Cold Land Processes Experiment Research Articles

Snow Surface Roughness across Spatio-Temporal Scales

The snow surface is at the interface between the atmosphere and Earth. The surface of the snowpack changes due to its interaction with precipitation, wind, humidity, short- and long-wave radiation, underlying terrain characteristics, and land cover. These connections create a dynamic snow surface that impacts the energy and mass balance of the snowpack, blowing snow potential, and other snowpack processes. Despite this, the snow surface is generally considered a constant parameter in many Earth system models. Data from the National Aeronautics and Space Administration (NASA) Cold Land Processes Experiment (CLPX) collected in 2002 and 2003 across northern Colorado were used to investigate the spatial and temporal variability of snow surface roughness. The random roughness (RR) and fractal dimension (D) metrics used in this investigation are well correlated. However, roughness is not correlated across scales, computed here from snow roughness boards at a millimeter resolution and airborne lidar at a meter resolution. Process scale differences were found based on land cover at each of the two measurement scales, as appraised through measurements in the forest and alpine.

Estimating alpine snow depth by combining multifrequency passive radiance observations with ensemble snowpack modeling

This paper presents a physically-based snow depth retrieval algorithm adapted for deep mountainous snowpack and airborne multifrequency (10.7, 18.7, 37.0 and 89.0 GHz) passive microwave (PM) radiance observations from a single flight. The algorithm employs a single forecast-analysis cycle of a traditional sequential assimilation scheme. It uses an ensemble of multi-layer snowpack model runs to resolve snow microstructure and melt-refreeze crusts, and microwave radiative transfer models to relate snow properties to microwave measurements. Snow depth was retrieved at a 120 m spatial resolution over three 1 km2 Intensive Study Areas (ISA) within the Rabbit Ears Meso-Cell Study Area (MSA) from the NASA Cold Land Processes Experiment (CLPX) in Colorado (United States) for one date in February 2003. When evaluated against in situ observations, root mean square error (RMSE) of the snow depth from the assimilation was 13.3 cm for areas with low (<5%) forest cover, which was a reduction of 48% in the RMSE compared with the modeled snow depth when the PM observations were not assimilated, indicating a ~5% relative error of the posterior snow depth with respect to the average snow depth (200 cm) measured at these pixels. For pixels with forest cover ranging from 5 to 15% and 15–30%, results were improved (R2 increased from 0.65 to 0.71 and from 0 to 0.38, respectively) by introducing a forest radiative transfer model during the assimilation.

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Abstract. Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage. Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

Characterizing Snowpack and the Freeze–Thaw State of Underlying Soil via Assimilation of Multifrequency Passive/Active Microwave Data: A Case Study (NASA CLPX 2003)

Ground-based passive microwave observations at 18.7- and 36.5-GHz frequencies and active microwave observations in L- [1.4 GHz] and Ku- [15.5 GHz] bands are used within an ensemble-based data assimilation (DA) framework to characterize the snow water equivalent (SWE) and the underlying soil freeze-thaw state (including soil surface temperature and both soil ice/liquid water content). The proposed framework is tested at the local-scale observation site of the National Aeronautics and Space Administration (NASA) Cold Land Processes Experiment field campaign during the third intensive observation period (February 18-26, 2003) for which the best set of collocated ground-based passive/active microwave observations, SWE, soil surface temperature, and moisture measurements are available. The DA approach effectively merges an a priori estimate of the soil freeze-thaw state and SWE generated by a land surface model (LSM) with information contained in passive/active microwave observations in order to overcome errors in the forcing data of LSM. Results indicate that the root-mean-square errors of SWE, soil surface temperature, and soil ice+liquid water content after the assimilation of passive (active) observations respectively decrease to 25.4 mm (22.8 mm), 0.61 K (0.52 K), and 0.063 (0.057) from 90.55 mm, 2.17 K, and 0.13 before assimilation, resulting in improvements of 75% (77%), 72% (76%), and 51% (56%). Also, it is found that the simultaneous assimilation of passive and active measurements further improves the estimates of SWE and soil temperature as well as soil ice/liquid water content, suggesting that there is an advantage offered by the synergistic use of passive and active measurements. Overall, the findings show that future studies can take advantage of remotely sensed microwave passive and active measurements from present and upcoming satellites such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), Soil Moisture Active Passive, and COld REgion Hydrology High-resolution Observatory (CoReH2O) for monitoring SWE and the underlying soil freeze-thaw state.

