17 research outputs found

    Exploring bistatic scattering modeling for land surface applications using radio spectrum recycling in the Signal of Opportunity Coherent Bistatic Simulator

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    The potential for high spatio-temporal resolution microwave measurements has urged the adoption of the signals of opportunity (SoOp) passive radar technique for use in remote sensing. Recent trends in particular target highly complex remote sensing problems such as root-zone soil moisture and snow water equivalent. This dissertation explores the continued open-sourcing of the SoOp coherent bistatic scattering model (SCoBi) and its use in soil moisture sensing applications. Starting from ground-based applications, the feasibility of root-zone soil moisture remote sensing is assessed using available SoOp resources below L-band. A modularized, spaceborne model is then developed to simulate land-surface scattering and delay-Doppler maps over the available spectrum of SoOp resources. The simulation tools are intended to provide insights for future spaceborne modeling pursuits

    Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

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    This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

    Modeling of Ocean Surface Delay Doppler Maps Measured in S-Band Signal of Opportunity Reflectometry

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    Signals-of-Opportunity (SoOp) remote sensing of the Earth measures the reflections of existing transmitters from Earth's surface in order to discern information on the Earth's geophysical properties. While the transmissions of Global Navigation Satellite System (GNSS) emitters have been widely used in this application, other sources are also available that can provide distinct advantages. In particular, S-band (i.e. 2.34 GHz) transmissions in the satellite-based digital audio radio service (e.g. the XM and Sirius systems) provide geostationary emissions of relatively higher power and in multiple channels of ~ 2 MHz bandwidth. The reflection of these signals for example from the ocean surface can be used to sense ocean wind speeds and other properties of the sea surface. Measurements of these reflections can be processed to produce Delay Doppler Maps (DDMs) that provide further insight into the scattering process. In this thesis, a model for ocean-reflected DDMs acquired by an airborne receiver developed by Purdue University is developed for comparison with the measurements. Particular emphasis in the comparisons is placed on scattering effects outside the specular region traditionally examined in SoOp sensing by examining portions of the DDMs at significant delay and doppler offsets with respect to the specular point. In addition, the impact of the receive antenna pattern on these portions of the DDM and on the polarization of the received signals is examined. The results show the utility of these measurements for ocean remote sensing, as well as the importance of an accurate model for ocean surface scattering for predicting the results obtained.ElectroScience LaboratoryUndergraduate Research ScholarshipNo embargoAcademic Major: Electrical and Computer Engineerin

    Improving Flood Detection and Monitoring through Remote Sensing

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    As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data

    Engineering Calibration and Physical Principles of GNSS-Reflectometry for Earth Remote Sensing

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    The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA mission that uses 32 Global Positioning System (GPS) satellites as active sources and 8 CYGNSS satellites as passive receivers to measure ocean surface roughness and wind speed, as well as soil moisture and flood inundation over land. This dissertation addresses two major aspects of engineering calibration: (1) characterization of the GPS effective isotropic radiated power (EIRP) for calibration of normalized bistatic radar cross section (NBRCS) observables; and (2) development of an end-to-end calibration approach using modeling and measurements of ocean surface mean square slope (MSS). To estimate the GPS transmit power, a ground-based GPS constellation power monitor (GCPM) system has been built to accurately and precisely measure the direct GPS signals. The transmit power of the L1 coarse/acquisition (C/A) code of the full GPS constellation is estimated using an optimal search algorithm. Updated values for transmit power have been successfully applied to CYGNSS L1B calibration and found to significantly reduce the PRN dependence of CYGNSS L1 and L2 data products. The gain pattern of each GPS satellite’s transmit antenna for the L1 C/A signal is determined from measurements of signal strength received by the 8-satellite CYGNSS constellation. Determination of GPS patterns requires knowledge of CYGNSS patterns and vice versa, so a procedure is developed to solve for both of them iteratively. The new GPS and CYGNSS patterns have been incorporated into the science data processing algorithm used by the CYGNSS mission and result in improved calibration performance. Variable transmit power by numerous Block IIF and IIR-M GPS space vehicles has been observed due to their flex power mode. Non-uniformity in the GPS antenna gain patterns further complicates EIRP estimation. A dynamic calibration approach is developed to further address GPS EIRP variability. It uses measurements by the direct received GPS signal to estimate GPS EIRP in the specular reflected direction and then incorporates them into the calibration of NBRCS. Dynamic EIRP calibration instantaneously detects and corrects for power fluctuations in the GPS transmitters and significantly reduces errors due to GPS antenna gain azimuthal asymmetry. It allows observations with the most variable Block IIF transmitters (approximately 37% of the GPS constellation) to be included in the standard data products and further improves the calibration quality of the NBRCS. A physics-based approach is then proposed to examine potential calibration errors and to further improve the Level 1 calibration. The mean square slope (mss) is a key physical parameter that relates the ocean surface properties (wave spectra) to the CYGNSS measurement of NBRCS. An approach to model the mss for validation with CYGNSS mss data is developed by adding the contribution of a high frequency tail to the WAVEWATCH III (WW3) mss. It is demonstrated that the ratio of CYGNSS mss to modified WW3 mss can be used to diagnose potential calibration errors that exist in the Level 1 calibration algorithm. This approach can help to improve CYGNSS data quality, including the Level 1 NBRCS and Level 2 ocean surface wind speed and roughness. The engineering calibration methods presented in this dissertation make significant contributions to the spatial coverage, calibration quality of the measured NBRCS and the geophysical data products produced by the NASA CYGNSS mission. The research is also useful to the system design, science investigation and engineering calibration of future GNSS-reflectometry missions.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168052/1/wangtl_1.pd

