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field data to serve as the basis for validating current and emerging algorithms, as well as developing a cryosphere-specific spaceborne imaging program to address the limitations mentioned above. Most recent algorithms for snow property mapping have not been tested under a wide variation of snow cover properties, land cover, and terrain. This has prevented a thorough quantification of the confidence, or skill, of different approaches and has prevented a comprehensive determination of the conditions
field data to serve as the basis for validating current and emerging algorithms, as well as developing a cryosphere-specific spaceborne imaging program to address the limitations mentioned above. Most recent algorithms for snow property mapping have not been tested under a wide variation of snow cover properties, land cover, and terrain. This has prevented a thorough quantification of the confidence, or skill, of different approaches and has prevented a comprehensive determination of the conditions
ice as compared with snow-free areas. Microwave brightness temperature measured by spaceborne sensors over snow-covered areas originates from radiation emitted from the underlying surface, the snowpack, the vegetation, and the atmosphere. In theory, the dielectric constant of frozen water is altered relative to that of water in its liquid form, and the effect of snow on the emissivity can be used in algorithms to estimate snow water equivalence (SWE) from spaceborne emissions, typically at 18
ice as compared with snow-free areas. Microwave brightness temperature measured by spaceborne sensors over snow-covered areas originates from radiation emitted from the underlying surface, the snowpack, the vegetation, and the atmosphere. In theory, the dielectric constant of frozen water is altered relative to that of water in its liquid form, and the effect of snow on the emissivity can be used in algorithms to estimate snow water equivalence (SWE) from spaceborne emissions, typically at 18
properties as well as remote sensing signatures. The dataset is archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado (available online at http://nsidc.org/data/clpx/ ). The purpose of the LSOS measurements was to allow scaling between ground-based and airborne- and satellite-based instruments. The detailed measurements allow full characterization of the test site and can be used to validate small-scale models and further extrapolate and test algorithms at larger scales. The
properties as well as remote sensing signatures. The dataset is archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado (available online at http://nsidc.org/data/clpx/ ). The purpose of the LSOS measurements was to allow scaling between ground-based and airborne- and satellite-based instruments. The detailed measurements allow full characterization of the test site and can be used to validate small-scale models and further extrapolate and test algorithms at larger scales. The
remote sensing attractive for providing spatially distributed snow information. Microwave datasets were collected in CLPX to help improve the understanding of microwave signal response to snow and soil properties, develop and test retrieval algorithms, and advance the use of microwave remote sensing data in models. a. Airborne Synthetic Aperture Radar imagery The AIRSAR is a side-looking imaging P-, L-, and C-band radar flown aboard the NASA DC-8 aircraft ( Lou et al. 2001 ). Two modes of AIRSAR data
remote sensing attractive for providing spatially distributed snow information. Microwave datasets were collected in CLPX to help improve the understanding of microwave signal response to snow and soil properties, develop and test retrieval algorithms, and advance the use of microwave remote sensing data in models. a. Airborne Synthetic Aperture Radar imagery The AIRSAR is a side-looking imaging P-, L-, and C-band radar flown aboard the NASA DC-8 aircraft ( Lou et al. 2001 ). Two modes of AIRSAR data
), and 2) the Utah Energy Balance Model (UEB; Tarboton and Luce 1996 ). Table 1 illustrates that both models have been extensively evaluated in a wide range of hydroclimatological conditions in terrestrial and marine environments. The NSM exploits the strengths of these models, notably the physical algorithms used by SNTHERM to solve all soil–snow–atmosphere mass and energy fluxes, other than the solution of snow surface temperature, which follows conventions of the UEB model. Euler predictor
), and 2) the Utah Energy Balance Model (UEB; Tarboton and Luce 1996 ). Table 1 illustrates that both models have been extensively evaluated in a wide range of hydroclimatological conditions in terrestrial and marine environments. The NSM exploits the strengths of these models, notably the physical algorithms used by SNTHERM to solve all soil–snow–atmosphere mass and energy fluxes, other than the solution of snow surface temperature, which follows conventions of the UEB model. Euler predictor
remote sensing algorithms, therefore, often include simple representations of radiative transfer in canopies. Variants of Beer’s law or two-stream approximations are generally used (e.g., Sellers et al. 1986 ; Verseghy et al. 1993 ); these treat canopies as horizontally homogeneous turbid media and only predict the average radiation. The radiative environment beneath real canopies, however, is highly heterogeneous because of sun flecks, canopy gaps, and clearings on wide ranges of length scales
remote sensing algorithms, therefore, often include simple representations of radiative transfer in canopies. Variants of Beer’s law or two-stream approximations are generally used (e.g., Sellers et al. 1986 ; Verseghy et al. 1993 ); these treat canopies as horizontally homogeneous turbid media and only predict the average radiation. The radiative environment beneath real canopies, however, is highly heterogeneous because of sun flecks, canopy gaps, and clearings on wide ranges of length scales
candidate ground returns from which the filtering algorithm can estimate the ground surface location. Sufficiently large slope-induced errors would be subject to removal by filtering algorithms. Three lidar datasets from each site were used for this study; they were acquired on 9 April 2003, 19 September 2003, and 1 April 2005. The raw data were normalized using ground control points, postprocessed to remove redundant data points and noise, and then classified as ground or vegetation points by the
candidate ground returns from which the filtering algorithm can estimate the ground surface location. Sufficiently large slope-induced errors would be subject to removal by filtering algorithms. Three lidar datasets from each site were used for this study; they were acquired on 9 April 2003, 19 September 2003, and 1 April 2005. The raw data were normalized using ground control points, postprocessed to remove redundant data points and noise, and then classified as ground or vegetation points by the
, unpublished manuscript). The purpose of measuring the meteorological parameters was to quantify variability from local to regional scales within various snow environments and to archive forcing data for algorithm and model development and verification. The network included 10 main meteorological towers and one eddy covariance site. Data from these sites and other existing meteorological networks provided a high-quality dataset with nested spatial coverage. Additional spatial datasets were collected during
, unpublished manuscript). The purpose of measuring the meteorological parameters was to quantify variability from local to regional scales within various snow environments and to archive forcing data for algorithm and model development and verification. The network included 10 main meteorological towers and one eddy covariance site. Data from these sites and other existing meteorological networks provided a high-quality dataset with nested spatial coverage. Additional spatial datasets were collected during
). FASST, a year-round state-of-the-ground model, was initially developed to provide information to mobility and sensor performance algorithms for military purposes. It has since been used in nonmilitary situations ( Holcombe 2004 ; Sawyer 2007 ; Frankenstein et al. 2007 ). FASST predicts soil moisture, ice and vapor content, and temperature as a function of depth as well as snow and ice accretion/depletion as a function of meteorological forcing and site characteristics. Incorporated into the model
). FASST, a year-round state-of-the-ground model, was initially developed to provide information to mobility and sensor performance algorithms for military purposes. It has since been used in nonmilitary situations ( Holcombe 2004 ; Sawyer 2007 ; Frankenstein et al. 2007 ). FASST predicts soil moisture, ice and vapor content, and temperature as a function of depth as well as snow and ice accretion/depletion as a function of meteorological forcing and site characteristics. Incorporated into the model
used in the model simulations take two primary forms: meteorological station data ( Elder et al. 2008b ) and atmospheric analysis data ( Liston et al. 2008 ). A network of ground-based meteorological observing stations was deployed as part of CLPX ( Elder et al. 2008b ). The objective of measuring meteorological parameters was to quantify variability from local to regional scales within various snow environments and to provide forcing data for algorithm and model development and verification. Ten
used in the model simulations take two primary forms: meteorological station data ( Elder et al. 2008b ) and atmospheric analysis data ( Liston et al. 2008 ). A network of ground-based meteorological observing stations was deployed as part of CLPX ( Elder et al. 2008b ). The objective of measuring meteorological parameters was to quantify variability from local to regional scales within various snow environments and to provide forcing data for algorithm and model development and verification. Ten
