Contributions to land, sea, and sea ice remote sensing using GNSS-reflectometry

This PhD thesis researches the use of passive remote sensing techniques using signals transmitted from the navigation satellites (GNSS) in order to retrieve different geophysical parameters. The thesis consists of two different parts without taking into account the introduction, the state of the art and the conclusions. The first part analyzes the Interference Pattern Technique (IPT), which was previously used in another PhD thesis, and proposes some key improvements. First, the addition of horizontal polarization to the existing vertical polarization is proposed. Then, the retrieval of soil moisture is studied using the horizontal polarization only and combining both polarizations to correct for the surface roughness effects. It is also demonstrated that the phase difference between the two interference patterns is directly related to soil moisture content. A field campaign was conducted in Australia to test empirically all the theoretical developments and algorithms. Secondly, the possibility of measuring Significant Wave Height (SWH) and Mean Sea Surface Level (MSSL) using the IPT is studied. A three month field campaign over coastal sea is devoted to that study. The SWH retrieval is a new estimation algorithm based on measuring the point where the interference pattern loses coherence. The MSSL retrieval is based on the estimation of the IPT oscillation frequency, testing different spectral estimators to improve the accuracy. Since the IPT is limited in coverage due to its static requirements, the research conducted in this thesis migrated to scatterometric GNSS-R techniques. The main goal that migration was to increase coverage of the different GNSS-R instruments. Therefore, the second part of this thesis analyzes the applicability of a scatterometric technique from different platforms: ground-based (mobile and fixed), airborne, and spaceborne. The ground-based still platforms have allowed to develop a soil moisture retrieval algorithm. The ground-based moving platforms have extended the validity of that algorithm. Airborne platforms have been used to study the reflected electric field statistics when the surface reflecting surface is varying (smooth or rough land, and sea). They have also been used to develop different algorithms to measure the coherent and incoherent scattered components depending on the data structure (real-data or complex data). Coherent reflectivity measured from airborne platforms has been compared to other techniques such microwave radiometry, which is highly used in the soil moisture retrieval from spaceborne sensors, and other sensors using optical, multispectral and thermal frequency bands. These relationships between microwave radiometry and GNSS-R measurements suggests the potential synergy of both techniques. A sea ice detection algorithm is also developed using scatterometric GNSS-R data from the UK TDS-1 mission. This algorithm is based on measuring the degree of coherence of the reflected waveform. Finally, a field campaign was conducted to study the effect of vegetation on the GNSS signals that pass through it in order to take into account and correct the effect of vegetation in the GNSS-R data and in the soil moisture retrieval algorithms.Aquesta tesi doctoral aprofundeix en el coneixement de les tècniques de teledetecció passives utilitzant senyals emesos pels satèl·lits de navegació (GNSS) amb l'objectiu de recuperar diferents paràmetres geofísics del terreny. La tesi conté dues parts ben diferenciades a banda de la introducció, estat de l'art i conclusions. La primera part analitza la tècnica coneguda com a patró d'interferències, utilitzada prèviament en una altra tesi doctoral, i proposa certes millores per la seva aplicabilitat. En primer lloc es decideix afegir polarització horitzontal a la ja existent polarització vertical, i s'estudia la recuperació d'humitat del sòl utilitzant només polarització horitzontal i combinant les dues polaritzacions per corregir els efectes de la rugositat del terreny. A continuació es demostra que la mesura de desfasament entre els dos patrons d'interferència està directament relacionada amb la humitat del terreny. Es va realitzar una campanya de mesures a Austràlia per provar empíricament tots els desenvolupaments teòrics i algorismes proposats. En segon lloc s'analitza l'aplicabilitat del patró d'interferències en la mesura de l'altura de les onades (SWH) i del nivell del mar (MSSL), tots dos de forma precisa. L'estimació de l'alçada de les onades és un procés totalment nou basat en mesurar el punt on el patró d'interferències perd la coherència. L'estimació del nivell del mar es basa en l'anàlisi espectral del patró d'interferències provant diferents estimadors espectrals. Atès que la tècnica del patró d'interferència està limitada en cobertura per les seves característiques estàtiques, la investigació duta a terme en aquesta tesi doctoral va migrar cap a tècniques GNSS-R escateromètriques. El principal objectiu a assolir va ser el d'augmentar la cobertura dels diferents instruments GNSS-R de mesura. En conseqüència, la segona part d'aquesta tesi analitza l'aplicabilitat d'aquestes tècniques des de diferents plataformes terrestres (mòbils i fixes), aerotransportades i satèl·lit. Les plataformes terrestres fixes han permès derivar algoritmes de recuperació d'humitat i les mòbils estendre la validació d'aquests. Les plataformes aerotransportades s'han utilitzat per mirar l'estadística del camp elèctric reflectit quan la superfície on es reflecteixen els senyals GNSS va variant (terra plana o terra rugosa, i mar). També han servit per desenvolupar diferents algorismes amb l'objectiu de determinar les components coherent i incoherent del senyal reflectit. De la mateixa manera, dades de reflectivitat coherent mesurades des d'aquestes plataformes han estat comparades amb altres tècniques de teledetecció passiva com la radiometria de microones, altament utilitzada en la mesura d'humitat de terreny, i altres sensors òptics, multi-espectrals, i tèrmics. Aquests resultats han permès suggerir la possible sinergia de dades d'ambdues tecnologies. Un algorisme per detectar la presència de gel sobre el mar també ha estat desenvolupat mitjançant l'ús de dades GNSS-R escateromètriques satel·litals de la missió UK TDS-1. Aquest algorisme es basa en mesurar el grau de coherència de la forma d'ona reflectida. Finalment, s'ha realitzat un estudi de l'efecte de la vegetació en els senyals GNSS que la travessen, per tal de poder corregir aquest efecte en els algoritmes de recuperació d'humitat del terreny

