Detection of dry snow using spaceborne microwave radiometer data

Snow monitoring on global scale is an important task considering the essential role of snow cover in the Earth’s climate and the scarcity of ground-based snow observations. Snow has distinctive, frequency-dependent characteristics in terms of microwave emission.This enables the use of brightness temperatures, as measured by spaceborne passive microwave sensors, not only for the estimation of snow cover extent (SCE) through (dry) snow detection, but also for the retrieval of snow depth and snow water equivalent (SWE).Approaches for SWE retrieval, such as the methodology of the GlobSnow v3.0 SWE product, frequently implement dry snow detection as one of the main processing steps. Reliable dry snow detection is thus crucial, however, common algorithms are known to generally underestimate the presence of snow due to their sensitivity to vegetation and liquid water content of the snowpack, amongst other. Although several suggestions for improvement have been proposed, an extensive, long-term comparison has not been conducted. This thesis hence investigates six current dry snow detection algorithms and their intraseasonal performance in order to identify the most appropriate one for implementation in the GlobSnow SWE product. The aim is to improve the product which is primarily affected by underestimation during the snow accumulation period from September to February. The investigated algorithms are based on the brightness temperature difference involving primarily, but not exclusively, the 18/19-GHz and 37-GHz channels which are available for the SMMR, SSM/I and SSMIS instruments covering more than 40 years of observations. In addition to conventional daily snow masks, cumulative snow masks are investigated as a means to counteract underestimation. The assessment focuses on seasonal snow above 40° North, and is conducted for the snow seasons from 1979/1980 to 2017/2018 with reference to exhaustive, in situ snow depth data from multiple sources. In addition, spatially-complete SCE maps by the Interactive Multisensor Snow and Ice Mapping System serve as reference from 2007/2008 to 2016/2017, in order to evaluate the detected snow cover extent as a whole. The results emphasise the potential of cumulative masks to counteract underestimation and increase detection accuracy, and highlight the benefit of discriminating between different scattering sources, that could otherwise be mistaken for snow. Two methods are found to be overall best-performing: the empirically-derived algorithm of the EUMETSAT H SAF H11 product (applicable to SMMR, SSM/I and SSMIS), and the decision tree published by Grody and Basist in 1996 (applicable to SSM/I and SSMIS). Promising accuracies with respect to in situ data are achieved using cumulative masks, reaching approximately 0.83 and 0.80 for the approaches of Grody and Basist and of the H SAF product, respectively. Implementing the H SAF algorithm into the GlobSnow SWE product is expected to lead to immediate improvements of the latter and is thus planned, though falls outside the scope of this thesis. Further investigation is required to adapt the approach of Grody and Basist to the whole long-term passive microwave data record including SMMR data

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

ORCHIDEE-MICT (revision 4126), a land surface model for the high-latitudes: model description and validation

Abstract. The high-latitude regions of the northern hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance – those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest – are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently-developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input data sets, are extensively evaluated against: (i) temperature gradients between the atmosphere and deep soils; (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment. </jats:p

Snow observations from Arctic Ocean Soviet drifting stations: legacy and new directions

The Arctic Ocean is one of the most rapidly changing regions on the planet. Its warming climate has driven reductions in the region's sea ice cover which are likely unprecedented in recent history, with many of the environmental impacts being mediated by the overlying snow cover. As well as impacting energetic and material fluxes, the snow cover also obscures the underlying ice from direct satellite observation. While the radar waves emitted from satellite-mounted altimeters have some ability to penetrate snow cover, an understanding of snow geophysical properties remains critical to remote sensing of sea ice thickness. The paucity of Arctic Ocean snow observations was recently identified as a key knowledge gap and uncertainty by the Intergovernmental Panel on Climate Change's Special Report on Oceans and Cryosphere in a Changing Climate. This thesis aims to address that knowledge gap. Between 1937 and 1991 the Soviet Union operated a series of 31 crewed stations which drifted around the Arctic Ocean. During their operation, scientists took detailed observations of the atmospheric conditions, the physical oceanography, and the snow cover on the sea ice. This thesis contains four projects that feature these observations. The first two consider a well known snow depth and density climatology that was compiled from observations at the stations between 1954 & 1991. Specifically, Chapter two considers the role of seasonally evolving snow density in sea ice thickness retrievals, and Chapter three considers the impact of the climatological treatment itself on satellite estimates of sea ice thickness variability and trends. Chapter four presents a statistical model for the sub-kilometre distribution of snow depth on Arctic sea ice through analysis of snow depth transect data. Chapter five then compares the characteristics of snow melt onset at the stations with satellite observations and results from a recently developed model

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

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

The Arctic

peer reviewe

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

HUMAN AND CLIMATE IMPACTS ON FLOODING VIA REMOTE SENSING, BIG DATA ANALYTICS, AND MODELING

Over the last 20 years, the amount of streamflow has greatly increased and spring snowmelt floods have occurred more frequently in the north-central U.S. In the Red River of the North Basin (RRB) overlying portions of North Dakota and Minnesota, six of the 13 major floods over the past 100 years have occurred since the late 1990s. Based on numerous previous studies as well as senior flood forecasters’ experiences, recent hydrological changes related to human modifications [e.g. artificial subsurface drainage (SSD) expansion] and climate change are potential causes of notable forecasting failures over the past decade. My dissertation focuses on the operational and scientific gaps in current forecasting models and observational data and provides insights and value to both the practitioner and the research community. First, the current flood forecasting model needs both the location and installation timing of SSD and SSD physics. SSD maps were developed using satellite “big” data and a machine learning technique. Next, using the maps with a land surface model, the impacts of SSD expansion on regional hydrological changes were quantified. In combination with model physics, the inherent uncertainty in the airborne gamma snow survey observations hinders the accurate flood forecasting model. The operational airborne gamma snow water equivalent (SWE) measurements were improved by updating antecedent surface moisture conditions using satellite observations on soil moisture. From a long-term perspective, flood forecasters and state governments need knowledge of historical changes in snowpack and snowmelt to help flood management and to develop strategies to adapt to climate changes. However, historical snowmelt trends have not been quantified in the north-central U.S. due to the limited historical snow data. To overcome this, the current available historical long-term SWE products were evaluated across diverse regions and conditions. Using the most reliable SWE product, a trend analysis quantified the magnitude of change extreme snowpack and melt events over the past 36 years. Collectively, this body of research demonstrates that human and climate impacts, as well as limited and noisy data, cause uncertainties in flood prediction in the great plains, but integrated approaches using remote sensing, big data analytics, and modeling can quantify the hydrological changes and reduce the uncertainties. This dissertation improves the practice of flood forecasting in Red River of the North Basin and advances research in hydrology and snow science