Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air–snow (a–s) and snow–ice (s–i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice

Optimization of the Geometry of Communication for Autonomous Missions of Underwater Vehicles

The potential of Autonomous Underwater Vehicles (AUVs) working as a team in sampling, monitoring and surveillance of the marine environment has been realized since quite a long time. One of the most relevant obstacle to their operational implementation resides in the limitations of the acoustic channel for inter-vehicle communications. Underwater acoustic modeling and simulation plays an important role in predicting possible losses and transmission failures between them, and underwater sound propagation can be precisely measured or estimated. In this thesis, sound speed data from a real experiment (CommsNet13) were used to simulate environmental conditions and analyze acoustic communication between an USBL-vehicle on the sea surface and an acoustic modem on the sea bottom, in order achieve an effective geometry of transmission for future trials

Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S and C band airborne ultra-wideband FMCW (frequency-modulated continuous wave) radar

During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S and C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. This paper reports seasonal snow depths derived from radar data. We found large variations in seasonal radar-inferred depths with multi-modal distributions assuming a constant relative permittivity for snow equal to 1.89. About 34 % of the snow depths observed in 2018 were between 3.2 and 4.2 m, and close to 30 % of the snow depths observed in 2021 were between 2.5 and 3.5 m. We observed snow strata in ice facies, combined percolation and wet-snow facies, and dry-snow facies from radar data and identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell (K'elt'aeni) to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ∼2.89 m w.e. a−1 between the years 2003 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w.e. a−1, which matches closely to the 2.75 m w.e. a−1 inferred from independent ground-truth measurements made the same year. The snow accumulation rate between the years 2003 and 2021 also showed a linear increasing trend of 0.011 m w.e. a−2. This trend is corroborated by comparisons with the surface mass balance (SMB) derived for the same period from the regional atmospheric climate model MAR (Modèle Atmosphérique Régional). According to MAR data, which show an increase of 0.86 ∘C in this area for the period of 2003–2021, the linear upward trend is associated with the increase in snowfall and rainfall events, which may be attributed to elevated global temperatures. The findings of this study confirmed the viability of our methodology, as well as its underlying assumptions and interpretation models.</p

Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications

On the correlation between GNSS-R reflectivity and L-band microwave radiometry

This work compares microwave radiometry and global navigation satellite systems-reflectometry (GNSS-R) observations using data gathered from airborne flights conducted for three different soil moisture conditions. Two different regions are analyzed, a crops region and a grassland region. For the crops region, the correlation with the I/2 (first Stokes parameter divided by two) was between 0.74 and 0.8 for large incidence angle reflectivity data (30°-50°), while it was between 0.51 and 0.61 for the grassland region and the same incidence angle conditions. For the crops region, the correlation with the I/2 was between 0.64 and 0.69 for lower incidence angle reflectivity data (<;30°), while it was between 0.41 and 0.6 for the grassland region. This indicates that for large incidence angles the coherent scattering mechanism is dominant, while the lower incidence angles are more affected by incoherent scattering. Also a relationship between the reflectivity and the polarization index (PI) is observed. The PI has been used to remove surface roughness effects, but due to its dependence on the incidence angle only the large incidence angle observations were useful. The difference in ground resolution between microwave radiometry and GNSS-R and their strong correlation suggests that they might be combined to improve the spatial resolution of microwave radiometry measurements in terms of brightness temperature and consequently soil moisture retrievals.This work was supported in part by the Spanish Ministry of Science and Innovation, “AROSA-Advanced Radio Ocultations and Scatterometry Applications using GNSS and other opportunity signals,” under Grant AYA2011-29183-C02-01/ESP and “AGORA: Tecnicas Avanzadas en Teledetección Aplicada Usando Señales GNSS y Otras Señales de Oportunidad,” under Grant ESP2015-70014-C2-1-R (MINECO/FEDER), in part by the Monash University Faculty of Engineering 2013 Seed Grant, and in part by the Advanced Remote Sensing Ground-Truth Demo and Test Facilities and Terrestrial Environmental Observatories funded by the German Helmholtz-Association. The work of A. A.-Arroyo was supported by the Fulbright Commission in Spain through a Fulbright grant.Peer ReviewedPostprint (author's final draft

Doctor of Philosophy

dissertationPeriodic temperature measurements in the DOI/GTN-P Deep Borehole Array on the western Arctic Slope of Alaska have shown a strong near-surface permafrost warming over the last 40 years, particularly since ∼ 1990. Due to the manner in which these deep wells were drilled, the portion of the observed permafrost warming caused by climate change has remained unclear. Other factors that have strongly influenced temperatures near the wellbores include the heat deposited into permafrost during drilling and local-landscape changes associated with drilling operations (creation of reserve pits and drill pads). Multidimensional heat-transfer models capable of assessing the magnitude of the drilling and local-landscape disturbances near the wellbores have not been available. For the western Arctic Slope, such models must be capable of simulating heat-transfer processes in layered fine-grained mudrocks whose thermal properties are highly nonlinear due to the occurrence of unfrozen water at temperatures well below 0°C. An assessment of the drilling and landscape-change effects also requires knowledge of the specific thermophysical properties occurring at the well sites. Little information has been available about these properties on the western Arctic Slope. To establish the portion of the observed permafrost warming related to drilling and landscape-change effects, multidimensional (2-D cylindrical, 3-D cartesian) numerical heat-transfer models were created that simulate heat flow in layered heterogenous materials surrounding a wellbore, phase changes, and the unfrozen water properties of a wide range of fine-grained sediments. Using these models in conjunction with the borehole temperature measurements, the mean thermophysical properties of permafrost rock units on the western Arctic Slope were determined using an optimization process. Incorporation of local meteorological information into the optimization allows a more refined estimate of the thermal properties to be determined at a well site. Applying this methodology to the East Simpson #1 well on the Beaufort Sea coast (70°55.046'N, 154°37.286'W), the freezing point of permafrost is found to be -1.05°C at this site and thermal diffusivities range 0.22-0.40 × 10 -6 m2 s-1. Accounting for the drilling and landscape-change effects, tundra adjacent to East Simpson is found to have warmed 5.1 K since the mid-1880s. Of this, 3.1 K (60%) of the warming has occurred since 1970

GNSS reflectometry for land remote sensing applications

Soil moisture and vegetation biomass are two essential parameters from a scienti c and economical point of view. On one hand, they are key for the understanding of the hydrological and carbon cycle. On the other hand, soil moisture is essential for agricultural applications and water management, and vegetation biomass is crucial for regional development programs. Several remote sensing techniques have been used to measure these two parameters. However, retrieving soil moisture and vegetation biomass with the required accuracy, and the appropriate spatial and temporal resolutions still remains a major challenge. The use of Global Navigation Satellite Systems (GNSS) reflected signals as sources of opportunity for measuring soil moisture and vegetation biomass is assessed in this PhD Thesis. This technique, commonly known as GNSS-Reflectometry (GNSS-R), has gained increasing interest among the scienti c community during the last two decades due to its unique characteristics. Previous experimental works have already shown the capabilities of GNSS-R to sense small reflectivity changes on the surface. The use of the co- and cross-polarized reflected signals was also proposed to mitigate nuisance parameters, such as soil surface roughness, in the determination of soil moisture. However, experimental evidence of the suitability of that technique could not be demonstrated. This work analyses from a theoretical and an experimental point of view the capabilities of polarimetric observations of GNSS reflected signals for monitoring soil moisture and vegetation biomass. The Thesis is structured in four main parts. The fi rst part examines the fundamental aspects of the technique and provides a detailed review of the GNSS-R state of the art for soil moisture and vegetation monitoring. The second part deals with the scattering models from land surfaces. A comprehensive description of the formation of scattered signals from rough surfaces is provided. Simulations with current state of the art models for bare and vegetated soils were performed in order to analyze the scattering components of GNSS reflected signals. A simpli ed scattering model was also developed in order to relate in a straightforward way experimental measurements to soil bio-geophysical parameters. The third part reviews the experimental work performed within this research. The development of a GNSS-R instrument for land applications is described, together with the three experimental campaigns carried out in the frame of this PhD Thesis. The analysis of the GNSS-R and ground truth data is also discussed within this part. As predicted by models, it was observed that GNSS scattered signals from natural surfaces are a combination of a coherent and an incoherent scattering components. A data analysis technique was proposed to separate both scattering contributions. The use of polarimetric observations for the determination of soil moisture was demonstrated to be useful under most soil conditions. It was also observed that forests with high levels of biomass could be observed with GNSS reflected signals. The fourth and last part of the Thesis provides an analysis of the technology perspectives. A GNSS-R End-to-End simulator was used to determine the capabilities of the technique to observe di erent soil reflectivity conditions from a low Earth orbiting satellite. It was determined that high accuracy in the estimation of reflectivity could be achieved within reasonable on-ground resolution, as the coherent scattering component is expected to be the predominant one in a spaceborne scenario. The results obtained in this PhD Thesis show the promising potential of GNSS-R measurements for land remote sensing applications, which could represent an excellent complementary observation for a wide range of Earth Observation missions such as SMOS, SMAP, and the recently approved ESA Earth Explorer Mission Biomass.La humedad del suelo y la biomasa de la vegetaci on son dos parametros clave desde un punto de vista tanto cient co como econ omico. Por una parte son esenciales para el estudio del ciclo del agua y del carbono. Por otra parte, la humedad del suelo es esencial para la gesti on de las cosechas y los recursos h dricos, mientras que la biomasa es un par ametro fundamental para ciertos programas de desarrollo. Varias formas de teledetección se han utilizado para la observaci on remota de estos par ametros, sin embargo, su monitorizaci on con la precisi on y resoluci on necesarias es todav a un importante reto tecnol ogico. Esta Tesis evalua la capacidad de medir humedad del suelo y biomasa de la vegetaci on con señales de Sistemas Satelitales de Posicionamiento Global (GNSS, en sus siglas en ingl es) reflejadas sobre la Tierra. La t ecnica se conoce como Reflectometr í a GNSS (GNSS-R), la cual ha ganado un creciente inter es dentro de la comunidad científ ca durante las dos ultimas d ecadas. Experimentos previos a este trabajo ya demostraron la capacidad de observar cambios en la reflectividad del terreno con GNSS-R. El uso de la componente copolar y contrapolar de la señal reflejada fue propuesto para independizar la medida de humedad del suelo de otros par ametros como la rugosidad del terreno. Sin embargo, no se pudo demostrar una evidencia experimental de la viabilidad de la t ecnica. En este trabajo se analiza desde un punto de vista te orico y experimental el uso de la informaci on polarim etrica de la señales GNSS reflejadas sobre el suelo para la determinaci on de humedad y biomasa de la vegetaci on. La Tesis se estructura en cuatro partes principales. En la primera parte se eval uan los aspectos fundamentales de la t ecnica y se da una revisi on detallada del estado del arte para la observaci on de humedad y vegetaci on. En la segunda parte se discuten los modelos de dispersi on electromagn etica sobre el suelo. Simulaciones con estos modelos fueron realizadas para analizar las componentes coherente e incoherente de la dispersi on de la señal reflejada sobre distintos tipos de terreno. Durante este trabajo se desarroll o un modelo de reflexi on simpli cado para poder relacionar de forma directa las observaciones con los par ametros geof sicos del suelo. La tercera parte describe las campañas experimentales realizadas durante este trabajo y discute el an alisis y la comparaci on de los datos GNSS-R con las mediciones in-situ. Como se predice por los modelos, se comprob o experimentalmente que la señal reflejada est a formada por una componente coherente y otra incoherente. Una t ecnica de an alisis de datos se propuso para la separacióon de estas dos contribuciones. Con los datos de las campañas experimentales se demonstr o el bene cio del uso de la informaci on polarim etrica en las señales GNSS reflejadas para la medici on de humedad del suelo, para la mayor a de las condiciones de rugosidad observadas. Tambi en se demostr o la capacidad de este tipo de observaciones para medir zonas boscosas densamente pobladas. La cuarta parte de la tesis analiza la capacidad de la t ecnica para observar cambios en la reflectividad del suelo desde un sat elite en orbita baja. Los resultados obtenidos muestran que la reflectividad del terreno podr a medirse con gran precisi on ya que la componente coherente del scattering ser a la predominante en ese tipo de escenarios. En este trabajo de doctorado se muestran la potencialidades de la t ecnica GNSS-R para observar remotamente par ametros del suelo tan importantes como la humedad del suelo y la biomasa de la vegetaci on. Este tipo de medidas pueden complementar un amplio rango de misiones de observaci on de la Tierra como SMOS, SMAP, y Biomass, esta ultima recientemente aprobada para la siguiente misi on Earth Explorer de la ESA