Remote sensing of water vapor over land using the advanced microwave sounding unit

Includes bibliographical references.Water vapor is a fundamentally important variable in the atmosphere for making accurate forecasts. Its global distribution is a challenge to determine and can change rapidly in both space and time. Several ground and space based methods are currently employed to determine its spatial and temporal variability. The microwave spectrum is very useful for remote sensing due to its ability to penetrate through clouds at most frequencies. Microwave satellite sensors have been used to retrieve atmospheric state parameters for several decades, however the retrievals of certain parameters have not been performed satisfactorily over land thus far. Retrievals rely on the ability to extract the atmospheric state from the upwelling radiation, most of which comes from emission from the surface. Knowing the surface emissivity to a high degree of accuracy is essential for calculating the land surface temperature, however it is also important because this emission must be removed in order to retrieve the atmospheric parameters desired. Land type, vegetation, snow, ice, rain, urbanization effects, and many other factors have an effect on the aggregate emission within each viewing scene and results in a strong sensitivity and variability of microwave emissivity on small scales. A physically based iterative optimal estimation retrieval has been implemented to retrieve atmospheric parameters from the Advanced Microwave Sounding Unit (AMSU). This retrieval is based on the method of Engelen and Stephens (1999). The retrieval uses a first guess of water vapor and temperature profiles (currently from radiosondes, but will soon be from GDAS), and uses a first guess of emissivity at each of five frequencies (from the MEM). The retrieval was run with a highly accurate first guess in order to detect bias, and the total precipitable water amounts were validated against a radiosonde match-up dataset. The match-up showed fair agreement between the radiosondes and the retrieval (within 20%), however a systematic bias was detected due mostly to coastline contamination. Data from the Global Positioning System (GPS) was also used to validate the total precipitable water, however the results showed less agreement than the radiosonde results (variations of ~20-35%). Most of this disagreement stemmed from geographical co-location differences. The analytical Jacobian was also examined to determine the sensitivities of all channels to the state vector parameters. This enables any retrieval user to pick a channel configuration that gives the desired sensitivities. Vertical profiles of water vapor sensitivities at four varying emissivities were investigated. Sensitivities of water vapor to emissivity were also examined at three distinct atmospheric pressure levels. The Jacobian determined that water vapor is able to be detected throughout a vertical column with adequate skill, although problematic areas occurred between 600 and 800 mb as the emissivity approached unity (e>0.99) for a wet atmospheric case. These results give confidence that AMSU can detect TPW over land for both weather forecasting and for climate studies. The current capabilities may be improved further once bias sources are dealt with satisfactorily.Research was supoprted in part by Cloud Sat at NASA-Goddard under Contract Agreement NAS5-99237, the DoD Center for Geosciences/Atmospheric Research at Colorado State University under the Cooperative Agreement DAAD19-02-2-0005 with the Army Research Lab, and by the Joint Center for Satellite Data Assimilation (JCSDA) Program via NOAA grant NA17RJ1228#15 under CIRA's Cooperative Agreement with NOAA

