Field Measurements for Remote Sensing of the Cryosphere

Remote sensing observations of the cryosphere, like any other target of interest, require ground-based measurements for both calibration and validation, as inversion algorithms are usually underdetermined and uncertainties in the retrieval are needed for application. Field-based observations are performed in selected representative locations, and typically involve both direct in situ measurements of the physical properties of interest, as well as ground-based remote sensing techniques. New state-of-the-art modern techniques for measuring physical properties rapidly and at high spatial resolution have recently given us a new view of spatiotemporal variability. These are important, as large variability at scales below the typical footprint of spaceborne sensors often exists. Simulating remote sensing measurements using ground-based sensors provides the ability to perform both in situ and remote sensing measurements at the same scale, providing insight into the dominant physical processes that must be accounted for in inversion models and retrieval schemes. While direct in situ measurements provide the most accurate information about the properties of interest, they are time-consuming and expensive and are, therefore, only practical at relatively few locations, and often with low temporal resolution. Spatial sampling strategies, designed specifically for the remote sensing observation of interest, can reduce uncertainties in comparisons between ground-based and airborne/spaceborne estimates. Intensive remote sensing calibration and validation campaigns, often associated with an upcoming or recent satellite launch, provide unique opportunities for detailed characterization at a wide range of scales, and these are typically large international collaborative efforts. This chapter reviews standard in situmanual field measurements for snow and ice properties, as well as newer high-resolution techniques and instruments used to simulate airborne and spaceborne remote sensing observations. Sampling strategies and example applications from recent international calibration and validation experiments are given. Field measurements are a crucial component of remote sensing of the cryosphere, as they provide both the necessary direct observations of the variables of interest, as well as measurements that simulate the particular remote sensing technique at scales that can be characterized accurately. Ground-based observations provide the information needed to: improve and develop new retrieval algorithms; calibrate algorithms; and validate results to provide accurate uncertainty assessments

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

Observations of Moderate to Deep Seasonal Snow in Agricultural Fields with a Radar Scatterometer at Ku- and X-band Frequencies

The water contained within a snowpack, or the snow water equivalent (SWE), is very important to the hydrological cycle and to populations who depend on it for drinking, agriculture and industry. Measuring SWE over large areas is therefore important, but difficult given the scale of such an endeavor. Radar remote sensing of snow offers the promise of measuring SWE remotely but before we can do so, we must better understand how microwaves and snow interact. This thesis investigates the interaction of Ku- and X-band radar with moderate to deep seasonal snow in agricultural fields over two winters in Ontario. The University of Waterloo Scatterometer (UW-Scat) was used to make measurements of both snow-covered and snow-free fields in Maryhill and Englehart Ontario spanning a range of SWE up to 186 mm. In the 2013-14 season, 4 observations were made in Maryhill. In the 2014-15 season 3 sites were revisited over 6 dates in Maryhill and 3 sites were visited in Englehart. Accompanying the radar observations, in situ observations of snowpack properties including depth, density, stratigraphy, and grain size estimation were made at each site. Sensitivity to SWE was observed at Ku-band but not at X-band. An upper limit of sensitivity was observed around 140 mm after which point, Ku-band backscatter no longer responded to increasing SWE. However an investigation of seasonal depth hoar evolution suggested that the presence of depth hoar layers within the snowpack was the primary influence on backscatter response. Polarimetric data indicated the signal from early season, low-accumulation snowpacks was driven by vegetation where present and this influence decreased with further accumulation of snow. The major contribution of this study is the identification of depth hoar layers as a driver of backscatter response. This outcome points the way to further research on the influence of depth hoar, especially the mechanisms by which it exerts influence on the signal. Another contribution of this study is the identification of early-season influence of agricultural vegetation on backscatter through the use of polarimetric information

Automatisierte Erkennung und Kartierung von Lawinenablagerungen mit optischen Fernerkundungsdaten