    Earth Observations for Addressing Global Challenges

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    "Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

    Monitoring freeze-thaw state by means of GNSS reflectometry. An analysis of TechDemoSat-1 data

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    The article of the freeze/thaw dynamic of high-latitude Earth surfaces is extremely important and informative for monitoring the carbon cycle, the climate change, and the security of infrastructures. Current methodologies mainly rely on the use of active and passive microwave sensors, while very few efforts have been devoted to the assessment of the potential of observations based on signals of opportunity. This article aims at assessing the performance of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) for high-spatial and highoral resolution monitoring of the Earth-surface freeze/thaw state. To this aim, reflectivity values derived from the TechDemoSat-1 (TDS-1) data have been collected and elaborated, and thus compared against the soil moisture active passive (SMAP) freeze/thaw information. Shallow subsurface soil temperature values recorded by a network of in situ stations have been considered as well. Even if an extensive and timeliness cross availability of both types of experimental data is limited by the spatial coverage and density of TDS-1 observations, the proposed analysis clearly indicates a significant seasonal cycle in the calibrated reflectivity. This opens new perspectives for the bistatic L-band high-resolution satellite monitoring of the freeze/thaw state, as well as to support the development of next-generation of GNSS-R satellite missions designed to provide enhanced performance and improved temporal and spatial coverage over high latitude areas