Evaluating Passive Microwave Radiometry for the Dynamical Transition From Dry to Wet Snowpacks

The microwave response of an idealized snowpack is evaluated for a change of the liquid water content (LWC) ranging from 0.0 m3 m-3 to 0.1 m3 m-3 , a realistic range for a dry to a wet snowpack transition. Next, the microwave radiometric behavior of a snowpack as a function of LWC is investigated using a coupled snow hydrology-microwave emission model that consists of a direct combination of multi-layer snow hydrology model and a forward model of microwave emission based on the simulated multi-layer snowpack. The microwave response during the transition is characterized by an initial increase of the brightness temperature (Tb) followed by a monotonic attenuation of Tb with a linear increase of LWC. Thus, the microwave response Tb to the increase in LWC exhibits a convex shape. The early amplification of Tb is caused by a sharp increase of the absorption coefficient attributed by a relatively small amount of LWC within the snowpack. This peak of Tb can be explained by the decrease in layer reflectivity and transmissivity in the Microwave Emission Model of Layered Snowpacks. The decrease of snow layer transmissivity indicates that the snowpack operates as an opaque medium in the microwave spectrum for small values of LWC. However, when LWC continues to increase, the increase of interface reflectivity at the atmosphere-snowpack interface begins to suppress the increasing Tb trend. As a result, the Tb subsequently decreases because the interface transmissivity, a complement of reflectivity, decreases asymptotically. This arched (convex) behavior of the Tb signal with increasing LWC is also exhibited by the Special Sensor Microwave Imager and the ground-based microwave radiometer observations concurrent with the presence of LWC in the simulated snowpack. For the validation of the Tb response to the LWC, simulations using a coupled snow hydrology-microwave emission model are compared against both passive microwave satellite and ground-based radiometer observations during the 2002-2003 Cold Land Processes Experiment (CLPX). Two Meso-cell Study Areas from CLPX are selected to evaluate the coupled model performances of Tb and snowpack physical properties in response to changes in LWC.

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as snow water equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions, but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment and the Helsinki University of Technology microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 and 37 GHz vertically polarised microwaves are consistent with advanced microwave scanning radiometer-earth observing system and special sensor microwave imager retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10-cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method, then it is equivalent to ±13 mm SWE (7 % of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.

The effect of spatial variability on the sensitivity of passive microwave measurements to snow water equivalent

Passive microwave (PM) remote sensing measurements are routinely utilized to estimate snow depth and water equivalent (SWE). Both vegetation and physical snowpack variables including snowpack grain size, snow depth, and stratigraphy influence the observed brightness temperature. The natural heterogeneity of snowpack and vegetation states within the microwave footprint occurs at spatial scales shorter than PM observation scales. In this study, we analyze the relationship between PM brightness temperature measurements and the heterogeneity of snowpack and vegetation. Specifically, we explore the question of whether PM observations are sensitive to changes in snow depth even given sub-pixel variability in snow and vegetation. To examine this question, densely sampled, spatially distributed in situ snow properties from multiple study areas made during the NASA Cold Land Processes Experiment (CLPX) are employed in a forward modeling scheme to study the effect of highly variable snow and vegetation properties on the observed PM measurement.In all test cases, this study finds that there exists sensitivity of microwave brightness temperature (Tb) to total snow depth contained within the measurement footprint, regardless of the heterogeneous nature of snow pack properties. Across three study areas, Tb decreases by 23–35K as depth increased up to the signal saturation depth, which ranged from 70 to 120cm. With regard to vegetation sensitivity, forest fractions (F) as little as 0.2 can modify the PM measurement by up to 10K, and F greater than 0.6 mask virtually all of the microwave signal attributable to snow. Finally, with respect to the measurement scale, our results indicate that the scale at which the PM measurement is made does not affect the sensitivity of the Tb to mean snow depth, to the scale (1km) examined herein.

Implications of Representing Snowpack Stratigraphy for the Assimilation of Passive Microwave Satellite Observations