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Polar research from satellites

In the polar regions and climate change section, the topics of ocean/atmosphere heat transfer, trace gases, surface albedo, and response to climate warming are discussed. The satellite instruments section is divided into three parts. Part one is about basic principles and covers, choice of frequencies, algorithms, orbits, and remote sensing techniques. Part two is about passive sensors and covers microwave radiometers, medium-resolution visible and infrared sensors, advanced very high resolution radiometers, optical line scanners, earth radiation budget experiment, coastal zone color scanner, high-resolution imagers, and atmospheric sounding. Part three is about active sensors and covers synthetic aperture radar, radar altimeters, scatterometers, and lidar. There is also a next decade section that is followed by a summary and recommendations section

Information retrieval from spaceborne GNSS Reflectometry observations using physics- and learning-based techniques

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets

SATELLITE MICROWAVE MEASUREMENT OF LAND SURFACE PHENOLOGY: CLARIFYING VEGETATION PHENOLOGY RESPONSE TO CLIMATIC DRIVERS AND EXTREME EVENTS

The seasonality of terrestrial vegetation controls feedbacks to the climate system including land-atmosphere water, energy and carbon (CO2) exchanges with cascading effects on regional-to-global weather and circulation patterns. Proper characterization of vegetation phenology is necessary to understand and quantify changes in the earthÆs ecosystems and biogeochemical cycles and is a key component in tracking ecological species response to climate change. The response of both functional and structural vegetation phenology to climatic drivers on a global scale is still poorly understood however, which has hindered the development of robust vegetation phenology models. In this dissertation I use satellite microwave vegetation optical depth (VOD) in conjunction with an array of satellite measures, Global Positioning System (GPS) reflectometry, field observations and flux tower data to 1) clarify vegetation phenology response to water, temperature and solar irradiance constraints, 2) demonstrate the asynchrony between changes in vegetation water content and biomass and changes in greenness and leaf area in relation to land cover type and climate constraints, 3) provide enhanced assessment of seasonal recovery of vegetation biomass following wildfire and 4) present a method to more accurately model tropical vegetation phenology. This research will establish VOD as a useful and informative parameter for regional-to-global vegetation phenology modeling, more accurately define the drivers of both structural and functional vegetation phenology, and help minimize errors in phenology simulations within earth system models. This dissertation also includes the development of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) vegetation health climate indicators as part of a NASA funded project entitled Development and Testing of Potential Indicators for the National Climate Assessment; Translating EOS datasets into National Ecosystem Biophysical Indicators

Relationship between aquarius L-band active and passive multi-year observations over Australia

This PFC is focused in evaluating the feasibility of doing a combined changed algorithm to simplify the process of low resolution downscaling using high resolution.The aim of this Thesis is to further our understanding of the geophysical information that can be estimated from active and passive L-band sensors. All data was obtained from NASA's satellite Aquarius durin the period Sept. 2011- August 2014

Earth Observations for Addressing Global Challenges

"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