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The cryosphere, that region of the world where water is temporarily or permanently frozen, plays a crucial role on our planet. Recent developments in remote sensing techniques, and the acquisition of new data sets, have resulted in significant advances in our understanding of all components of the cryosphere and its processes. This book, based on contributions from 40 leading experts, offers a comprehensive and authoritative overview of the methods, techniques and recent advances in applications of remote sensing of the cryosphere. Examples of the topics covered include: • snow extent, depth, grain-size and impurities • surface and subsurface melting • glaciers • accumulation over the Greenland and Antarctica ice sheets • ice thickness and velocities • gravimetric measurements from space • sea, lake and river ice • frozen ground and permafrost • fieldwork activities • recent and future cryosphere-oriented missions and experiments All figures are in color and provide an excellent visual accompaniment to the technical and scientific aspect of the book. Readership: Senior undergraduates, Masters and PhD Students, PostDocs and Researchers in cryosphere science and remote sensing. Remote Sensing of the Cryosphere is the significant first volume in the new Cryosphere Science Series. This new series comprises volumes that are at the cutting edge of new research, or provide focussed interdisciplinary reviews of key aspects of the science.
Comprehensive Remote Sensing, Nine Volume Set covers all aspects of the topic, with each volume edited by well-known scientists and contributed to by frontier researchers. It is a comprehensive resource that will benefit both students and researchers who want to further their understanding in this discipline. The field of remote sensing has quadrupled in size in the past two decades, and increasingly draws in individuals working in a diverse set of disciplines ranging from geographers, oceanographers, and meteorologists, to physicists and computer scientists. Researchers from a variety of backgrounds are now accessing remote sensing data, creating an urgent need for a one-stop reference work that can comprehensively document the development of remote sensing, from the basic principles, modeling and practical algorithms, to various applications. Fully comprehensive coverage of this rapidly growing discipline, giving readers a detailed overview of all aspects of Remote Sensing principles and applications Contains ‘Layered content’, with each article beginning with the basics and then moving on to more complex concepts Ideal for advanced undergraduates and academic researchers Includes case studies that illustrate the practical application of remote sensing principles, further enhancing understanding
Improving the estimation of snow water equivalent (SWE) in the Sierra Nevada is critical for the water resources management in California. In this study, we carried out an experiment to estimate SWE in the Upper Kern Basin, Sierra Nevada, by assimilating AMSR-E observed brightness temperatures (Tb) into a coupled hydrology and radiative transfer model using an ensemble Kalman batch reanalysis. The data assimilation framework merges the complementary SWE information from modeling and observations to improve SWE estimates. The novelty of this assimilation study is that both the modeling and the radiance data processing were specifically improved to provide more information about SWE. With the enhanced SWE signals in both simulations and observations, the batch reanalysis stands a better chance of successfully improving the SWE estimates. The modeling was at a very high resolution (90m) and spanned a range of mountain environmental factors to better characterize the effects of the mountain environment on snow distribution and radiance emission. We have developed a dynamic snow grain size module to improve the radiance modeling during the intense snowfall events. The AMSR-E 37GHz V-pol observed Tb was processed at its native footprint resolution at ~100 square km. In the batch assimilation, the model predicted the prior SWE and Tb; the prior estimate of an entire year was then updated by the dry-season observations at one time. One advantage of this is that the prior SWE of a certain period is updated using the observations both before and after this period, which takes advantage of the temporally continuous signal of the seasonal snow accumulation in the observations. We found the posterior SWE estimates showed improved accuracy and robustness. During the study period of 2003 to 2008, at point-scale, the average bias of the six-year April 1st SWE was reduced from -0.17 m to -0.02m, the average temporal SWE RMSE of the snow accumulation season decreased by 51.2%. The basin-scale results showed that the April 1st SWE bias reduced from -0.17m to -0.11m, and the temporal SWE RMSE of the accumulation season decreased by 23.6%.
An independent technical study evaluating the use of snowpack depth measurements to estimate snow water equivalent (SWE) of shallow and ephemeral snowpacks in the Great Salt Lake Desert Basin, located in Utah, Nevada, and Idaho. A parameterized bulk snow density model was combined with mean air temperature measurements to predict snow water equivalent in the Great Salt Lake Desert Basin using only snowpack depth measurements and prior 10-day average daily mean air temperatures. The model was developed using historic snowpack data obtained from a limited number of automated snowpack telemetry (SNOTEL) and weather stations within and near the Basin. Model results from lower-elevation, shallow and ephemeral snowpacks may be used to supplement data obtained from existing SNOTEL stations, sparsely located in the higher elevations of the Basin, to create a more-complete and accurate prediction of the Basin’s snow water equivalent, which may be used to better-manage the water demands of the Basin’s surrounding populations.
Water quality and management are of great significance globally, as the demand for clean, potable water far exceeds the availability. Water science research brings together the natural and applied sciences, engineering, chemistry, law and policy, and economics, and the Treatise on Water Science seeks to unite these areas through contributions from a global team of author-experts. The 4-volume set examines topics in depth, with an emphasis on innovative research and technologies for those working in applied areas. Published in partnership with and endorsed by the International Water Association (IWA), demonstrating the authority of the content Editor-in-Chief Peter Wilderer, a Stockholm Water Prize recipient, has assembled a world-class team of volume editors and contributing authors Topics related to water resource management, water quality and supply, and handling of wastewater are treated in depth
California Department of Water Resources (DWR) Snow Surveys Section has recently explored the potential use of recently developed hydrologic models to estimate snow water equivalent (SWE) for the Sierra Nevada mountain range. DWR Snow Surveys Section's initial step is to determine how well these hydrologic models compare to the trusted regression equations, currently used by DWR Snow Surveys Section. A comparison scheme was ultimately developed between estimation measures for SWE by interpreting model results for the Feather River Basin from: a) National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) gridded SWE reconstruction product, b) United States Geological Survey (USGS) Precipitation-Runoff Modeling System (PRMS), and c) DWR Snow Surveys Section regression equations. Daily SWE estimates were extracted from gridded results by computing an average SWE based on 1,000 ft elevation band increments from 3,000 to 10,000 ft (i.e. an elevation band would be from 3,000 to 4,000 ft). The dates used for processing average SWE estimates were cloud-free satellite image dates during snow ablation months, March to August, for years 2000-2012. The average SWE for each elevation band was linearly interpolated for each snow sensor elevation. The model SWE estimates were then compared to the snow sensor readings used to produce the snow index in DWR's regression equations. In addition to comparing JPL's SWE estimate to snow sensor readings, PRMS SWE variable for select hydrologic response units (HRU) were also compared to snow sensor readings. Research concluded with the application of statistical methods to determine the reliability in the JPL products and PRMS simulated SWE variable, with results varying depending on time duration being analyzed and elevation range.
Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 163. The North, with its vast and varied landscapes, sparse population, and cold climate has always challenged its explorers: physically, mentally, logistically, and technically. The scientific community in particular has known such challenges in the past and does so today, especially in light of the projected intensification of climate change at high latitudes. Indeed, there are clear signs that change is already ongoing in many environmental variables: Air temperature and annual precipitation (including snowfall) are increasing in many regions; spring snow cover extent is decreasing; lake and river ice freeze-up dates are occurring later and breakup dates earlier; glaciers are retreating rapidly; permafrost temperatures are increasing and, in many cases, the permafrost is thawing; and sea-ice extent is at record minimums and thinning.
Since 1982, under an agreement between the California Department of Water Resources and the USDA Forest Service, snow sensors have been installed and operated in Forest Service-administered wilderness areas in the Sierra Nevada of California. The sensors are to be removed by 2005 because of the premise that sufficient data will have been collected to allow "correlation" and, by implication, prediction of wilderness snow data by nonwilderness sensors that are typically at a lower elevation. Because analysis of snow water equivalent (SWE) data from these wilderness sensors would not be possible until just before they are due to be removed, "surrogate pairs" of high- and low-elevation snow sensors were selected to determine whether correlation and prediction might be achieved. Surrogate pairs of sensors with between 5 and 15 years of concurrent data were selected, and correlation and regression were used to examine the statistical feasibility of SWE prediction after "removal" of the wilderness sensors. Of the 10 pairs analyzed, two pairs achieved a correlation coefficient of 0.95 or greater. Four more had a correlation of 0.94 for the accumulation period after the snow season was split into accumulation and melt periods. Standard errors of estimate for the better fits ranged from 15 to 25 percent of the mean April 1 snow water equivalent at the high-elevation sensor. With the best sensor pairs, standard errors of 10 percent were achieved. If this prediction error is acceptable to water supply forecasters, sensor operation through 2005 in the wilderness may produce predictive relationships that are useful after the wilderness sensors are removed
Coherent synthetic aperture radar (SAR) based terrain monitoring relies on effective image focusing, compensation of interferometric phase biases and mitigation of decorrelation effects. The observation of dry-snow-covered terrain is affected by microwave interaction with the snow and this poses a challenge for terrain mapping applications in terms of the effect of the snow on target focusing, signal phase bias and decorrelation. However, these effects also provide an opportunity to map snow properties. As such, dry-snow presents a dual problem for coherent SAR applications: the joint mitigation of the effects of snow to allow unbiased observation of the terrain under the snow and measurement of the snow layer itself. This thesis introduces novel methods to address three aspects of this dual problem: (i) image formation - the defocusing and phase biasing effects of dry-snow are considered including how these can be corrected and exploited to estimate snow water equivalent (SWE) from a single SAR channel; (ii) interferometric phase bias - the limitations of SAR interferometry (InSAR) based SWE change mapping are addressed by exploiting the effect that terrain slope has on the dry-snow InSAR phase contribution; and (iii) temporal decorrelation - an adaptation of phase-linking methods is introduced to better enable multi-temporal InSAR for the case of seasonal snow covered terrain which suffers sever cross-season decorrelation effects. In each case, an analytical model for the respective method is presented, the use of the method is demonstrated with simulated data while method performance is validated with real SAR datasets, either from the SFU Airborne SAR System or the RADARSAT-2 satellite. Together, these contributions represent a significant advancement in enabling wide-scale and persistent coherent SAR monitoring of snow-covered terrains.