Abstract Under certain conditions, passive microwave satellite observations can be used to estimate snow water equivalent (SWE) across large areas, either through direct retrieval or data assimilation. However, the layered character of snowpacks increases the complexities of estimation algorithms. A multilayer model of snowpack stratigraphy that can serve as the forward model of a snow data assimilation system is described and evaluated. The model’s ability to replicate large-scale snowpack layer features is evaluated using observations from the Cold Land Processes Experiment (Colorado, 2002 and 2003) and a 2002 Nome–Barrow snowpit transect [Snow Science Traverse—Alaska Region (SnowSTAR2002)]. The multilayer model linked with a radiative transfer scheme improved the estimation of brightness temperatures both in terms of absolute values and frequency/polarization differences (error reductions ranging from 47% to 72%) relative to a one-layer model with similar, but depth-averaged, physics at the Colorado sites. The two models were also employed along the SnowSTAR2002 transect of snowpit measurements. The general unavailability of meteorological forcings along the transect made the use of coarse-scale reanalysis data necessary to simulate snow properties and microwave radiances. Errors in the precipitation forcings led to overestimation of SWE, and the simulated brightness temperatures from the two models showed large differences, due mostly to the inability of the single-layer model to simulate the observed larger grain sizes. These differences had implications for the estimation of snow depth; assimilation of Special Sensor Microwave Imager (SSM/I) observations into the multilayer model resulted in improved snow depth estimates (RMSEs of 18.1 cm versus 34.1 cm without assimilation), while the single-layer assimilation slightly decreased the estimation skill (RMSEs of 34.1 versus 33.6 cm).

The need for prior information in characterizing snow water equivalent from microwave brightness temperatures

Microwave brightness temperatures (Tbs) contain information about snow properties, including the thickness, density, grain size, and temperature of stratigraphic layers; each of these properties generally varies vertically in the snowpack. Inverse solutions to characterize snow via Tbs have met with only limited success, not least because multiple sets of snow properties can lead to the same Tbs; the problem becomes more complex with increasing number of layers. Our objective is to address the question: Can an inverse solution be obtained with only very minimal prior information on snow properties under ideal conditions? We developed a new inverse algorithm: a Bayesian Markov Chain Monte Carlo scheme solved using the Metropolis algorithm. We allowed the number of snowpack layers itself to be unknown by generating different chains for each possible number of layers (up to a maximum of four), then selecting the optimal chain using a model selection criterion. We generated synthetic Tb observations using the microwave emission model for layered snow (MEMLS), and then used the Metropolis algorithm, MEMLS, and the synthetic observations to estimate the snow properties for 150 snowpits measurements made during the NASA Cold Land Processes Experiment (CLPX) field campaign. We evaluated algorithm results using the accuracy criteria on snow water equivalent (SWE) imposed for the planned CoreH2O satellite mission. We obtained an accurate solution to number of layers, layer thickness, density, grain size, snow temperature and ground temperature from microwave measurements for shallow snow (less than 300mm), with an RMSE of 18.2mm. We examined the sensitivity of these results to error in MEMLS, and found that for model error less than 8K standard deviation, the SWE RMSE is uniformly less than 30mm. For deep snow, however, the problem appears to be intractable, with RMS errors greater than 20% even for an assumption of zero model error. We conclude that for deep snow, additional prior information is required for accurate SWE estimation from microwave Tbs. We suggest that such prior information could come from physical snowpack models.

The effects of variation in snow properties on passive microwave snow mass estimation

Estimating snow mass at continental scales is difficult, but important for understanding land-atmosphere interactions, biogeochemical cycles and the hydrology of the Northern latitudes. Remote sensing provides the only consistent global observations, but with unknown errors. We test the theoretical performance of the Chang algorithm for estimating snow mass from passive microwave measurements using the Helsinki University of Technology (HUT) snow microwave emission model. The algorithm's dependence upon assumptions of fixed and uniform snow density and grainsize is determined, and measurements of these properties made at the Cold Land Processes Experiment (CLPX) Colorado field site in 2002–2003 used to quantify the retrieval errors caused by differences between the algorithm assumptions and measurements. Deviation from the Chang algorithm snow density and grainsize assumptions gives rise to an error of a factor of between two and three in calculating snow mass. The possibility that the algorithm performs more accurately over large areas than at points is tested by simulating emission from a 25km diameter area of snow with a distribution of properties derived from the snow pit measurements, using the Chang algorithm to calculate mean snow-mass from the simulated emission. The snow mass estimation from a site exhibiting the heterogeneity of the CLPX Colorado site proves only marginally different than that from a similarly-simulated homogeneous site. The estimation accuracy predictions are tested using the CLPX field measurements of snow mass, and simultaneous SSM/I and AMSR-E measurements.

A blended global snow product using visible, passive microwave and scatterometer satellite data

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.

Estimation of Snow Water Equivalence Using the Polarimetric Scanning Radiometer From the Cold Land Processes Experiments (CLPX03)

In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.

Active Remote Sensing of Snow Using NMM3D/DMRT and Comparison With CLPX II Airborne Data

We applied the Numerical Maxwell Model of three-dimensional simulations (NMM3D) in the Dense Media Radiative Theory (DMRT) to calculate backscattering coefficients. The particles' positions are computer-generated and the subsequent Foldy-Lax equations solved numerically. The phase matrix in NMM3D has significant cross-polarization, particularly when the particles are densely packed. The NMM3D model is combined with DMRT in calculating the microwave scattering by dry snow. The NMM3D/DMRT equations are solved by an iterative solution up to the second order in the case of small to moderate optical thickness. The numerical results of NMM3D/DMRT are illustrated and compared with QCA/DMRT. The QCA/DMRT and NMM3D/DMRT results are also applied to compare with data from two specific datasets from the second Cold Land Processes Experiment (CLPX II) in Alaska and Colorado. The data are obtained at the Ku-band (13.95 GHz) observations using airborne imaging polarimetric scatterometer (POLSCAT). It is shown that the model predictions agree with the field measurements for both co-polarization and cross-polarization. For the Alaska region, the average snow depth and snow density are used as the inputs for DMRT. The grain size, selected from within the range of the ground measurements, is used as a best-fit parameter within the range. For the Colorado region, we use the Variable Infiltration Capacity Model (VIC) to obtain the input snow profiles for NMM3D/DMRT.

Airborne Ku-Band Polarimetric Radar Remote Sensing of Terrestrial Snow Cover

Characteristics of the Ku-band polarimetric scatterometer (POLSCAT) data acquired from five sets of aircraft flights in the winter months of 2006-2008 for the second Cold Land Processes Experiment (CLPX-II) in Colorado are described in this paper. The data showed the response of the Ku-band radar echoes to snowpack changes for various types of background vegetation in the study site in north central Colorado. We observed about 0.15-0.5-dB increases in backscatter for every 1 cm of snow-water-equivalent (SWE) accumulation for areas with short vegetation (sagebrush and pasture). The region with the smaller amount of biomass, signified by the backscatter in November, seemed to have the stronger backscatter response to SWE in decibels. The data also showed the impact of surface hoar growth and freeze/thaw cycles, which created large snow-grain sizes, ice crust layers, and ice lenses and consequently increased the radar signals by a few decibels. The copolarized HH/VV backscatter ratio seems to indicate double-bounce scattering between the ground surface and snow or vegetation. The cross-polarized backscatter [vertical-horizontal (VH)] showed not only the influence of vegetation but also the strong response to snow accumulation. The observed HV/VV ratio suggests the importance of multiple scattering or nonspherical scattering geometry of snow grain in the dense-media radiative transfer scattering model. Comparison of the POLSCAT and QuikSCAT data was made and confirmed the effects of mixed terrain covers in the coarse-resolution QuikSCAT data.

Forest canopy height from the Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar

In this study retrievals of forest canopy height were obtained through adjustment of a simple geometric-optical (GO) model against red band surface bidirectional reflectance estimates from NASA's Multiangle Imaging SpectroRadiometer (MISR), mapped to a 250 m grid. The soil-understory background contribution was partly isolated prior to inversion using regression relationships with the isotropic, geometric, and volume scattering kernel weights of a Li-Ross kernel-driven bidirectional reflectance distribution function (BRDF) model. The height retrievals were assessed using discrete return lidar data acquired over sites in Colorado as part of the Cold Land Processes Experiment (CLPX) and used with fractional crown cover retrievals to obtain aboveground woody biomass estimates. For all model runs with reasonable backgrounds and initial b/r (vertical to horizontal crown radii) values < 2.0, root mean square error (RMSE) distributions were centered between 2.5 and 3.7 m while R 2 distributions were centered between 0.4 and 0.7. The MISR/GO aboveground biomass estimates predicted via regression on fractional cover and mean canopy height for the CLPX sites showed good agreement with U.S. Forest Service Interior West map data (adjusted R 2 = 0.84). The implication is that multiangle sensors such as MISR can provide spatially contiguous retrievals of forest canopy height, cover, and aboveground woody biomass that are potentially useful in mapping distributions of aboveground carbon stocks, tracking disturbance, and in initializing, constraining, and validating ecosystem models. This is important because the MISR record is spatially comprehensive and extends back to the year 2000 and the launch of the NASA Earth Observing System (EOS) Terra satellite; it might thus provide a ~ 10-year baseline record that would enhance exploitation of data from the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission, as well as furthering realization of synergies with active instruments.

Millimeter Microwave Emissivities and Effective Temperatures of Snow-Covered Surfaces: Evidence for Lambertian Surface Scattering

The remote sounding of atmospheric temperature and humidity via satelliteborne microwave instruments requires knowledge of the surface emissivity in order to separate the atmospheric and surface components of emission. To date, microwave sounding of the lower troposphere is only carried out over open sea surfaces because of large uncertainties in the surface emissivity and effective emitting temperature of other surfaces. In addition to this, recent literature has brought into doubt the assumption of specular reflection which is commonly used in retrieving surface emissivity. This paper presents analysis of airborne data in the 89-183-GHz frequency range taken over snow-covered surfaces during the second phase of the Cold Land Processes Experiment (CLPX-II). Two methods for retrieving emissivity and effective temperature using the three 183-GHz channels are introduced. Both methods give independent estimates of the emissivity and effective temperature. These methods can be applied to estimate emissivity under both specular reflection and diffuse scattering. Evaluation of emissivities under diffuse scattering requires the summing of contributions over the upper half sphere. An analysis of flight data from CLPX-II shows that Lambertian scattering gives much more consistent solutions for emissivities and effective temperatures than the traditional approach assuming specular reflection.

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene's illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixel-weighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 µm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode.

NASA Cold Land Processes Experiment (CLPX 2002/03): Ground-Based and Near-Surface Meteorological Observations

Abstract A short-term meteorological database has been developed for the Cold Land Processes Experiment (CLPX). This database includes meteorological observations from stations designed and deployed exclusively for CLPX as well as observations available from other sources located in the small regional study area (SRSA) in north-central Colorado. The measured weather parameters include air temperature, relative humidity, wind speed and direction, barometric pressure, short- and longwave radiation, leaf wetness, snow depth, snow water content, snow and surface temperatures, volumetric soil moisture content, soil temperature, precipitation, water vapor flux, carbon dioxide flux, and soil heat flux. The CLPX weather stations include 10 main meteorological towers, 1 tower within each of the nine intensive study areas (ISA) and one near the local scale observation site (LSOS); and 36 simplified towers, with one tower at each of the four corners of each of the nine ISAs, which measured a reduced set of parameters. An eddy covariance system within the North Park mesocell study area (MSA) collected a variety of additional parameters beyond the 10 standard CLPX tower components. Additional meteorological observations come from a variety of existing networks maintained by the U.S. Forest Service, U.S. Geological Survey, Natural Resource Conservation Service, and the Institute of Arctic and Alpine Research. Temporal coverage varies from station to station, but it is most concentrated during the 2002/03 winter season. These data are useful in local meteorological energy balance research and for model development and testing. These data can be accessed through the National Snow and Ice Data Center Web site.

NASA Cold Land Processes Experiment (CLPX 2002/03): Field Measurements of Snowpack Properties and Soil Moisture

Abstract A field measurement program was undertaken as part NASA’s Cold Land Processes Experiment (CLPX). Extensive snowpack and soil measurements were taken at field sites in Colorado over four study periods during the two study years (2002 and 2003). Measurements included snow depth, density, temperature, grain type and size, surface wetness, surface roughness, and canopy cover. Soil moisture measurements were made in the near-surface layer in snow pits. Measurements were taken in the Fraser valley, North Park, and Rabbit Ears Pass areas of Colorado. Sites were chosen to gain a wide representation of snowpack types and physiographies typical of seasonally snow-covered regions of the world. The data have been collected with rigorous protocol to ensure consistency and quality, and they have undergone several levels of quality assurance to produce a high-quality spatial dataset for continued cold lands hydrological research. The dataset is archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

NASA Cold Land Processes Experiment (CLPX 2002/03): Airborne Remote Sensing

Abstract This paper describes the airborne data collected during the 2002 and 2003 Cold Land Processes Experiment (CLPX). These data include gamma radiation observations, multi- and hyperspectral optical imaging, optical altimetry, and passive and active microwave observations of the test areas. The gamma observations were collected with the NOAA/National Weather Service Gamma Radiation Detection System (GAMMA). The CLPX multispectral optical data consist of very high-resolution color-infrared orthoimagery of the intensive study areas (ISAs) by TerrainVision. The airborne hyperspectral optical data consist of observations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Optical altimetry measurements were collected using airborne light detection and ranging (lidar) by TerrainVision. The active microwave data include radar observations from the NASA Airborne Synthetic Aperture Radar (AIRSAR), the Jet Propulsion Laboratory’s Polarimetric Ku-band Scatterometer (POLSCAT), and airborne GPS bistatic radar data collected with the NASA GPS radar delay mapping receiver (DMR). The passive microwave data consist of observations collected with the NOAA Polarimetric Scanning Radiometer (PSR). All of the airborne datasets described here and more information describing data collection and processing are available online.