    Advanced GNSS-R instruments for altimetric and scatterometric applications

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    This work is the result of more than eight years during a bachelor thesis, a master thesis, and the Ph.D. thesis dedicated to the development of the Microwave Interferometric Reflectometer (MIR) instrument. It summarizes all the knowledge acquired during this time, and describes the MIR instrument as detailed as possible. MIR is a Global Navigation Satellite System - Reflectometer (GNSS-R), that is, an instrument that uses Global Navigation Satellite System (GNSS) signals scattered on the Earth's surface to retrieve geophysical parameters. These signals are received below the noise level, but since they have been spread in the frequency domain using spread-spectrum techniques, and in particular using the so-called Pseudo Random Noise (PRN) codes, it is still possible to retrieve them because of the large correlation gain achieved. In GNSS-R, two main techniques are used for this purpose: the conventional technique cGNSS-R and the interferometric one iGNSS-R, each with its pros and cons. In the former technique, the reflected signal is cross-correlated against a locally generated clean-replica of the transmitted signal. In the latter technique the reflected signal is cross-correlated with the direct one. Nowadays multiple GNSS systems coexist, transmitting narrow and wide, open and private signals. A comparison between systems, signals, and techniques in fair conditions is necessary. The MIR instrument has been designed as an airborne instrument for that purpose: the instrument has two arrays, an up-looking one, and a down-looking one, each with 19 dual-band antennas in a hexagonal distribution. The instrument is able to form 2 beams at each frequency band (L1/E1, and L5/E5A), which are pointing continuously to the desired satellites taking into account their position, as well as the instrument's position and attitude. The data is sampled and stored for later post-processing. Last but not least, MIR is auto-calibrated using similar signals to the ones transmitted by the GNSS satellites. During the instrument development, the Distance Measurement Equipment/TACtical Air Navigation (DME/TACAN) signals from the Barcelona airport threatened to disrupt the interferometric technique. These signals were also studied, and it was concluded that the use of a mitigation systems were as strongly recommended. The interferometric technique was also affected by the unwanted contribution of other satellites. The impact of these contributions was studied using real data gathered during this Ph.D. thesis. During these 8 years, the instrument was designed, built, tested, and calibrated. A field campaign was carried out in Australia between May 2018 and June 2018 to determine the instrument's accuracy in sensing soil moisture and sea altimetry. This work describes each of these steps in detail and aims to be helpful for those who decide to continue the legacy of this instrument.Este trabajo es el resultado de más de 8 años de doctorado dedicados al desarrollo del instrumento Microwave Interferometric Reflectometer (MIR). Esta tesis resume todo el conocimiento adquirido durante este tiempo, y describe el MIR lo más detalladamente posible. El MIR es un Reflectómetro de señales de Sistemas Globales de Navegación por Satélite (GNSS-R), es decir, es un instrumento que usa señales de GNSS reflejadas en la superficie de la tierra para obtener parámetros geofísicos. Estas señales son recibidas bajo el nivel de ruido, pero dado que han sido ensanchadas en el dominio frecuencial usando técnicas de espectro ensanchado, y en particular usando códigos Pseudo Random Noise (PRN), es todavía posible recibirlas debido a la elevada ganancia de correlación. En GNSS-R existen dos técnicas para este propósito: la convencional (cGNSS-R), y la interferométrica (iGNSS-R), cada una con sus pros y sus contras. En la primera se calcula la correlación cruzada de la señal reflejada y de una réplica generada del código transmitido. En la segunda técnica se calcula la correlación cruzada de la señal reflejada y de la señal directa. Hoy en día muchos sistemas GNSS coexisten, transmitiendo señales de distintos anchos de banda, algunas públicas y otras privadas. Una comparación entre sistemas, señales, y técnicas en condiciones justas es necesaria. El MIR es un instrumento aerotransportado diseñado como para ese propósito: el instrumento tiene dos arrays de antenas, uno apuntando al cielo, y otro apuntando al suelo, cada uno con 19 antenas doble banda en una distribución hexagonal. El instrumento puede formar 2 haces en cada banda frecuencial (L1/E1 y L5/E5A) que apuntan continuamente a los satélites deseados teniendo en cuenta su posición, y la posición y actitud del instrumento. Los datos son guardados para ser procesados posteriormente. Por último pero no menos importante, el MIR se calibra usando señales similares a las transmitidas por los satélites de GNSS. Durante el desarrollo del instrumento, señales del sistema Distance Measuremt Equi Distance Measurement Equipment/TACtical Air Navigation (DME/TACAN) del aeropuerto de Barcelona mostraron ser una amenaza para la técnica interferométrica. Estas señales fueron estudiadas y se concluyó que era encarecidamente recomendado el uso de sistemas de mitigación de interferencias. La técnica interferométrica también se ve afectada por las contribuciones no deseadas de otros satélites, llamado cross-talk. El impacto del cross-talk fue estudiado usando datos reales tomados durante esta tesis doctoral. A lo largo de estos 8 años el instrumento ha sido diseñado, construido, testeado y calibrado. Una campaña de medidas fue llevada a cabo en Australia entre Mayo de 2018 y Junio de 2018 para determinar la capacidad del instrumento para estimar la humedad del terreno y la altura del mar. Este documento describe cada uno de estos pasos al detalle y espera resultar útil para aquellos que decidan continuar con el legado de este instrumento.Postprint (published version

