Snow-covered Terrain Research Articles

Comparison of simulation models NRMM and NTVPM for assessing military tracked vehicle cross-country performance

Mobility of ground vehicles is an important issue for defence operations. In the United States and some other NATO countries, the NATO Reference Mobility Model (NRMM) is currently used to evaluate military ground vehicle mobility. The cross-country performance prediction module of NRMM is based on empirical relations established with test data primarily collected decades ago. It has inherent limitations, such as the uncertainty whether the empirical relations can be extrapolated beyond the test conditions upon which they were derived. This paper describes a comparison of the empirically-based NRMM with the physics-based Nepean Tracked Vehicle Performance Model (NTVPM) for assessing the cross-country performance of military tracked vehicles. It examines the soundness of the methodologies of NRMM and NTVPM, the adequacy of the vehicle design parameters and terrain characteristics that they take into account, the user friendliness of their operations, and the correlations between the measured cross-country performance of a notional tracked vehicle (an armoured personnel carrier) and that predicted by the two simulation models on sandy terrain, muskeg and snow-covered terrain. Based on the results of the evaluation, it appears that the physics-based NTVPM has potential as the basis for the development of the next generation simulation model for assessing the cross-country performance of military tracked vehicles.

Evaluation of the computer simulation model NTVPM for assessing military tracked vehicle cross-country mobility

In the United States and some other NATO (North Atlantic Treaty Organization) countries, the NATO Reference Mobility Model is currently used to evaluate military ground vehicle mobility. The module of the NATO Reference Mobility Model for predicting the cross-country performance of military vehicles is empirically based and was developed using test data collected decades ago. The NATO Reference Mobility Model has inherent limitations, such as the uncertainty whether its empirical relations can be extrapolated beyond the test conditions upon which they were derived or whether it can be used for evaluating new-generation military vehicles. This suggests that there is a need for the development of a physics-based model that takes into account the advancements in terramechanics and modelling/simulation techniques. This paper describes the results of a detailed evaluation of a physics-based model – the Nepean Tracked Vehicle Performance Model – for assessing military tracked vehicle cross-country performance. The performance of a notional tracked vehicle (an armoured personnel carrier) predicted by the latest version of the Nepean Tracked Vehicle Performance Model is compared with test data obtained on sandy terrain, muskeg and snow-covered terrain. The correlations between the predicted and measured performance are evaluated using the coefficient of correlation, coefficient of determination, root mean square deviation and coefficient of variation. The applications of the Nepean Tracked Vehicle Performance Model to predicting the maximum possible vehicle speed (speed-made-good) on a given terrain, the sensitivity of vehicle performance to variations in the values of terrain parameters and the mean maximum pressure are demonstrated. The results of this study indicate that the Nepean Tracked Vehicle Performance Model has potential to form the basis for the development of the next-generation cross-country performance assessment methodology for military tracked vehicles.

Optimization of Noah and Noah_MP WRF Land Surface Schemes in Snow-Melting Conditions over Complex Terrain

The paper presents the results of high-resolution simulations performed with the WRF Model, coupled with two different land surface schemes, Noah and Noah_MP, with the aim of accurately reproducing winter season meteorological conditions in a typical Alpine valley. Accordingly, model results are compared against data collected during an intensive field campaign performed in the Adige Valley, in the eastern Italian Alps. In particular, the ability of the model in reproducing the time evolution of 2-m temperature and of incoming and outgoing shortwave and longwave radiation is examined. The validation of model results highlights that, in this context, WRF reproduces rather poorly near-surface temperature over snow-covered terrain, with an evident underestimation, during both daytime and nighttime. Furthermore it fails to capture specific atmospheric processes, such as the temporal evolution of the ground-based thermal inversion. The main cause of these errors lies in the miscalculation of the mean gridcell albedo, resulting in an inaccurate estimate of the reflected solar radiation calculated by both Noah and Noah_MP. Therefore, modifications to the initialization, to the land-use classification, and to both land surface models are performed to improve model results, by intervening in the calculation of the albedo, of the snow cover, and of the surface temperature. Qualitative and quantitative analyses show that, after these changes, a significant improvement in the comparability between model results and observations is achieved. In particular, outgoing shortwave radiation is lowered, 2-m temperature maxima increased accordingly, and ground-based thermal inversions are better captured.