Lawinen bedrohen Gebäude sowie Verkehrsinfrastruktur im Alpenraum. Sie fordern in der Schweiz mehr Todesopfer als jeder andere Typ von Naturkatastrophen. Deshalb sind rasch verfügbare und präzise Informationen über die Lage und Reichweite von Lawinenereignissen wichtig für die Lawinenwarnung und die Entscheidungsfindung bezüglich der Sperrung von Strassen, Bergbahnen und Skipisten. Für die Evaluation der Gefahrenprognose, für die Erstellung von Kataster und Gefahrenkarten sowie für die Kalibrierung und Evaluation von Lawinenmodellen sind sie ebenfalls von grosser Bedeutung. Heute werden diese Informationen vorwiegend von Beobachtern vor Ort erhoben. Aufgrund der eingeschränkten Zugänglichkeit hochalpiner Gebiete im Winter kann aber nur ein Bruchteil aller Lawinenereignisse erfasst werden. Insbesondere kleinere bis mittlere Lawinenereignisse in abgelegenen Gebieten werden nur sporadisch kartiert. Aber gerade dieser Lawinentyp fordert die meisten Todesopfer unter der steigenden Zahl von Wintersportlern, die sich abseits der markierten Pisten bewegen. Fernerkundungssensoren können auch über schwer zugänglichem Gebiet grossflächig Daten erheben und sind deshalb ein potentielles Werkzeug, das zur Schliessung dieser Informationslücke beitragen kann. In dieser Arbeit wird systematisch untersucht, inwiefern Lawinenkegel mit räumlich hochauflösenden optischen Fernerkundungsdaten erkannt und kartiert werden können. Anhand von Feld-Spektroradiometermessungen von neun Lawinenkegeln wird analysiert, ob allgemeingültige, substantielle spektrale Unterschiede zwischen Lawinenkegel und der angrenzenden, ungestörten Schneedecke bestehen. Obwohl interessante Absorptionsfeatures im nahen Infrarotbereich des elektromagnetischen Spektrums identifiziert werden können, sind die Unterschiede kaum ausgeprägt genug, um sie mit flugzeug- oder satellitengestützten Sensoren zu erfassen. Das direktionale Reflexionsverhalten der rauen Oberfläche eines Lawinenkegels verhält sich konträr zum Reflexionsverhalten der ungestörten Schneedecke. Anhand von Daten des Luftbildscanners ADS40, aufgenommen aus unterschiedlichen Blickwinkeln, kann gezeigt werden, dass dieser Unterschied im Reflexionsverhalten der zwei Schneeoberflächentypen mit grosser Wahrscheinlichkeit genutzt werden kann, um Lawinenkegel zu detektieren. Allerdings reicht der in dieser Untersuchung verfügbare Blickwinkelunterschied von 16° nicht aus, um Lawinenkegel allein auf Basis der direktionalen Unterschiede mit genügender Genauigkeit zu kartieren. Die Texturen von Lawinenkegeln und der ungestörten Schneedecke unterscheiden sich deutlich. Eine grobe Unterscheidung ist bereits von blossem Auge möglich. Die Statistik zweiter Ordnung, welche die räumliche Verteilung von Intensitätswerten berücksichtigt, kann Texturmerkmale in digitalen Bilddaten quantitativ erfassen. Dies ist die Voraussetzung für eine automatisierte Erkennung spezifischer Texturen. Anhand von RC30 Luftbildern, aufgenommen während des Lawinenwinters 1999, werden in der Literatur beschriebene Texturmasse auf ihre Eignung für die Unterscheidung zwischen Lawinenkegel und ungestörter Schneedecke getestet. Dabei werden die massgebenden Parameter systematisch variiert, um die optimalen Einstellungen zu identifizieren. Das Texturmass Entropy erweist sich als stabilster Indikator für die Differenzierung zwischen rauen und glatten Schneeoberflächen. Weil aber auch weitere raue Schneeoberflächen, wie vom Wind modellierte Schneedecken oder künstlich angehäufter Schnee an Rändern von Skipisten, vergleichbare Texturwerte wie Lawinenkegel zeigen, reichen Texturparameter alleine nicht aus, um Lawinenkegel eindeutig zu identifizieren. Basierend auf den Erkenntnissen aus den vorangegangenen Untersuchungen wird eine Prozessierungskette entwickelt, welche spektrale und direktionale Parameter mit Texturparametern und Informationen aus Hilfsdatensätzen kombiniert. Diese Prozessierungskette wird anhand von Daten des Luftbildscanners ADS40 im Raum Davos evaluiert und verbessert. Dabei werden 94% der in drei Testgebieten vorhandenen Lawinenkegel vom Algorithmus korrekt erkannt. Auch kleinere Kegel mit einer Fläche von weniger als 2000 m2 und Kegel in Schattenhängen werden korrekt erfasst. Dieses Ergebnis zeigt das grosse Potential des entwickelten Ansatzes für die automatisierte Erkennung und Kartierung von Lawinenkegeln. Die Verfügbarkeit geeigneter Daten ist aber aufgrund der nach intensiven Schneefällen häufigen noch vorhandenen Bewölkung eingeschränkt. Zudem treten vereinzelt Fehlklassifikationen auf. Dies sind hauptsächlich vom Wind modellierte Schneedecken, künstlich angehäufter Schnee und von spärlicher Vegetation durchsetzte Flächen. Trotz diesen Einschränkungen kann der in dieser Arbeit entwickelte Ansatz in Zukunft zur Schliessung substanzieller Datenlücken beitragen. Besonders in Gebirgen von Entwicklungsländern, in denen noch kaum verlässliche Informationen über Lawinenniedergänge existieren, können