    Remote Sensing of Forest Biomass Using GNSS Reflectometry

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    In this study, the capability of Global Navigation Satellite System Reflectometry in evaluating forest biomass from space has been investigated by using data coming from the TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and from the Cyclone Satellite System (CyGNSS) mission of NASA. The analysis has been first conducted using TDS-1 data on a local scale, by selecting five test areas located in different parts of the Earth's surface. The areas were chosen as examples of various forest coverages, including equatorial and boreal forests. Then, the analysis has been extended by using CyGNSS to a global scale, including any type of forest coverage. The peak of the Delay Doppler Map calibrated to retrieve an "equivalent" reflectivity has been exploited for this investigation and its sensitivity to forest parameters has been evaluated by a direct comparison with vegetation optical depth (VOD) derived from the Soil Moisture Active Passive L-band radiometer, with a pantropical aboveground biomass (AGB) map and then with a tree height (H) global map derived from the Geoscience Laser Altimeter System installed on-board the ICEsat satellite. The sensitivity analysis confirmed the decreasing trend of the observed equivalent reflectivity for increasing biomass, with correlation coefficients 0.31 ≤ R ≤ 0.54 depending on the target parameter (VOD, AGB, or H) and on the considered dataset (local or global). These correlations were not sufficient to retrieve the target parameters by simple inversion of the direct relationships. The retrieval has been therefore based on Artificial Neural Networks making it possible to add other inputs (e.g., the incidence angle, the signal to noise ratio, and the lat/lon information in case of global maps) to the algorithm. Although not directly correlated to the biomass, these inputs helped in improving the retrieval accuracy. The algorithm was tested on both the selected areas and globally, showing a promising ability to retrieve the target parameter, either AGB or H, with correlation coefficients R ≃ 0.8

    Investigating the Sensitivity of Spaceborne GNSS-R Measurements to Ocean Surface Winds and Rain

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    Earth remote sensing using reflected Global Navigation Satellite System (GNSS) signals is an emerging trend, especially for ocean surface wind measurements. GNSS-Reflectometry (GNSS-R) measurements of ocean surface scattering cross section are directly related to the surface roughness at scale sizes ranging from small capillary waves to long gravity waves. These roughness scales are predominantly due to swell, surface winds and other meteorological phenomena such as rain. In this study we are interested in understanding and characterizing the impact of these phenomena on GNSS-R signals in order to develop a better understanding of the geophysical parameters retrieved from these measurements. In the first part of this work, we look at GNSS-R measurements made by the NASA Cyclone Global Navigation Satellite System (CYGNSS) for developing an effective wind retrieval model function for GNSS-R measurements. In a fully developed sea state, the wind field has a constant speed and direction. In this case, a single Fully Developed Seas (FDS) Geophysical Model Function (GMF) is constructed which relates the scattering cross-section to the near surface wind speed. However, the sea age and fetch length conditions inside a hurricane are in general not consistent with a fully developed sea state. Therefore, a separate empirical Young Sea Limited Fetch (YSLF) GMF is developed to represent the conditions inside a hurricane. Also, the degree of under development of the seas is not constant inside hurricanes and conditions vary significantly with azimuthal location within the hurricane due to changes in the relative alignment of the storms forward motion and its cyclonic rotation. The azimuthal dependence of the scattering cross-section is modelled and a modified azimuthal YSLF GMF is constructed using measurements by CYGNSS over 19 hurricanes in 2017 and 2018. Next, we study the impact of rain on CYGNSS measurements. At L-band rain has a negligible impact on the transmitted signal in terms of path attenuation. However, there are other effects due to rain, such as changes in surface roughness and rain induced local winds, which can significantly alter the measurements. In this part of the study we propose a 3-fold rain model for GNSS-R signals which accounts for: 1) attenuation; 2) surface effects of rain; and 3) rain induced local winds. The attenuation model suggests a total of 96% or greater transmissivity at L-Band up to 30mm/hr of rain. A perturbation model is used to characterize the other two rain effects. It suggests that rain is accompanied by an overall reduction in the scattering cross-section of the ocean surface and, most importantly, this effect is observed only up to 15 m/s of surface winds, beyond which the gravity capillary waves dominate the scattering in the quasi-specular direction. This work binds together several rain-related phenomena and enhances our overall understanding of rain effects on GNSS-R measurements. Finally, one of the important objectives for the CYGNSS mission is to provide high quality global scale GNSS-R measurements that can reliably be used for ocean science applications. In this part of the work we develop a Neural Network based quality control filter for automated outlier detection for CYGNSS retrieved winds. The primary merit of the proposed Machine Learning (ML) filter is its ability to better account for interactions between the individual engineering, instrument and measurement conditions than can separate threshold quality flags for each one.PHDClimate and Space Sciences and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166140/1/rajibala_1.pd
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