Atmospheric and Forest Decoupling of Passive Microwave Brightness Temperature Observations Over Snow-Covered Terrain in North America

This study addresses two significant sources of uncertainty prevalent in snow water equivalent (SWE) retrievals derived from advanced microwave scanning radiometer (AMSR-E) passive microwave (PMW) brightness temperature (Tb) observations at 18.7 and 36.5 GHz. Namely, atmospheric and overlying forest effects are decoupled from the original AMSR-E PMW Tb observations using relatively simple, physically based radiative transfer models. Comparisons against independent Tb measurements collected during airborne PMW Tb surveys highlight the effectiveness of the proposed AMSR-E atmospheric decoupling procedure. The atmospheric contribution to Tb ranges from 1 to 3 K depending on the frequency and polarization measured as well as meteorologic conditions at the time of AMSR-E overpass. It is further shown that forest decoupling should be conducted as a function of both land cover type and snow cover class. The exponential decay relationship between the forest structure parameter, namely, MODIS-derived leaf area index (LAI) and forest transmissivity, is fitted across snow-covered terrain in North America. The fitted exponential function can be utilized during forest decoupling activities for evergreen needle-leaved forest and woody savanna regions, but remains uncertain in other forest types due to a sparsity of snow-covered areas. By removing forest-related Tb contributions from the original AMSR-E observations, the results suggest that Tb spectral difference between 18.7 and 36.5 GHz, in general, increases across thinly vegetated to heavily vegetated regions, which can be beneficial when applied to traditional SWE retrieval algorithms.

Fully-automated estimation of snow depth in near real time with the use of unmanned aerial vehicles without utilizing ground control points

A new method for on-demand production of a numerical snow depth map, based on observations carried out in near real time by an unmanned aerial vehicle (UAV), is elaborated. The novelty of the approach resides in the neglection of artificial ground control points (GCPs). The method is based on processing oblique aerial geotagged images of snow-covered terrain taken with visible-light (RGB) camera installed on UAV. The compressed photographs (JPG format) are pre-processed with the use of parallel computing in order to resize the images. The structure-from-motion (SfM) procedure – herein based on the RunSFM solution – is utilized to generate a non-georeferenced dense point cloud. The subsequent automatic georeferencing procedure comprises two stages: (1) initial registration of the snow-covered dense point cloud using the Helmert transformation, the parameters of which are estimated from camera positions in local (image) and Universal Transverse Mercator (UTM) coordinate systems, (2) final registration of the snow-covered dense point cloud with the iterative closest point (ICP) algorithm applied in respect to a reference snow-free and highly accurate dense point cloud. The second phase is based on the automated ICP application to two subsets of these dense point clouds which include only tall land cover elements, mainly trees. This enables the ICP-based transformation of the source (with snow) dense point cloud into the reference one (snow-free). The two dense point clouds are subsequently interpolated to digital surface models (DSMs), the difference of which forms a numerical map of the estimated snow depth. The procedure is tested on the RGB images taken by the micro fixed-wing UAV in the Izerskie Mountains in southwestern Poland. In addition, a reproducibility test, which compares the above-mentioned procedure with its replica based on modified settings (NIR camera, lower flight altitude, bigger number of images, higher resolution, SfM reconstruction produced with Theia and OpenMVS), is also performed. The estimation is validated against spatially-distributed snow depth measurements. It is found that the method resolves snow depth with a correct order of magnitude. Mean absolute error (MAE) of snow depth estimation varied between 0.33 and 0.43m, whereas measured snow depths were between 0.24 and 1.06m (mean of 0.41m). Medium level of reproducibility is reported, with considerable similarity in statistics of snow depth estimation and only partial (local) agreement between spatial patterns of estimated and measured snow depth fields. In addition, the results show potential for using NIR images in producing realistic estimated snow depth surfaces.

The effect of vernal solar UV radiation on serum 25-hydroxyvitamin D concentration depends on the baseline level: observations from a high latitude in Finland

ABSTRACTHumans obtain vitamin D from conversion of 7-dehydrocholesterol in the skin by ultraviolet B (UVB) radiation or from dietary sources. As the radiation level is insufficient in winter, vitamin D deficiency is common at higher latitudes. We assessed whether vernal solar UVB radiation at latitudes 61°N and 67°N in Finland has an impact on serum 25-hydroxyvitamin D [S-25(OH)D] concentrations. Twenty-seven healthy volunteers participated in outdoor activities in snow-covered terrain for 4–10 days in March or April, with their face and hands sun-exposed. The personal UVB doses and S-25(OH)D levels were monitored. A mean UVB dose of 11.8 standard erythema doses (SED) was received during an average of 12.3 outdoor hours. The mean S-25(OH)D concentration in subjects with a baseline concentration below 90.0 nmol/L (n=13) increased significantly, by 6.0 nmol/L from an initial mean of 62.4 nmol/L (p<0.001), whereas in those with a basal concentration above 90.0 nmol/L (n=12) it decreased significantly, by 6.7 nmol/L from a mean of 116.9 nmol/L (p<0.01). To conclude, only 7% of total body surface area was exposed to vernal sunlight and this was capable of increasing S-25(OH)D levels in subjects with a baseline level below 90 nmol/L but not in those with higher levels.

LiDAR remote sensing of the cryosphere: Present applications and future prospects

The cryosphere consists of frozen water and includes lakes/rivers/sea ice, glaciers, ice caps/sheets, snow cover, and permafrost. Because highly reflective snow and ice are the main components of the cryosphere, it plays an important role in the global energy balance. Thus, any qualitative or quantitative change in the physical properties and extents of the cryosphere affects global air circulation, ocean and air temperatures, sea level, and ocean current patterns. Due to the hardships involved in collecting ground control points and field data for high alpine glaciers or vast polar ice sheets, several researchers are currently using remote sensing. Satellites provide an effective space-borne platform for remotely sensing frozen areas at the global and regional scales. However, satellite remote sensing has several constraints, such as limited spatial and temporal resolutions and expensive data acquisition. Therefore, aerial and terrestrial remote sensing platforms and sensors are needed to cover temporal and spatial gaps for comprehensive cryospheric research. Light Detection and Ranging (LiDAR) antennas form a group of active remote sensors that can easily be deployed on all three platforms, i.e., satellite, aerial, and terrestrial. The generation of elevation data for glacial and snow-covered terrain from photogrammetry requires high contrast amongst various reflective surfaces (ice, snow, firn, and slush). Conventional passive optical remote sensors do not provide the necessary accuracy, especially due to the unavailability of reliable ground control points. However, active LiDAR sensors can fill this research gap and provide high-resolution and accurate Digital Elevation Models (DEMs). Due to the obvious advantages of LiDAR over conventional passive remote sensors, the number of LiDAR-based cryospheric studies has increased in recent years. In this review, we highlight studies that have utilised LiDAR sensors for the cryospheric research of various features, such as snow cover, polar ice sheets and their atmospheres, alpine glaciers, and permafrost. Because this technology shows immense promise for applications in future cryospheric research, we also emphasise the prospects of utilising LiDAR sensors. In this paper, a large compilation of relevant references is presented to allow readers to explore particular topics of interest.

The Effect of Boreal Forest Canopy in Satellite Snow Mapping—A Multisensor Analysis

Satellite-based snow-cover monitoring is performed using optical, synthetic aperture radar (SAR), and passivemicrowave sensors. Effects of forest canopy on the observed signal need to be considered with all of these sensor types. Various models describing the interaction of electromagnetic radiation with forest canopy have been developed, but many of these are overly complex with high computational and ancillary data requirements. However, for retrieval purposes, simple models are preferred. This work aims at increasing the understanding of the effect of forest canopy on remote sensing observations of snow-covered terrain for both microwave and optical regimes and at quantifying the capability of simple zeroth-order models in simulating these effects. To achieve these goals, a spatial analysis of optical, SAR, and passive-microwave remote sensing data in the northern boreal forest region was performed. Model parameters for vegetation transmissivity as well as the properties of the underlying surface were optimized by utilizing lidar-ranging- and Landsat-based simplified proxy parameters describing forest canopy closure and stem volume. The results demonstrated that despite using these relatively simple proxies, a zeroth-order model can accurately estimate the extinction of electromagnetic signals in a forest, particularly for passive microwave and optical data. The SAR model successfully estimated the median of the observations, but larger scatter of the observations was reflected by a higher root mean square error and lower correlation between models and observations. Due to both good estimation accuracy and simplicity, the presented models can be considered to be applicable in existing snow retrieval algorithms.

Thermal mapping of self-heating zones on coal waste dumps in Upper Silesia (Poland) — A case study

The evolution of self-heating areas on five chosen coal-waste dumps in Upper Silesia (Poland) was quantitatively investigated with the aim of finding a cheap and fast method of detecting and localising coal-waste fires on the dumps. The results show that Landsat 4–5 TM and ETM+ images from snow-covered terrain enable the burning areas of the dumps to be seen and that, in addition, they can be used to observe the evolution of these thermal anomalies. Furthermore, the analysis showed that the external temperature had an impact on surfaces of self-heating areas; when the air-temperature was low, the surface temperatures of these hot areas were lowered. In the study, it managed to observe the evolution of self-heating areas, i.e., the appearance of hot spots, their migration and their disappearance were observed. The method used is relatively rapid, low-cost and field temperature measurements are not essential.

Finite element modeling of interfacial forces and contact stresses of pneumatic tire on fresh snow for combined longitudinal and lateral slips

Significant challenges exist in the prediction of interaction forces generated from the interface between pneumatic tires and snow-covered terrains due to the highly non-linear nature of the properties of flexible tires, deformable snow cover and the contact mechanics at the interface of tire and snow. Operational conditions of tire–snow interaction are affected by many factors, especially interfacial slips, including longitudinal slip during braking or driving, lateral slip (slip angle) due to turning, and combined slip (longitudinal and lateral slips) due to brake-and-turn and drive-and-turn maneuvers, normal load applied on the wheel, friction coefficient at the interface and snow depth. This paper presents comprehensive three-dimensional finite element simulations of tire–snow interaction for low-strength snow under the full-range of controlled longitudinal and lateral slips for three vertical loads to gain significant mechanistic insight. The pneumatic tire was modeled using elastic, viscoelastic and hyperelastic material models; the snow was modeled using the modified Drucker–Prager Cap material model (MDPC). The traction, motion resistance, drawbar pull, tire sinkage, tire deflection, snow density, contact pressure and contact shear stresses were obtained as a function of longitudinal slip and lateral slip. Wheel states – braked, towed, driven, self-propelled, and driving – have been identified and serve as key classifiers of discernable patterns in tire–snow interaction such as zones of contact shear stresses. The predicted results can be applied to analytical deterministic and stochastic modeling of tire–snow interaction.

Detection and Measurement of Snowfall from Space

Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize the measurement of rainfall with the addition of several complications, such as the indirect character of remote sensing precipitation estimation, the presence of frozen or snow-covered terrain, and the unknown vertical distribution of hydrometeors in the cloud column. Several methods for the retrieval of snowfall intensity from satellite have been proposed in recent times using passive and active sensors. No satisfactory answer to the general problem of quantitative snowfall intensity determination has been found to date, but several studies contribute to delineate a working framework for the future operational retrieval algorithms.

Development of a tool for modeling snowmobile and snowcoach noise in Yellowstone and Grand Teton National Parks

The National Park Service (NPS) develops winter use plans for Yellowstone and Grand Teton National Parks to help manage the use of Over-Snow Vehicles (OSVs), such as snowmobiles and snowcoaches. The use and management of OSVs in the parks is an issue because of potential environmental impacts and because of actions by environmental, recreational, and commercial groups. The U.S. Department of Transportation, Research and Innovative Technology Administration, John A. Volpe National Transportation Systems Center (the Volpe Center) supported the NPS by modeling the acoustical environment in the parks associated with potential modeling alternatives as well as current and historical conditions. The modeling considered a number of alternatives for inclusion in the NPS's winter use plans. These alternatives affect the type and number of OSVs that are allowed to operate in the parks and where they are allowed to travel. The acoustical modeling was performed by using the Federal Aviation Administration's (FAA) Integrated Noise Model (INM), adapted for use with OSVs. INM adaptation included the development of an over-ground sound propagation model to account for propagation over snow-covered terrain. The Volpe Center also developed a new OSV noise database, which defined OSV noise as a function of speed and source-to-receiver distance, based on previously published OSV acoustical studies and winter 2005-2006 measurements. Vehicle types modeled included two- and four-stroke snowmobiles as well as two- and four-track snowcoaches

Assessment of Cloud Cover Characteristics in Satellite Datasets and Reanalysis Products for Greenland

Abstract Clouds have an important controlling influence on the radiation balance, and hence surface melting, over the Greenland ice sheet and need to be classified to derive reliable albedo estimates from visible imagery. Little is known, however, about the true cloud cover characteristics for the largest island on Earth, Greenland. Here, an attempt is made to address this knowledge gap by examining cloud characteristics, as determined by three complementary satellites sensors: the Advanced Very High Resolution Radiometer (AVHRR), the Along Track Scanning Radiometer-2 (ATSR-2), and the Moderate Resolution Imaging Spectroradiometer (MODIS). The first provides a multidecadal time series of clouds, albedo, and surface temperature, and is available, in the form of the extended AVHRR Polar Pathfinder dataset (APP-x), as a homogeneous, consistent dataset from 1982 until 2004. APP-x data, however, are also the most challenging to cloud classify over snow-covered terrain, due to the limited spectral capabilities of the instrument. ATSR-2 permits identification and classification using stereophotogrammetric techniques and MODIS has enhanced spectral sampling in the visible and thermal infrared but over more limited time periods. The spatial cloud fractions from the three sensors are compared and show good agreement in terms of both magnitude and spatial pattern. The cloud fractions, and inferred patterns of accumulation, are then assessed from three commonly used reanalysis datasets: NCEP–NCAR, the second NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II), and the 40-yr ECMWF Re-Analysis (ERA-40). Poor agreement between the reanalysis datasets is found. NCEP–DOE AMIP-II produces a cloud fraction similar to that observed by the satellites. NCEP–NCAR and ERA-40, however, bear little similarity to the cloud fractions derived from the satellite observations. This suggests that they may produce poor accumulation estimates over the ice sheet and poor estimates of radiation balance. Using these reanalysis data to force a mass balance model of the ice sheet, without appropriate downscaling and correction for the substantial biases present, may, therefore, produce substantial errors in surface melt rate estimates.

Airborne Retrievals of Snow Microwave Emissivity at AMSU Frequencies Using ARTS/SCEM-UA

Abstract The remote sounding, by satellite, of atmospheric temperature and humidity is an important source of data for assimilation into operational weather forecasting routines. For retrievals of these variables near the surface, wavebands with low optical depths are monitored to allow penetration through the overlying atmosphere. Brightness temperatures in these relatively transparent bands are also sensitive to the land surface emissivity and effective temperature. Inadequate understanding of these land surface emissivities is a major issue when assimilating Advanced Microwave Sounding Unit data for the land-covered portion of the globe. One approach for estimating the emissivity of snow-covered surfaces is an empirical model derived from satellite-based and land-based retrievals of emissivity for a variety of snow types. The Met Office’s Hercules C-130 aircraft flew over snow-covered Arctic terrain of northern Finland during the Polar Experiment (POLEX) of March 2001. On these flights, microwave radiometers provided microwave brightness temperatures at 23.8, 50.3, 89.0, 157, and 183 GHz. The work presented here uses these data along with a robust multiparameter optimization routine [Shuffled Complex Evolution Metropolis (SCEM-UA)] coupled to the Atmospheric Radiative Transfer Simulator (ARTS) to retrieve emissivities at the measured frequencies. These results are then used to validate an empirical model. This latter model predicts 23.8–157-GHz emissivities with an RMSE of less than 0.02 and bias of less than 0.01 when compared with data at an incidence angle of 40°. Nonmonotonic behavior in the emissivity spectrum for this campaign, reported in earlier work, is confirmed by the retrievals presented here.

Assessment of winter fluxes of CO2 and CH4 in boreal forest soils of central Alaska estimated by the profile method and the chamber method: a diagnosis of methane emission and implications for the regional carbon budget

This research was carried out to estimate the winter fluxes of CO2 and CH4 using the concentration profile method and the chamber method in black spruce forest soils in central Alaska during the winter of 2004/5. The average winter fluxes of CO2 and CH4 by chamber and profile methods were 0.24 ± 0.06 (SE; standard error) and 0.21 ± 0.06 gCO2-C/m2/d, and 21.4 ± 5.6 and 21.4 ± 14 μgCH4-C/m2/hr. This suggests that the fluxes estimated by the two methods are not significantly different based on a one-way ANOVA with a 95% confidence level. The hypothesis on the processes of CH4 transport/production/emission in underlying snow-covered boreal forest soils is proven by the pressure differences between air and in soil at 30 cm depth. The winter CO2 emission corresponds to 23% of the annual CO2 emitted from Alaska black spruce forest soils, which resulted in the sum of mainly root respiration and microbial respiration during the winter based on the δ13CO2 of .22.5‰. The average wintertime emissions of CO2 and CH4 were 49 ± 13 gCO2-C/m2/season and 0.11 ± 0.07 gCH4-C/m2/season, respectively. This implies that winter emissions of CO2 and CH4 are an important part of the annual carbon budget in seasonally snow-covered terrain of typical boreal forest soils.

Feasibility of multi-temporal interferometric SAR data for stand-level estimation of boreal forest stem volume

The feasibility of interferometric SAR (INSAR) coherence observations for stem volume (biomass) retrieval is investigated by applying coherence data determined from 14 ERS-1 and ERS-2 C-band SAR image pairs. The image set covers a single forested test area in Finland, and both summer (snow-free) and winter conditions are represented. The data set enabled (a) the study of stem volume retrieval performance under varying conditions, (b) the analysis of the seasonal behavior of interferometric coherence, and (c) the determination of the accuracy characteristics of empirical (nonlinear) coherence modeling. Additionally, a new technique to estimate forest stem volume from INSAR data was developed based on constrained iterative inversion of the applied empirical model. The results indicate that the usability of winter images with snow-covered terrain is superior to that of images obtained under summer conditions. The applied empirical model appears to be adequate for describing the stand-wise coherence of boreal forest. Hence, a practical stem volume estimation method can be established based on it. The highest correlation coefficient between the estimated stem volume and the ground truth stem volume showed values as high as r=0.9 and a relative RMSE level of 48% was obtained, respectively.

Version 2 total ozone mapping spectrometer ultraviolet algorithm: problems and enhancements

Satellite instruments provide global maps of surface UV irradiance by combining backscattered radiance measurements with radiative transfer models. The accuracy of the models is limited by uncertainties in input parameters representing the atmosphere and the Earth's surface. To reduce these uncertainties, we have made enhancements to the currently operational TOMS surface UV irradiance algorithm (Version 1) by including the effects of diurnal variations of cloudiness, an improved treatment of snow/ice, and a preliminary aerosol correction. We compare results of the version 1 TOMS UV algorithm and the proposed version. We evaluate different approaches for improved treatment for average cloud attenuation within a satellite pixel, with and without snow/ ice on the ground. In addition to treating cloud transmission based only on the measurements at the local time of the TOMS observations, the results from other satellites and weather assimilation models can be used to estimate atmospheric UV irradiance transmission throughout the day. A new method is proposed to obtain a more realistic treatment of the effects from snow-covered terrain. The method is based on an empirical relation between UV reflectivity and measured snow depth. The new method reduces the bias between the TOMS UV estimations and ground-based UV measurements for snow periods. We also briefly discuss the complex problem of estimating surface UV radiation in presence of UV-absorbing aerosols. The improved (Version 2) algorithm can be applied to reprocess the existing TOMS UV irradiance and exposure estimates (since November 1978) and to future satellite sensors (e.g., GOME-2, OMI on EOS/Aura, and Triana/EPIC).

Determination of snow-covered area in different land covers in central Alaska, U.S.A., from aircraft data — April 1995

During April 1995, a field and aircraft experiment was conducted in central Alaska in support of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-mapping project. The MODIS Airborne Simulator (MAS), a 50 channel spectroradiometer, was flown on board the NASA ER-2 aircraft. An objective of the mission was to determine the accuracy of mapping snow in different surface covers using an algorithm designed to map global snow cover after the launch of MODIS in 1998. The surface cover in this area of central Alaska is typically spruce, birch, aspen, mixed forest and muskeg. Integrated reflectance,Riwas calculated from the visible/near-infrared channels of the MAS sensor. TheRiwas used to estimate different vegetation-cover densities because there is an inverse relationship between vegetation-cover density and albedo in snow-covered terrain. A vegetation-cover density map was constructed using MAS data acquired on 13 April 1995 over central Alaska. In the part of the scene that was mapped as having a vegetation-cover density of &amp;lt; 50%, the snow-mapping algorithm mapped 96.41% snow cover. These areas are generally composed of muskeg and mixed forests and include frozen lake. In the part of the scene that was estimated to have a vegetation-cover density of ≥50%, the snow-mapping algorithm mapped 71.23% snow cover. These areas are generally composed of dense coniferous or deciduous forests. Overall, the accuracy of the snow-mapping algorithm is &amp;gt; 87.41% for a 13 April MAS scene with a variety of surface covers (coniferous and deciduous and mixed forests, muskeg, tundra and frozen lake).

Determination of snow-covered area in different land covers in central Alaska, U.S.A., from aircraft data — April 1995

During April 1995, a field and aircraft experiment was conducted in central Alaska in support of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-mapping project. The MODIS Airborne Simulator (MAS), a 50 channel spectroradiometer, was flown on board the NASA ER-2 aircraft. An objective of the mission was to determine the accuracy of mapping snow in different surface covers using an algorithm designed to map global snow cover after the launch of MODIS in 1998. The surface cover in this area of central Alaska is typically spruce, birch, aspen, mixed forest and muskeg. Integrated reflectance, Ri was calculated from the visible/near-infrared channels of the MAS sensor. The Ri was used to estimate different vegetation-cover densities because there is an inverse relationship between vegetation-cover density and albedo in snow-covered terrain. A vegetation-cover density map was constructed using MAS data acquired on 13 April 1995 over central Alaska. In the part of the scene that was mapped as having a vegetation-cover density of &lt; 50%, the snow-mapping algorithm mapped 96.41% snow cover. These areas are generally composed of muskeg and mixed forests and include frozen lake. In the part of the scene that was estimated to have a vegetation-cover density of ≥50%, the snow-mapping algorithm mapped 71.23% snow cover. These areas are generally composed of dense coniferous or deciduous forests. Overall, the accuracy of the snow-mapping algorithm is &gt; 87.41% for a 13 April MAS scene with a variety of surface covers (coniferous and deciduous and mixed forests, muskeg, tundra and frozen lake).

SNOW COVER CHARACTERIZATION USING MULTIBAND FMCW RADARS

The use of radars to characterize the physical properties of a snow cover offers an attractive alternative to manual snow pit measurements. Radar techniques are non-invasive and have the potential to cover large areas of a snow-covered terrain. A promising radar technique for snow cover studies is the frequency modulated continuous wave (FMCW) radar. The use of a multiband radar approach for snow cover studies was investigated in order to fully exploit the capabilities of FMCW radars. FMCW radars operating at and near the C-, X- and Ka-bands were used to obtain radar profiles over a wide range of snow cover conditions. These frequency-dependent radar signatures were used to identify important snow cover features such as ice and depth hoar layers. Snow grain size information was also obtained from the frequency-dependent scattering losses that were observed in the snow cover. Several case studies of FMCW radar profiles are presented in order to demonstrate the advantages of a multiband radar approach for monitoring the spatial and temporal variability of snow cover properties and/or processes over an extended area.