Permanent water and flash flood detection using global navigation satellite system reflectometry

In this thesis, research for inland water extent and flash floods remote sensing using Global Navigation Satellite System Reflectometry (GNSS-R) data of the Cyclone Global Navigation Satellite System (CYGNSS) is presented. Firstly, a high-resolution Machine Learning (ML) method for detecting inland water extent using the CYGNSS data is implemented via the Random Under-Sampling Boosted (RUSBoost) algorithm. The CYGNSS data of the year 2018 over the Congo and Amazon basins are gridded into 0.01ﾟ × 0.01ﾟ cells. The RUSBoost-based classifier is trained and tested with the CYGNSS data over the Congo basin. The Amazon basin data that is unknown to the classifier is then used for further evaluation. Using only three observables extracted from the CYGNSS Delay-Doppler Maps (DDMs), the proposed technique is able to detect 95.4% and 93.3% of the water bodies over the Congo and Amazon basins, respectively. The performance of the RUSBoost-based classifier is also compared with an image processing based inland water detection method. For the Congo and Amazon basins, the RUSBoost-based classifier has a 3.9% and 14.2% higher water detection accuracies, respectively. Secondly, a flash flood detection method using the CYGNSS data is investigated. Considering Hurricane Harvey and Hurricane Irma as two case studies, six different Data Preparation Approaches (DPAs) for flood detection based on the CYGNSS data and the RUSBoost classification algorithm are investigated in this thesis. Taking flood and land as two classes, flash flood detection is tackled as a binary classification problem. Eleven observables are extracted from the DDMs for feature selection. These observables, alongside two features from ancillary data, are considered in feature selection. All the combinations of these observables with and without ancillary data are fed into the classifier with 5-fold cross-validation one-by-one. Based on the test results, five observables with the ancillary data are selected as a suitable feature vector for flood detection here. Using the selected feature vector, six different DPAs are investigated and compared to find the best one for flash flood detection. Then, the performance of the proposed method is compared with that of a Support Vector Machine (SVM) based classifier. For Hurricane Harvey and Hurricane Irma, the selected method is able to detect 89.00% and 85.00% of flooded points, respectively, with a resolution of 500m × 500m, and the detection accuracy for non-flooded land points is 97.20% and 71.00%, respectively