different frequencies. This indicates that &tau; is indeed frequency-dependent, and &tau; for each frequency is suggested to be applied in the retrieval of soil and vegetation parameters. Moreover, &tau; at different frequencies (e.g., X-band and L-band) cannot be simply combined for constructing accurate long time series microwave-based vegetation product. To this purpose, it is suggested to investigate the role of the leaf water potential in regulating plant water use and its impact on the normalized &tau; at multi-frequency. Overall, the CLAP is expected to improve our capability for understanding and applying current and future multi-frequency space-borne microwave systems (e.g. those from ROSE-L and CIMR) for vegetation monitoring.</p

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Multiple Volume Scattering in Random Media and Periodic Structures with Applications in Microwave Remote Sensing and Wave Functional Materials

The objective of my research is two-fold: to study wave scattering phenomena in dense volumetric random media and in periodic wave functional materials. For the first part, the goal is to use the microwave remote sensing technique to monitor water resources and global climate change. Towards this goal, I study the microwave scattering behavior of snow and ice sheet. For snowpack scattering, I have extended the traditional dense media radiative transfer (DMRT) approach to include cyclical corrections that give rise to backscattering enhancements, enabling the theory to model combined active and passive observations of snowpack using the same set of physical parameters. Besides DMRT, a fully coherent approach is also developed by solving Maxwell’s equations directly over the entire snowpack including a bottom half space. This revolutionary new approach produces consistent scattering and emission results, and demonstrates backscattering enhancements and coherent layer effects. The birefringence in anisotropic snow layers is also analyzed by numerically solving Maxwell’s equation directly. The effects of rapid density fluctuations in polar ice sheet emission in the 0.5~2.0 GHz spectrum are examined using both fully coherent and partially coherent layered media emission theories that agree with each other and distinct from incoherent approaches. For the second part, the goal is to develop integral equation based methods to solve wave scattering in periodic structures such as photonic crystals and metamaterials that can be used for broadband simulations. Set upon the concept of modal expansion of the periodic Green’s function, we have developed the method of broadband Green’s function with low wavenumber extraction (BBGFL), where a low wavenumber component is extracted and results a non-singular and fast-converging remaining part with simple wavenumber dependence. We’ve applied the technique to simulate band diagrams and modal solutions of periodic structures, and to construct broadband Green’s functions including periodic scatterers.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135885/1/srtan_1.pd

Parameter Optimization of a Discrete Scattering Model by Integration of Global Sensitivity Analysis Using SMAP Active and Passive Observations

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LAI) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the active-only model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for &#x03C3; HH&#x2070; , &#x03C3; VV&#x2070; , TBH, and TBV, respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data

Multiple Volume Scattering in Random Media and Periodic Structures with Applications in Microwave Remote Sensing and Wave Functional Materials

The objective of my research is two-fold: to study wave scattering phenomena in dense volumetric random media and in periodic wave functional materials. For the first part, the goal is to use the microwave remote sensing technique to monitor water resources and global climate change. Towards this goal, I study the microwave scattering behavior of snow and ice sheet. For snowpack scattering, I have extended the traditional dense media radiative transfer (DMRT) approach to include cyclical corrections that give rise to backscattering enhancements, enabling the theory to model combined active and passive observations of snowpack using the same set of physical parameters. Besides DMRT, a fully coherent approach is also developed by solving Maxwell’s equations directly over the entire snowpack including a bottom half space. This revolutionary new approach produces consistent scattering and emission results, and demonstrates backscattering enhancements and coherent layer effects. The birefringence in anisotropic snow layers is also analyzed by numerically solving Maxwell’s equation directly. The effects of rapid density fluctuations in polar ice sheet emission in the 0.5~2.0 GHz spectrum are examined using both fully coherent and partially coherent layered media emission theories that agree with each other and distinct from incoherent approaches. For the second part, the goal is to develop integral equation based methods to solve wave scattering in periodic structures such as photonic crystals and metamaterials that can be used for broadband simulations. Set upon the concept of modal expansion of the periodic Green’s function, we have developed the method of broadband Green’s function with low wavenumber extraction (BBGFL), where a low wavenumber component is extracted and results a non-singular and fast-converging remaining part with simple wavenumber dependence. We’ve applied the technique to simulate band diagrams and modal solutions of periodic structures, and to construct broadband Green’s functions including periodic scatterers.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137141/1/srtan_1.pd

Développement d’un système d’assimilation de mesures satellites micro-ondes passives dans un modèle de neige pour la prévision hydrologique au Québec

Dans le contexte québécois (Est du Canada), une bonne gestion de la ressource en eau est devenue un enjeu économique majeur et permet également d’éviter d’importantes catastrophes naturelles lors des crues printanières. La plus grande incertitude des modèles de prévision hydrologique résulte de la méconnaissance de la quantité de neige au sol accumulée durant l’hiver. Pour optimiser la gestion de ses barrages hydroélectriques, l'entreprise Hydro-Québec veut pouvoir mieux quantifier et anticiper l'apport en eau que représentera la fonte des neiges au printemps. Cet apport est estimé à partir de l’équivalent en eau de la neige (‘ÉEN’, ou Snow Water Equivalent, ‘SWE’) extrapolé sur l’ensemble d’un territoire. Cette étude se concentre sur la zone subarctique et boréale du Québec (58° - 45°N) incluant les bassins hydrographiques du complexe de la Baie James et du sud du Québec. Ces territoires représentent des régions immenses et hétérogènes difficiles d’accès. Le faible nombre de stations météorologiques permanentes et de relevés nivométriques entrainent de fortes incertitudes dans l’extrapolation de l’équivalent en eau de la neige, que ce soit à partir de mesures au sol ou de modèles de neige pilotés par des forçages météorologiques. La couverture quasi - quotidienne et globale des observations satellitaires est donc une source d’information au potentiel certain, mais encore peu utilisée pour ajuster les estimations de l’ÉEN dans les modèles hydrologiques. Utilisant les observations satellitaires micro-ondes passives (MOP) et des mesures de hauteurs de neige au sol pour ajuster les cartes de neige interpolées, le produit ÉEN GlobSnow2 est actuellement considéré comme un des plus performants à l’échelle globale. En comparant ce produit à une série temporelle de 30 ans de données au sol sur l’Est du Canada (1980 – 2009, avec un total de 38 990 mesures d’ÉEN), nous avons montré que sa précision n'était pas adaptée pour les besoins d'Hydro-Québec, avec une erreur quadratique moyenne (RMSE) relative de l'ordre de 36%. Une partie des incertitudes provient de la non représentativité des mesures de hauteur de neige au sol. Ce travail de thèse s'est donc concentré sur l'amélioration de la prédiction du couvert nival au Québec par l’assimilation des observations satellitaires MOP sans utilisation de relevés au sol. Les observations, décrites comme des températures de brillance (TB), sont fournies par les radiomètres AMSR-2 (Advanced Microwave Scanning Radiometer – 2) embarqués sur le satellite Jaxa (10 x 10 km2). L’approche développée propose de coupler un modèle de neige (Crocus de Météo-France) avec un modèle de transfert radiatif (DMRT-ML du LGGE, Grenoble) pour simuler l’émission du manteau neigeux modélisé. Des modèles de transfert radiatifs de végétation, de sol et d’atmosphère sont ajoutés et calibrés pour représenter le signal MOP au niveau des capteurs satellitaires. Les observations MOP d’AMSR-2 sont alors assimilées en réajustant directement les forçages atmosphériques pilotant le modèle de neige. Ces forçages sont dérivés du modèle de prévision atmosphérique canadien GEM à 10 km de résolution spatiale. Le système d’assimilation implémenté est un filtre particulaire par rééchantillonnage d’importance. La chaîne de modèles a été calibrée et validée avec des mesures au sol de radiométrie micro-onde et des relevés continus d’ÉEN et de hauteurs de neige. L’assimilation des TB montre d'excellents résultats avec des observations synthétiques simulées, améliorant la RMSE sur l’ÉEN de 82% comparé aux simulations d’ÉEN sans assimilation. Les experiences préliminaires de l’assimilation des observations satellitaires d’AMSR-2 en 11, 19 et 37 GHz (verticale polarization) montrent une amélioration significative des biais sur les ÉEN simulés sur un important jeu de données ponctuelles (12 stations de mesures d’ÉEN continues sur 4 années). La moyenne des biais inversés des valeurs d’ÉEN moyens et maximums sont réduits respectivement de 71 % et 32 % par rapport aux simulations d’ÉEN sans assimilation. Avec l’assimilation des observations d’AMSR-2 et pour les sites avec moins de 75 % de couverts forestiers, le pourcentage d'erreur relative sur l’ÉEN par rapport aux observations est de 15 % (contre 20 % sans assimilation), soit une précision significativement améliorée pour des applications hydrologiques. Ce travail ouvre de nouvelles perspectives très prometteuses pour la cartographie d’ÉEN à des fins hydrologiques sur une base journalière

Earth resources: A continuing bibliography (issue 26)

This bibliography lists 480 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1, 1980 and June 30, 1980. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis