Remote Sensing Observations of Tundra Snow with Ku- and X-band Radar

Seasonal patterns of snow accumulation in the Northern Hemisphere are changing in response to variations in Arctic climate. These changes have the potential to influence global climate, regional hydrology, and sensitive ecosystems as they become more pronounced. To refine our understanding of the role of snow in the Earth system, improved methods to characterize global changes in snow extent and mass are needed. Current space-borne observations and ground-based measurement networks lack the spatial resolution to characterize changes in volumetric snow properties at the scale of ground observed variation. Recently, radar has emerged as a potential complement to existing observation methods with demonstrated sensitivity to snow volume at high spatial resolutions (< 200 m). In 2009, this potential was recognized by the proposed European Space Agency Earth Explorer mission, the Cold Regions High Resolution Hydrology Observatory (CoReH2O); a satellite based dual frequency (17.2 and 9.6 GHz) radar for observation of cryospheric variables including snow water equivalent (SWE). Despite increasing international attention, snow-radar interactions specific to many snow cover types remain unevaluated at 17.2 or 9.6 GHz, including those common to the Canadian tundra. This thesis aimed to use field-based experimentation to close gaps in knowledge regarding snow-microwave interaction and to improve our understanding of how these interactions could be exploited to retrieve snow properties in tundra environments. Between September 2009 and March 2011, a pair of multi-objective field campaigns were conducted in Churchill, Manitoba, Canada to collect snow, ice, and radar measurements in a number of unique sub-arctic environments. Three distinct experiments were undertaken to characterize and evaluate snow-radar response using novel seasonal, spatial, and destructive sampling methods in previously untested terrestrial tundra environments. Common to each experiment was the deployment of a sled-mounted dual-frequency (17.2 and 9.6 GHz) scatterometer system known as UW-Scat. This adaptable ground-based radar system was used to collect backscatter measurements across a range of representative tundra snow conditions at remote terrestrial sites. The assembled set of measurements provide an extensive database from which to evaluate the influence of seasonal processes of snow accumulation and metamorphosis on radar response. Several advancements to our understanding of snow-radar interaction were made in this thesis. First, proof-of-concept experiments were used to establish seasonal and spatial observation protocols for ground-based evaluation. These initial experiments identified the presence of frequency dependent sensitivity to evolving snow properties in terrestrial environments. Expanding upon the preliminary experiments, a seasonal observation protocol was used to demonstrate for the first time Ku-band and X-band sensitivity to evolving snow properties at a coastal tundra observation site. Over a 5 month period, 13 discrete scatterometer observations were collected at an undisturbed snow target where Ku-band measurements were shown to hold strong sensitivity to increasing snow depth and water equivalent. Analysis of longer wavelength X-band measurements was complicated by soil response not easily separable from the target snow signal. Definitive evidence of snow volume scattering was shown by removing the snowpack from the field of view which resulted in a significant reduction in backscatter at both frequencies. An additional set of distributed snow covered tundra targets were evaluated to increase knowledge of spatiotemporal Ku-band interactions. In this experiment strong sensitivities to increasing depth and SWE were again demonstrated. To further evaluate the influence of tundra snow variability, detailed characterization of snow stratigraphy was completed within the sensor field of view and compared against collocated backscatter response. These experiments demonstrated Ku-band sensitivity to changes in tundra snow properties observed over short distances. A contrasting homogeneous snowpack showed a reduction in variation of the radar signal in comparison to a highly variable open tundra site. Overall, the results of this thesis support the single frequency Ku-band (17.2 GHz) retrieval of shallow tundra snow properties and encourage further study of X-band interactions to aid in decomposition of the desired snow volume signal.4 month

Developing Parameter Constraints for Radar-based SWE Retrievals

Terrestrial snow is an important freshwater reservoir with significant influence on the climate and energy balance. It exhibits natural spatiotemporal variability which has been enhanced by climate change, thus it is important to monitor on a large scale. Active microwave, or radar remote sensing has shown frequency-dependent promise in this regard, however, interpretation remains a challenge. The aim of this thesis was to develop constraints for radar based SWE retrievals which characterize and limit uncertainty with a focus on the underlying physical processes, snowpack stratigraphy, the influence of vegetation, and effects of background scattering. The University of Waterloo Scatterometer (UWScat) was used to make measurements at 9.6 and 17.2 GHz of snow and bare ground in a series of field-based campaigns in Maryhill and Englehart, ON, Grand Mesa, CO (NASA SnowEx campaign, year 1), and Trail Valley Creek, NT. Additional measurements from Tobermory, ON, and Churchill, MB (Canadian Snow and Ice Experiment) were included. The Microwave Emission Model for Layered Snowpacks, Version 3, adapted for backscattering (MEMLS3&a) was used to explore snowpack parameterization and SWE retrieval and the Freeman-Durden three component decomposition (FD3c) was used to leverage the polarimetric response. Physical processes in the snow accumulation environment demonstrated influence on regional snowpack parameterization and constraints in a SWE retrieval context with a single-layer snowpack parameterization for Maryhill, ON and a two-layer snowpack parameterization for Englehart, ON resulting in a retrieval RMSE of 21.9 mm SWE and 24.6 mm SWE, respectively. Use of in situ snow depths improved RMSE to 12.0 mm SWE and 10.9 mm SWE, while accounting for soil scattering effects further improved RMSE by up to 6.3 mm SWE. At sites with vegetation and ice lenses, RMSE improved from 60.4 mm SWE to 21.1 mm SWE when in situ snow depths were used. These results compare favorably with the common accuracy requirement of RMSE ≤ 30 mm and underscore the importance of understanding the driving physical processes in a snow accumulation environment and the utility of their regional manifestation in a SWE retrieval context. A relationship between wind slab thickness and the double-bounce component of the FD3c in a tundra snowpack was introduced for incidence angles ≥ 46° and wind slab thickness ≥ 19 cm. Estimates of wind slab thickness and SWE resulted in an RMSE of 6.0 cm and 5.5 mm, respectively. The increased double-bounce scattering was associated with path length increase within a growing wind slab layer. Signal attenuation in a sub-canopy SWE retrieval was also explored. The volume scattering component of the FD3c yielded similar performance to forest fraction in the retrieval with several distinct advantages including a real-time description of forest condition, accounting for canopy geometry without ancillary information, and providing coincident information on forest canopy in remote locations. Overall, this work demonstrated how physical processes can manifest regional outcomes, it quantified effects of natural inclusions and background scattering on SWE retrievals, it provided a means to constrain wind slab thickness in a tundra environment, and it improved characterization of coniferous forest in a sub-canopy SWE retrieval context. Future work should focus on identifying ice and vegetation conditions prior to SWE retrieval, testing the spatiotemporal validity of the methods developed herein, and finally, improving the integration of snowpack attenuation within retrieval efforts

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Effect of snow microstructure and subnivean water bodies on microwave radiometry of seasonal snow

Remote sensing using microwave radiometry is an acknowledged method for monitoring various environmental processes in the cryosphere, atmosphere, soil, vegetation and oceans. Several decades long time series of spaceborne passive microwave observations can be used to detect trends relating to climate change, while present measurements provide information on the current state of the environment. Unlike optical wavelengths, microwaves are mostly insensitive to atmospheric and lighting conditions and are therefore suitable for monitoring seasonal snow in the Arctic. One of the major challenges in the utilization of spaceborne passive microwave observations for snow measurements is the poor spatial resolution of instruments. The interpretation of measurements over heterogeneous areas requires sophisticated microwave emission models relating the measured parameters to physical properties of snow, vegetation and the subnivean layer. Especially the high contrast in the electrical properties of soil and liquid water introduces inaccuracies in the retrieved parameters close to coastlines, lakes and wetlands, if the subnivean water bodies are not accounted for in the algorithm. The first focus point of this thesis is the modelling of brightness temperature of ice- and snow-covered water bodies and their differences from snow-covered forested and open land areas. Methods for modelling the microwave signatures of water bodies and for using that information in the retrieval of snow parameters from passive microwave measurements are presented in this thesis. The second focus point is the effect of snow microstructure on its microwave signature. Even small changes in the size of scattering particles, snow grains, modify the measured brightness temperature notably. The coupling of different modelled and measured snow microstructural parameters with a microwave snow emission model and the application of those parameters in the retrieval of snow parameters from remote sensing data are studied

Skylab EREP Investigations Summary

The problems in the areas of agriculture, range and forestry; land use and cartography; geology and hydrology; oceans atmosphere, and data analysis techniques were investigated and summarized using Earth Resources Experiment Package (EREP) data

TERSSE: Definition of the Total Earth Resources System for the Shuttle Era. Volume 2: An Assessment of the Current State-of-the-Art

Results of a state-of-the-art assessment of technology areas which affect the Earth Resources Program are presented along with a functional description of the basic earth resources system. Major areas discussed include: spacecraft flight hardware, remote sensors, data processing techniques and hardware, user models, user interfaces, and operations technology

Goddard Laboratory for Atmospheric Sciences, collected reprints 1978 - 1979, volume 2

Information about the Earth hydrosphere, obtained in the field and from aircraft and satellite imagery is reported. Particular emphasis is given to the use of microwave sensors in the study of soil moisture, sea ice, snow cover and atmospheric parameters associated with watersheds

High Resolution Remote Sensing Observations of Summer Sea Ice

During the Arctic summer melt season, the sea ice transitions from a consolidated ice pack with a highly reflective snow-covered surface to a disintegrating unconsolidated pack with melt ponds spotting the ice surface. The albedo of the Arctic decreases by up to 50%, resulting in increased absorption of solar radiation, triggering the positive sea ice albedo feedback that further enhances melting. Summer melt processes occur at a small scale and are required for melt pond parameterization in models and quantifying albedo change. Arctic-wide observations of melt features were however not available until recently. In this work we develop original techniques for the analysis of high-resolution remote sensing observations of summer sea ice. By applying novel algorithms to data acquired from airborne and satellite sensors onboard IceBridge, Sentinel-2, WorldView and ICESat-2, we derive a set of parameters that describe melt conditions on Arctic sea ice in summer. We present a new, pixel-based classification scheme to identify melt features in high-resolution summer imagery. We apply the classification algorithm to IceBridge Digital Mapping System data and find a greater melt pond fraction (25%) on sea ice in the Beaufort and Chukchi Seas, a region consisting of predominantly first year ice, compared to the Central Arctic, where the melt pond fraction is 14% on predominantly multiyear ice. Expanding the study to observations acquired by the Sentinel-2 Multispectral Instrument, we track the variability in melt pond fraction and sea ice concentration with time, focusing on the anomalously warm summer of 2020. So as to obtain a three-dimensional view of the evolution of summer melt we also exploit ICESat-2 surface elevation measurements. We develop and apply the Melt Pond Algorithm to track ponds in ICESat-2 photon cloud data and derive their depth. Pond depth measurements in conjunction with melt pond fraction and sea ice concentration provide insights into the regional patterns and temporal evolution of melt on summer sea ice. We found mean melt pond fraction increased rapidly in the beginning of the melt season, peaking at 16% on 24 June 2020, while median pond depths increased steadily from 0.4 m at the beginning of the melt season, to peaking at 0.97 m on 16 July, even as melt pond fraction had begun to decrease. Our findings may be used to improve parameterization of melt processes in models, quantify freshwater storage, and study the partitioning of under ice light

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale