Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “snow remote sensing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Remote sensing of snow properties by passive microwave radiometry: GSFC truck experiment

Recent results indicate that microwave radiometry has the potential for inferring the snow depth and water equivalent information from snowpacks. In order to assess this potential for determining the water equivalent of a snowpack, it is necessary to understand the microwave emission and scattering behavior of the snow at various wavelengths under carefully controlled conditions. Truck-mounted microwave instrumentation was used to study the microwave characteristics of the snowpack in the Colorado Rocky Mountain region during the winters of 1977 to 78 and 7978 to 79. The spectral signatures of C, X, K sub u, and K sub a band radiometers with dual polarization were used, together with measurements of snowpack density, temperature an ram profiles, liquid water content, and rough characterization of the crystal sizes. These data compared favorably with calculated results based on recent microscopic scattering models.

Chang, A. T. C.↗
NASA NTRS1981

Investigation of multispectral remote sensing of snow cover using a solar radiation model

A solar radiation model is described which is able to calculate, for given atmospheric and topographic input conditions, the spectral distributions of the direct and diffuse components of the total irradiance incident upon a target surface. The irradiance includes both upwelling radiation backscattered toward the earth's surface and radiation reflected toward the target from other terrain surfaces. Spectral reflectance data for two dissimilar snow types are fed into the model, along with a geometric description of the topography of the snow covered terrain. These data permit the calculation of the exitance of the target over the wavelength limits of a given sensor and, hence, the apparent contrast between the two snow types for the given input conditions. The results show that the contrast between the two snow types, all other factors being constant, is greatest in the near infrared wavelengths.

Waterman, S. E.↗
NASA NTRS1977

Microwave remote sensing of snow experiment description and preliminary results

The active and passive microwave responses to snow were investigated at a site near Steamboat Springs, Colorado during the February and March winter months. The microwave equipment was mounted atop truck-mounted booms. Data were acquired at numerous frequencies, polarizations, and angles of incidence for a variety of snow conditions. The experiment description, the characteristics of the microwave and ground truth instruments, and the results of a preliminary analysis of a small portion of the total data volume acquired in Colorado are documented.

Ulaby, F. T.↗
NASA NTRS1987

Remote sensing of snow

The snow parameters affecting sensor responses at different wavelengths are discussed. The effects of snow depth and background radiation on gamma ray sensors and of crystal size, contaminants, snow depth, liquid water, and surface roughness on visible and near-infrared sensors are considered. The influence of temperature, crystal size, and liquid water on thermal infrared sensors and of liquid water, crystal size, water equivalent depth, stratification, snow surface roughness, density, temperature, and soil condition on microwave sensors are addressed.

Foster, J. L.↗
NASA NTRS2018

Microwave Remote Sensing of Snow: Advances over Ice Sheet, Land, and Sea Ice

Satellite microwave radiometers have enabled us to observe the cryosphere and its changes. A variety of algorithms have been developed since the late 1970s. These convert observed microwave radiation into geophysical, glaciological properties relevant to study ice sheets’ snow accumulation and melt, terrestrial snow water equivalent and freeze/thaw state, and various sea ice cover characteristics like extent, concentration, and thickness. In spite of the continuous availability of satellite microwave radiometers for the past 40 years, potential for advancing our understanding of the relationship between microwave radiation and snow/ice properties still exists. Original empirical algorithms have matured and are becoming more physically based. This presentation offers insights into some advances made during the past 10 years in monitoring ice sheet, terrestrial snow, and sea ice. These advances made it possible to provide new, more reliable climate-related variables to the community for the satellite era using the typical 18-37 GHz frequency range (e.g., retrievals of grain size profiles in Antarctica, snow cover stratification, and therefore accumulation with applications to climate studies). Recent NASA instruments have recorded low microwave frequencies (at 1.4 GHz). These observations have a large penetration depth, they emanate from deep into the ice, where properties including temperature are very stable. Nonetheless, it has been found at both Dome C, Antarctica and Summit, Greenland that changes in surface snow properties significantly influence these observations. Compared to ice sheets, terrestrial snow and sea ice present higher spatial heterogeneities. Over land, presence of canopy and lakes, though with known locations, add ambiguities in the retrievals of snow properties. Over sea ice, ridges, leads, changes in salinity, and sea ice drift augment further the level of difficulty in obtaining robust geophysical properties. Assessing the quality of satellite retrievals, often requiring field activities, is a necessary step in designing the next generation of microwave algorithms to monitor changes in the cryosphere.

Brucker, Ludovic↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗
NASA NTRS2008

Finland Validation of the New Blended Snow Product

As part of an ongoing effort to validate satellite remote sensing snow products for the recentlydeveloped U.S. Air Force Weather Agency (AFWA) - NASA blended snow product, Satellite and in-situ data for snow extent and snow water equivalent (SWE) are evaluated in Finland for the 2006-2007 snow season Finnish Meteorological Institute (FMI) daily weather station data and Finnish Environment Institute (SYKE) bi-monthly snow course data are used as ground truth. Initial comparison results display positive agreement between the AFWA NASA Snow Algorithm (ANSA) snow extent and SWE maps and in situ data, with discrepancies in accordance with known AMSR-E and MODIS snow mapping limitations. Future ANSA product improvement plans include additional validation and inclusion of fractional snow cover in the ANSA data product. Furthermore, the AMSR-E 19 GHz (horizontal channel) with the difference between ascending and descending satellite passes (Diurnal Amplitude Variations, DAV) will be used to detect the onset of melt, and QuikSCAT scatterometer data (14 GHz) will be used to map areas of actively melting snow.

Kim, E. J.↗
NASA NTRS2015

A Vision for an International Multi-Sensor Snow Observing Mission

Discussions within the international snow remote sensing community over the past two years have led to encouraging consensus regarding the broad outlines of a dedicated snow observing mission. The primary consensus - that since no single sensor type is satisfactory across all snow types and across all confounding factors, a multi-sensor approach is required - naturally leads to questions about the exact mix of sensors, required accuracies, and so on. In short, the natural next step is to collect such multi-sensor snow observations (with detailed ground truth) to enable trade studies of various possible mission concepts. Such trade studies must assess the strengths and limitations of heritage as well as newer measurement techniques with an eye toward natural sensitivity to desired parameters such as snow depth and/or snow water equivalent (SWE) in spite of confounding factors like clouds, lack of solar illumination, forest cover, and topography, measurement accuracy, temporal and spatial coverage, technological maturity, and cost.

snow↗
NASA NTRS2011

Global Assimilation of Multi-Sensor Snow Observations for Improved Characterization of Snow Processes

Snow conditions on the land surface are recognized to be key components of the global hydrological cycle as they play a critical role in the determination of local and regional climate. In many mid-latitude and high-latitude regions, the seasonal water storage and associated spring snowmelt dominate the local hydrology. The contribution to the runoff and moisture conditions from snow is vital in supporting agriculture and in determining water resources management practices. Consequently, accurate characterization of snow properties becomes important for both end-use applications and weather and climate research. Recently a joint effort between the u.S. Air Force and NASA has enabled a blended, multi-sensor snow product known as the AFWA NASA Snow Algorithm (ANSA). This global snow dataset has been generated by utilizing the Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) datasets. ANSA product includes estimates of snow cover extent, snow water equivalent (SWE) and SWE-derived snow depth fields. The MODIS-based products enable snow cover mappings under cloud-free conditions whereas the passive microwave data from AMSR-E provides measurements under cloudy conditions. These remotely-sensed snow observations are further augmented with the information from ground-based snow measurements through data fusion techniques. The resulting ANSA products are employed in the NASA Land Information System (LIS) data assimilation framework, which provides a comprehensive environment for integrating community land surface models, ground and satellite-based observations, and ensemble-based data assimilation tools. LIS incorporates the multisensor ANSA snow retrievals with the land surface model estimates to generate spatially and temporally continuous estimates of snow states, through data assimilation. A suite of experiments to assimilate ANSA snow cover, SWE and snow depth estimates with different land surface models in LIS are conducted and the resulting estimates of snow conditions are evaluated against a number of in-situ observational datasets, over several regions of the world. These evaluations are used to compare and contrast the advantages and disadvantages of these multi-sensor snow observations.

Kumar, Sujay↗
NASA NTRS2024

Enabling Advanced Snow Physics within Land Surface Models Through an Interoperable Model-Physics Coupling Framework

Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference datasets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2-12% and 57-75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Snow model↗
NASA NTRS2015

Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEx): For Measurement Sake Let it Snow

As a component of the Earth's hydrologic cycle, and especially at higher latitudes,falling snow creates snow pack accumulation that in turn provides a large proportion of the fresh water resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) mission Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011-2012 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite,and radiometers on constellation member satellites. Multi-parameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude and in-situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites taking in-situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx fieldcampaign is described and three illustrative cases detailed.

GCPEx↗
NASA NTRS2011

Passive Microwave Remote Sensing of Falling Snow and Associated GPM Field Campaigns

Retrievals of falling snow from space represent one of the next important challenges for the atmospheric, hydrological, and energy budget scientific communities. Historically, retrievals of falling snow have been difficult due to the relative insensitivity of satellite rain-based channels as used in the past. We emphasize the use of high frequency passive microwave channels (85-200 GHz) since these are more sensitive to the ice in clouds and have been used to estimate falling snow from space. While satellite-based remote sensing provides global coverage of falling snow events and the science is relatively new, retrievals are still undergoing development with challenges remaining. There are several current satellite sensors, though not specifically designed for estimating falling snow, are capable of measuring snow from space. These include NOAA's AMSU-B, the MHS sensors, and CloudSat radar. They use high frequency (greater than 85 GHz) passive and active microwave and millimeter-wave channels that are sensitive to the scattering from ice and snow particles in the atmosphere. Sensors with water vapor channels near 183 GHz center line provide opaqueness to the Earth's surface features that can contaminate the falling snow signatures, especially over snow covered surface. In addition, the Global Precipitation Measurement (GPM) mission scheduled for launch in 2013 is specifically designed to measure both liquid rain and frozen snow precipitation. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, and surface types. For example, can a lake effect snow system with low cloud tops having an ice water content (IWC) at the surface of 1.0 gram per cubic meter be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. In this work, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases [9] since simulations provide all the information to determine the measurements from space and the ground truth. This analysis relies on data from the Canadian CloudSat/CALIPSO Validation Program (C3VP) field campaign held from October 31, 2006 through March 1, 2007. In January 2012 GPM will return to the C3VP area for the GPM Cold Precipitation Experiment (GCPEx). This presentation will describe the thresholds-of-detection procedure and results, as well as the field campaign details.

Skofronick-Jackson, Gail↗
NASA NTRS1987

Theoretical models for microwave remote sensing of snow-covered sea ice

The volume scattering effects of snow-covered sea ice are studied with a three-layer random medium model for microwave remote sensing. Theoretical results are illustrated by matching experimental data for dry snow-covered thick first-year sea ice at Point Barrow. The radar backscattering cross sections are seen to increase with snow cover for snow-covered sea ice, due to the increased scattering effects in the snow layer. The results derived can also be applied to passive remote sensing.

Lin, F. C.↗
NASA NTRS2007

Remote Sensing of Terrestrial Snow and Ice for Global Change Studies

Snow and ice play a significant role in the Earth's water cycle and are sensitive and informative indicators climate change. Significant changes in terrestrial snow and ice water storage are forecast, and while evidence of large-scale changes is emerging, in situ measurements alone are insufficient to help us understand and explain these changes. Imaging remote sensing systems are capable of successfully observing snow and ice in the cryosphere. This chapter examines how those remote sensing sensors, that now have more than 35 years of observation records, are capable of providing information about snow cover, snow water equivalent, snow melt, ice sheet temperature and ice sheet albedo. While significant progress has been made, especially in the last five years, a better understanding is required of the records of satellite observations of these cryospheric variables.

Kelly, Richard↗
NASA NTRS1985

Snowmelt-runoff Model Utilizing Remotely-sensed Data

Remotely sensed snow cover information is the critical data input for the Snowmelt-Runoff Model (SRM), which was developed to simulatke discharge from mountain basins where snowmelt is an important component of runoff. Of simple structure, the model requires only input of temperature, precipitation, and snow covered area. SRM was run successfully on two widely separated basins. The simulations on the Kings River basin are significant because of the large basin area (4000 sq km) and the adequate performance in the most extreme drought year of record (1976). The performance of SRM on the Okutadami River basin was important because it was accomplished with minimum snow cover data available. Tables show: optimum and minimum conditions for model application; basin sizes and elevations where SRM was applied; and SRM strengths and weaknesses. Graphs show results of discharge simulation.

Rango, A.↗
NASA NTRS1989

Stereological characterization of dry alpine snow for microwave remote sensing

A persistent problem in investigations of electromagnetic properties of snow, from reflectance at visible wavelengths to emission and backscattering in the microwave, has been the proper characterization of the snow's physical properties. It is suggested that the granular and laminar structure of snow can be measured in its aggregated state by stereology performed on sections prepared from snow specimens, and that these kinds of measurements can be incorporated into models of the electromagnetic properties. With careful sampling, anisotropy in the snow microstructure at various scales can be quantified. It is shown how stereological parameters can be averaged over orientation and optical depth for radiative transfer modeling.

Davis, Robert E.↗