damit wertvolle Informationen für die Gefahrenkartierung und die Siedlungsplanung gewonnen werden. Summary Snow-avalanches kill more people in Switzerland than any other natural hazard and threaten buildings and traffic infrastructure. Rapidly available and accurate information about the location and extent of avalanche events is important for avalanche forecasting, safety assessments for roads and ski resorts, verification of warning products as well as for hazard mapping and avalanche model calibration/validation. Today, isolated observations from individual experts in the field provide information with limited coverage. Only a fraction of all avalanche events can be recorded due to restricted accessibility of many alpine terrain sections during winter season. Information on small to medium size avalanche events within remote regions is collected only sporadically. However, these avalanches notably claim most casualties within the raising number of people pursing off-slope activities. Remote sensing instruments are able to acquire wide-area datasets even over poorly accessible regions. Therefore they are promising tools to close the above- mentioned information gap. This research systematically investigates the potential of spatially high resolved remote sensing instruments for the detection and mapping of snow-avalanche deposits. Fieldspectroradiometer data of nine avalanche deposits are analysed to identify universally valid and significant spectral offsets between avalanche deposits and the adjacent undisturbed snow cover. Promising absorption features are found in the near-infrared region of the electromagnetic spectrum. Nevertheless, the differences are unlikely to be distinct enough for a detection using air- or spaceborne remote sensing instruments. The directional reflection of rough avalanche deposit surfaces is contrary to the directional reflection of smooth undisturbed snow covers. The potential of multriangular remote sensing data for the detection and mapping of avalanche deposits is demonstrated using multiangular data acquired by the airborne scanner ADS40. However, the difference between observation angles (16°) proves to be insufficient for accurate avalanche detection solely on the base of directional properties. Therefore, auxiliary data has to be utilised. The texture of avalanche deposits and undisturbed snow cover can already be distinguished by the naked eye. Using second-order statistics, comprising the spatial distribution of the variation in pixel brightness, textural characteristics in digital image data can be quantified. This is a prerequisite for an automated detection of particular textures. Different established texture measures are tested for their discriminating potential of avalanche deposits and undisturbed snow cover using RC30 aerial images of avalanche deposits acquired within the avalanche winter 1999 in Switzerland. The control parameters such as the size of the filter box are systematically varied to find the ideal settings. The texture measure Entropy is identified as the most distinct and stable indicator to distinguish between rough and smooth snow surfaces. But avalanche deposits are not the only rough snow surfaces within the Alpine winter landscape. For example wind modeled snow surfaces or artificially piled snow at the edge of roads and ski slopes show texture characteristics similar to avalanche deposits. Consequently, a classification approach using texture information only is not sufficient for an accurate identification of avalanche deposits. Based on the findings described above, we develop an avalanche detection and mapping processing chain, combining spectral, directional and textural parameters with auxiliary datasets. The processing chain is tested and improved using data acquired by the airborne scanner ADS40 over the region of Davos, Switzerland. The accuracy assessment, based on ground reference data within three test sites, shows that 94% of all existing avalanche deposits are identified. Even small scale deposits (area < 2000 m2) and deposits within shadowed areas are detected correctly. These results demonstrate the big potential of the proposed approach for automated detection and mapping of avalanche deposits. Yet, cloud cover constrains the availability of appropriate optical remote sensing data after heavy snowfall while wind modeled snow surfaces, artificially piled snow and sparsely vegetated snow surfaces cause sporadic misclassifications. Despite these constraints, the approach developed within this research shows a big potential to fill existing gaps in avalanche information. Especially within alpine areas of developing countries with almost no reliable information on past avalanche events, such an approach may be used to acquire valuable data for hazard mapping and settlement planning

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

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources