Snow Fraction Research Articles

Monitoring snow cover variability (2000–2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method

Monitoring the variability of snow cover is necessary and meaningful because snow cover is closely connected with climate and ecological change. In this work, 500m resolution MODIS daily snow cover products from 2000 to 2014 were adopted to analyze the status in Hengduan Mountains. In order to solve the spatial discontinuity caused by clouds in the products, we propose an adaptive spatio-temporal weighted method (ASTWM), which is based on the initial result of a Terra and Aqua combination. This novel method simultaneously considers the temporal and spatial correlations of the snow cover. The simulated experiments indicate that ASTWM removes clouds completely, with a robust overall accuracy (OA) of above 93% under different cloud fractions. The spatio-temporal variability of snow cover in the Hengduan Mountains was investigated with two indices: snow cover days (SCD) and snow fraction. The results reveal that the annual SCD gradually increases and the coefficient of variation (CV) decreases with elevation. The pixel-wise trends of SCD first rise and then drop in most areas. Moreover, intense intra-annual variability of the snow fraction occurs from October to March, during which time there is abundant snow cover. The inter-annual variability, which mainly occurs in high elevation areas, shows an increasing trend before 2004/2005 and a decreasing trend after 2004/2005. In addition, the snow fraction responds to the two climate factors of air temperature and precipitation. For the intra-annual variability, when the air temperature and precipitation decrease, the snow cover increases. Besides, precipitation plays a more important role in the inter-annual variability of snow cover than temperature.

Heterocyclic Aromatics in Petroleum Coke, Snow, Lake Sediments, and Air Samples from the Athabasca Oil Sands Region.

The aromatic fractions of snow, lake sediment, and air samples collected during 2011-2014 in the Athabasca oil sands region were analyzed using two-dimensional gas chromatography following a nontargeted approach. Commonly monitored aromatics (parent and alkylated-polycyclic aromatic hydrocarbons and dibenzothiophenes) were excluded from the analysis, focusing mainly on other heterocyclic aromatics. The unknowns detected were classified into isomeric groups and tentatively identified using mass spectral libraries. Relative concentrations of heterocyclic aromatics were estimated and were found to decrease with distance from a reference site near the center of the developments and with increasing depth of sediments. The same heterocyclic aromatics identified in snow, lake sediments, and air were observed in extracts of delayed petroleum coke, with similar distributions. This suggests that petroleum coke particles are a potential source of heterocyclic aromatics to the local environment, but other oil sands sources must also be considered. Although the signals of these heterocyclic aromatics diminished with distance, some were detected at large distances (>100 km) in snow and surface lake sediments, suggesting that the impact of industry can extend >50 km. The list of heterocyclic aromatics and the mass spectral library generated in this study can be used for future source apportionment studies.

Snow contribution to first‐year and second‐year Arctic sea ice mass balance north of Svalbard

AbstractThe salinity and water oxygen isotope composition (δ18O) of 29 first‐year (FYI) and second‐year (SYI) Arctic sea ice cores (total length 32.0 m) from the drifting ice pack north of Svalbard were examined to quantify the contribution of snow to sea ice mass. Five cores (total length 6.4 m) were analyzed for their structural composition, showing variable contribution of 10–30% by granular ice. In these cores, snow had been entrained in 6–28% of the total ice thickness. We found evidence of snow contribution in about three quarters of the sea ice cores, when surface granular layers had very low δ18O values. Snow contributed 7.5–9.7% to sea ice mass balance on average (including also cores with no snow) based on δ18O mass balance calculations. In SYI cores, snow fraction by mass (12.7–16.3%) was much higher than in FYI cores (3.3–4.4%), while the bulk salinity of FYI (4.9) was distinctively higher than for SYI (2.7). We conclude that oxygen isotopes and salinity profiles can give information on the age of the ice and enables distinction between FYI and SYI (or older) ice in the area north of Svalbard.

Трёхмерное моделирование схода лавинных потоков средствами пакета OpenFOAM

In this paper, a model of a snow avalanche was created with the help of open source CFD software OpenFOAM. The avalanche is considered as a turbulent two-phase flow - snow and air. We take incompressible Herschel-Balkley fluid as a model of snow. Air is a newtonian fluid. For tracking and locating the free surface we use the volume of fluid (VOF) method. In calculations we use solver interFoam which is based on the VOF method. The K - ε turbulence model was used. Navier-Stokes equations, rheological ratios, equations for turbulent kinetic energy and dissipation are used to determine the model. The avalanche occurred at the 22nd site on the Ukspor mountain was modeled. Computational domain was made using digital terrain model in ASCII GRID format. The shape of snow deposits area was calculated and compared with the real data. The velocity field of flow, pressure distribution, the field of volume snow fraction were obtained for different time instances. The value of the average velocity of the flow was 44,8 m/s, the value of the maximum velocity of the flow (including snow-dust cloud) was 78 m/s. These results allow to determine the avalanche hazard area and help to optimally design the defense systems. In future it is planned to simulate this event using more detailed grids and realize this model in other software for example INMOST or with own code to compare the results.

Spring and summer monthly MODIS LST is inherently biased compared to air temperature in snow covered sub-Arctic mountains

Satellite-derived land surface temperature (skin temperature) provides invaluable information for data-sparse high elevation and Arctic regions. However, the relationship between satellite-derived clear-sky skin temperature and various downscaled air temperature products for snow covered sub-Arctic alpine regions remain poorly understood, such that trend analysis or air temperature product integration is difficult. We compared monthly average air temperatures from two independent downscaled temperature products to MODIS Land Surface Temperature (LST) and air temperature at nine meteorological stations situated above tree-line in the southwest Yukon, Canada, between May and August 2008 for a full range of snow cover fractions. We found that both downscaled products generally agreed with LST for the low elevation, snow-free, vegetation classes. However, a systematic cold bias in Average LST emerged for snow fractions greater than approximately 40%, and this bias increased in magnitude as snow cover increased. In these situations the downscaled air temperatures were 5–7°C warmer than Average LST for snow fractions of >90%, and this pattern was largely independent of the number of measurements of LST within a month. Maximum LST was similar to average air temperatures for high snow fractions, but Minimum LST was colder by 10°C or more for all snow fractions. Consequently, the average of Maximum and Minimum LST produces the cold bias, compared to air temperature, for high snow cover fractions. Air temperature measured at nine meteorological monitoring stations located between elevations of 1408–2690m, on land cover classes Barren, Sparsely Vegetated or Permanent Snow and Ice, confirmed the cold bias results when incorporating Minimum LST in monthly averages. For snow fractions of <40% the RMSE for all of the temperature products was <2.5°C when compared to station air temperature and all biases were positive and <2.0°C. For snow fractions of >40%, the average LST bias became strongly negative at −4.5°C, and the RMSE increased to 6.1°C, whereas the downscaled products bias and RMSE were similar to those from snow fractions of <40%. A weak warm bias for all the temperature products occurred for small snow fractions over non-forested land cover classes. Downscaled air temperature fields show physically real differences from Average LST in spring and summer, caused by snow cover and the interplay of Maximum and Minimum LST. These findings indicate that the integration of MODIS LST with downscaled air temperature products or local air temperature requires the incorporation of snow cover.

Improving Budyko curve‐based estimates of long‐term water partitioning using hydrologic signatures from GRACE

AbstractThe Budyko hypothesis provides a first‐order estimate of water partitioning into runoff (Q) and evapotranspiration (E). Observations, however, often show significant departures from the Budyko curve; moreover, past improvements to Budyko curve tend to lose predictive power when migrated between regions or to small scales. Here to estimate departures from the Budyko curve, we use hydrologic signatures extracted from Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage anomalies. The signatures include GRACE amplitude as a fraction of precipitation (A/P), interannual variability, and 1‐month lag autocorrelation. We created a group of linear models embodying two alternate hypotheses that departures can be predicted by (a) Taylor series expansion based on the deviation of physical characteristics (seasonality, snow fraction, and vegetation index) from reference conditions and (b) surrogate indicators covarying with E, e.g., A/P. These models are fitted using a mesoscale USA data set (HUC4) and then evaluated using world data sets and USA basins &lt;1 105 km2. The model with A/P could reduce error by 50% compared to Budyko itself. We found that seasonality and fraction of precipitation as snow account for a major portion of the predictive power of A/P, while the remainder is attributed to unexplained basin characteristics. When migrated to a global data set, type b models performed better than type a. This contrast in transferability is argued to be due to data set limitations and catchment coevolution. The GRACE‐based correction performs well for USA basins &gt;1000 km2 and, according to comparison with other global data sets, is suitable for data fusion purposes, with GRACE error as estimates of uncertainty.

Hydrogeological response to climate change in alpine hillslopes

AbstractClimate change threatens water resources in snowmelt‐dependent regions by altering the fraction of snow and rain and spurring an earlier snowmelt season. The bulk of hydrological research has focused on forecasting response in streamflow volumes and timing to a shrinking snowpack; however, the degree to which subsurface storage offsets the loss of snow storage in various alpine geologic settings, i.e. the hydrogeologic buffering capacity, is still largely unknown. We address this research need by assessing the affects of climate change on storage and runoff generation for two distinct hydrogeologic settings present in alpine systems: a low storage granitic and a greater storage volcanic hillslope. We use a physically based integrated hydrologic model fully coupled to a land surface model to run a base scenario and then three progressive warming scenarios, and account for the shifts in each component of the water budget. For hillslopes with greater water retention, the larger storage volcanic hillslope buffered streamflow volumes and timing, but at the cost of greater reductions in groundwater storage relative to the low storage granite hillslope. We found that the results were highly sensitive to the unsaturated zone retention parameters, which in the case of alpine systems can be a mix of matrix or fracture flow. The presence of fractures and thus less retention in the unsaturated zone significantly decreased the reduction in recharge and runoff for the volcanic hillslope in climate warming scenarios. This approach highlights the importance of incorporating physically based subsurface flow in to alpine hydrology models, and our findings provide ways forward to arrive at a conceptual model that is both consistent with geology and hydrologic principles. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Transition of dominant peak flow source from snowmelt to rainfall along the Colorado Front Range: Historical patterns, trends, and lessons from the 2013 Colorado Front Range floods

AbstractThe Colorado Front Range has a large elevation gradient with deep seasonal snowpack in the mountains and limited snow accumulation in the foothills and plains. This study examines how the sources of annual peak flows (snowmelt, rainfall, mixed) change with the fraction of time snow persists on the ground, snow persistence (SP), and whether these sources have changed over time. Sources of peak flows for 20 gaging stations are estimated using a gridded rain and snow model forced with PRISM daily precipitation and both PRISM and TopoWx temperature. The mean snowmelt contribution to peak flow is highly correlated with SP (r2 = 0.86–0.90). Watersheds with SP &lt; 0.3 (low snow, elevation &lt;2000 m) are rainfall‐dominated, and watersheds with SP &gt; 0.7 (persistent snow, elevation &gt;3100 m) are mostly snowmelt‐dominated, with mixed sources between these thresholds. Rainfall runoff peak flows are possible at all elevations, but their likelihood declines with increasing SP. Rainfall runoff from an extreme storm in September 2013 produced the highest annual peaks at many stations, including some snowmelt‐dominated watersheds. Regional Kendall trend tests indicate that the contributions of snowmelt to peak flows and total annual inputs have declined in the mixed source zone. These changes may affect hydrographs, as analyses confirm that snowmelt runoff generally produces more attenuated peaks than rainfall runoff. Discrimination of peak flow source is sensitive to input data and model structure for mixed rain and snowmelt events, and both observation and modeling research are needed to help understand potential runoff changes in these conditions.

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

An accurate precipitation phase determination—i.e., solid versus liquid—is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models.

Extreme values of snow-related variables in Mediterranean regions: trends and long-term forecasting in Sierra Nevada (Spain)

Abstract. Mountain areas in Mediterranean regions constitute key monitoring points for climate variability and its impacts, but long time datasets are not always available due to the difficult access to high areas, relevant for capturing temperature and precipitation regimes, and the predominance of cloudy remote sensing images during the snow season. Sierra Nevada National Park (South Spain), with altitudes higher than 3500 m a.s.l., is part of the Global Change in Mountain Regions network. Snow occurrence just 40 km from the seaside determines a wide range of biodiversity, a snowmelt fluvial regime, and the associated ecosystem services. This work presents the local trend analysis of weather variables at this area together with additional snow-related variables. For this, long term point and distributed observations from weather stations and remote sensing sources were studied and used as input and calibration datasets of a physically based snow model to derive long term series of mean and maximum daily fraction of snow covered area, annual number of days with snow, annual number of days with precipitation, mean and maximum mean daily snow water equivalent, and snowmelt and evaporation volumes. The joint analysis of weather and snow variables showed a decrease trend in the persistence and extent of the snow cover area. The precipitation regime, rather than the temperature trend, seems to be the most relevant driver on the snow regime forcing in Mediterranean areas. This poses a constraint for rigorous scenario analysis in these regions, since the precipitation pattern is poorly approximated by climatic models in these regions.

Impact of climate and land cover changes on snow cover in a small Pyrenean catchment

The seasonal snow in the Pyrenees Mountains is an essential source of runoff for hydropower production and crop irrigation in Spain and France. The Pyrenees are expected to undergo strong environmental perturbations over the 21st century because of climate change (rising temperatures) and the abandonment of agro-pastoral areas (reforestation). Both changes are happening at similar timescales and are expected to have an impact on snow cover. The effect of climate change on snow in the Pyrenees is well understood , but the effect of land cover changes is much less documented. Here, we analyze the response of snow cover to a combination of climate and land cover change scenarios in a small Pyrenean catchment (Bassies, 14.5 km 2 , elevation range 940–2651 m a.s.l.) using a distributed snowpack evolution model. Climate scenarios were constructed from the output of regional climate model projections, whereas land cover scenarios were generated based on past observed changes and an inductive pattern-based model. The model was validated over a snow season using in situ snow depth measurements and high-resolution snow cover maps derived from SPOT (Satellite Pour l'Observation de la Terre – Earth Observation Satellite) satellite images. Model projections indicate that both climate and land cover changes reduce the mean snow depth. However, the impact on the snow cover duration is moderated in reforested areas by the shading effect of trees on the snow surface radiation balance. Most of the significant changes are expected to occur in the transition zone between 1500 m a.s.l. and 2000 m a.s.l. where (i) the projected increase in air temperatures decreases the snow fraction of the precipitation and (ii) the land cover changes are concentrated. However, the consequences on the runoff are limited because most of the meltwater originates from high-elevation areas of the catchment, which are less affected by climate change and reforestation.

Adding a dynamical cryosphere to &amp;lt;i&amp;gt;i&amp;lt;/i&amp;gt;LOVECLIM (version 1.0): coupling with the GRISLI ice-sheet model

Abstract. We present a coupling approach to and the first results of the GRISLI ice-sheet model within the iLOVECLIM-coupled climate model. The climate component is a relatively low-resolution earth system model of intermediate complexity, well suited for long-term integrations and thus for coupled climate–cryosphere studies. We describe the coupling procedure with emphasis on the downscaling scheme and the methods to compute the snow fraction from total precipitation fields. We then present results for the Greenland ice sheet under pre-industrial climate conditions at the end of a 14 000 yr long integration. The simulated ice sheet presents too large a thickness in its central part owing to the overestimation of precipitation in the atmospheric component. We find that including downscaling procedures for temperature improves the temperature distributions over Greenland for both the summer and annual means. We also find an ice-sheet areal extent that is overestimated with respect to the observed Greenland ice sheet.

Feeding habits of mesopelagic copepods in Sagami Bay: Insights from integrative analysis

We investigated the feeding habits of mesopelagic copepods in Sagami Bay during a spring bloom, focusing on omnivorous copepods of the families Aetideidae, Metridinidae, Scolecitrichidae, and Spinocalanidae by integrative application of stable-isotope (SI) analysis, microscopic observation of gut contents, elemental analysis of gut contents and sinking particles with an electron probe micro analyzer (EPMA), and morphological analysis of mouthparts. The SI ratios (δ13C and δ15N) of most mesopelagic species that initially were assumed to feed mainly on marine snow (sinking particles) were allocated within the SI plots that were assumed for the consumers of particulate organic matter from the epipelagic zone. Microscopy showed different compositions of gut contents among the copepods, most of which ingested marine snow containing incompletely degraded phytoplankton and cyanobacteria. According to the EPMA analysis, percentages of terrigenous mineral particles in marine snow were significantly higher than those in most of the copepod guts, suggesting selective ingestion of sinking particles by these copepods. Morphological analysis showed that mouthparts of most of the copepods were not suitable for fine-particle feeding. These mesopelagic copepods were distributed mostly below 50m, where Chl-a was essentially depleted. These observations suggest feeding specialization among mesopelagic omnivorous copepods, as well as their selective ingestion of fresher particles and/or parts among diverse fractions of marine snow.

Evaluation of remotely sensed snow cover product in Central Asia

Central Asian countries depend highly on water resources from snow and glacier melt, which has to be studied thoroughly to estimate water availability. However, the observation network in Central Asia is poor to carry out such studies in detail. Observations from space using remote sensing techniques might fill this observation gap, which needs to be validated. Therefore, this study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product in Central Asia. For the evaluation, in situ snow depth data from 30 meteorological stations and higher resolution Landsat data are used. The results show an overall snow agreement between MODIS and ground observations of 93.1 and 92.7% for MODIS Terra and MODIS Aqua snow products, respectively. The agreement between MODIS and Landsat is 91.9% when considering snow and land agreements. The snow fraction product from MODIS is also validated using Landsat data, and varying accuracies are obtained. The main disadvantage of the MODIS snow cover product are the cloud induced gaps. Therefore, cloud covered pixels are eliminated using the ModSnow algorithm. Using the in situ data, the snow agreement of cloud removed snow cover data is checked, and an accuracy of 84.4% is achieved.

Atmospheric deposition of beryllium in Central Europe: Comparison of soluble and insoluble fractions in rime and snow across a pollution gradient

Little is known about atmospheric input of beryllium (Be) into ecosystems, despite its highly toxic behavior. For three consecutive winters (2009–2011), we measured Be concentrations in horizontal deposition (rime) and vertical deposition (snow) at 10 remote mountain-top locations in the Czech Republic, Central Europe. Beryllium was determined both in filtered waters, and in HF digests of insoluble particles. Across the sites, soluble Be concentrations in rime were 7 times higher, compared to snow (6.1 vs. 0.9ng·L−1). Rime scavenged the pollution-rich lower segments of clouds. The lowest Be concentrations were detected in the soluble fraction of snow. Across the sites, 34% of total Be deposition occurred in the form of soluble (bioavailable) Be, the rest were insoluble particles. Beryllium fluxes decreased in the order: vertical dry deposition insoluble>vertical dry deposition soluble>horizontal deposition soluble>vertical wet deposition insoluble>vertical wet deposition soluble>horizontal deposition insoluble. The average contributions of these Be forms to total deposition were 56, 21, 8, 7, 5 and 3%, respectively. Sites in the northeast were more Be-polluted than the rest of the country with sources of pollution in industrial Silesia.

Observations of snow mixed pixel spectral characteristics using a ground-based spectral radiometer and comparing with unmixing algorithms

The unmixing algorithms of mixed snow pixels and the fractional snow cover products are an important research direc- tion for snow remote sensing. In the present study, we first designed the mixed snow pixels of different snow fraction/proportion in Northern Xinjiang, China as ground truth. Then, a SVC HR-1024 ground-based spectral radiometer was used for measuring the spectral property of this designed pixel for different snow fractions and different underlying surfaces. Finally, using the measured spectral data, the four mixed-pixel decomposition models were validated and evaluated for their performance in terms of accuracy and computational efficiency. The results showed that the reflectivity does not decline linearly with the reduction of snow ratio in the pixel, and that the unmixing accuracy inversely depends on the scales of the observation. Further, the comparison of the above mentioned unmixing algotihms showed that the linear regression method has the worst accuracy, especially when the snow proportion is less than 50%; the accuracy of sparse regression algorithm and non-negative matrix factorization were slightly higher than the full constrained linear mixed-pixel decomposition; however, full constrained linear mixed-pixel decomposition method had higher computational efficiency than the other two methods; the sparse regression algorithm has lowest computational efficiency. With unmixing remote sensing images, due to the large data volumes, we must consider the algorithms' computational efficiency. This study would promote quantitative researches on snow mixed pixel decomposition, and provide a theoretical basis for accurately extracting the snow coverage of interest area using remote sensing images.

Surface-based precipitation phase determination methods in hydrological models

We compared solid and liquid precipitation mass output from three categories of common model precipitation phase determination schemes (PPDS) to the recorded precipitation phase in a set of 45 years of 3-hour manual meteorological observations from 19 Swedish meteorological stations. In the first category of rain/snow thresholds, it was found that rain/snow air temperature threshold (ATT) is a better precipitation phase indicator than a rain/snow dew point temperature threshold. When a rain/snow ATT of 0.0 °C (a default value used in some recent models) was replaced by 1.0 °C, misclassified precipitation was reduced by almost one half. A second category of PPDS use two ATTs, one snow and one rain, with a linear decrease in snow fraction between. This category identified precipitation phase better than a rain/snow ATT at 17 stations. Using all observations from all the meteorological stations, a final category using an air-temperature-dependent snow probability curve resulted in slightly lower misclassified precipitation mass at 13 of the 19 stations. However, schemes from the linear decrease in snow fraction category had the lowest misclassified precipitation mass at four meteorological stations.

Improving surface-based precipitation phase determination through air mass boundary identification

Most hydrological models apply one empirical formula based on surface air temperature for precipitation phase determination. This approach is flawed as surface precipitation phase results from energy exchanges between falling precipitation and air in the lower atmosphere. Different lower atmospheric conditions cause different precipitation phase probabilities for near-freezing temperatures. Often directly measured lower atmospheric conditions are not available for remote areas. However, meteorological observations occurring before/after similar air mass boundaries have similar atmospheric conditions that vary from most other observations. Therefore, hydrological models can indirectly account for lower atmospheric conditions. Twenty years of manual observations from eight United States weather stations were used to compare misclassified precipitation proportions when analyzing (a) all precipitation observations together and (b) identified cold air mass boundary observations (CAB) and non-CAB observations separately. The CAB observations were identified by a rapid surface air temperature decrease. A two-surface air temperature threshold method with one threshold all snow (TS°C) and one all rain (TR°C) having a linear snow fraction decrease between the thresholds was used. The TS (0 °C), and TR (4 °C) values for CAB were 1 °C warmer than for non-CAB (−1 °C, 3 °C). Analyzing CAB and non-CAB separately reduced misclassified precipitation 23%, from 7.0 to 5.4%.

An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale

An advanced approach for estimating the sub-pixel fraction of snow covered area for boreal forest and tundra belt from optical data is presented. The method named SCAmod by the Finnish Environment Institute (SYKE) is based on a forward semi-empirical model where the at-satellite observed reflectance is expressed as a function of the fractional snow cover (FSC). The effective forest transmissivity determined for each target unit-area and the generally applicable reflectances of three major contributors (wet snow, forest canopy and snow-free ground) serve as model parameters. The forest transmissivity describes the visibility of the ground through the forest canopy from above, and can be determined from visible reflectance data acquired at full snow cover conditions. SCAmod can be applied to data from various sensors operating at optical and near-infrared region. We apply the method to Envisat/AATSR and Terra/MODIS data and validate the resulting FSC against ground truth data over Finland, with considerations of feasibility of these ground truth data in a scale of the MODIS/AATSR pixel. In comparison, also NASA MOD10_L2 fractional snow product is validated. The results indicate that SCAmod performs better than MOD10_L2 particularly in forested areas; an RMSE of 0.11 for the fraction of snow covered area (range is 0–1) is achieved. For large-scale snow mapping, using reflectance data for transmissivity determination would evidently be very time-consuming. Here we present also a new method for transmissivity generation using global land cover data and demonstrate its use for snow cover mapping in continental scale. The resulting FSC data are compared against FSC from high-resolution Landsat TM/ETM+ data for selected areas in Northern and Eastern Europe. The comparison indicates that SCAmod is feasible also in continental-scale snow mapping and is superior in identifying snow in dense forests.

Evaluation of emission from snow-covered ground for passive microwave remote sensing

This article investigates emission behaviour at frequencies of 18.7, 36.5 and 89 GHz and an incidence angle of 55° over a snow-covered surface at the local scale observation site in Fraser, CO, USA, using both one-layer and two-layer emission models. The models employ the matrix doubling approach to implement the radiative-transfer equation based on dense media theory and the advanced integral equation model. When compared to Ground-Based Passive Microwave Radiometer (GBMR-7) observation on 21 February 2003, both the models could simulate the observed brightness temperature well, but the polarization difference between the observation and the models was smaller for the two-layer emission model than the one-layer model. In addition, we successfully interpreted the emission magnitude and polarization separation of a snow-removed surface by incorporating a Mie scattering transition layer above the soil medium. In this work, we also demonstrated the effect of snow fraction on the brightness temperature difference at 18.7 and 36.5 GHz over a snow-covered surface with the field observation. In conclusion, we demonstrate the snow impact on soil surface with snow depth (SD) and snow fraction variation through modelling and in situ data.