Snow Surface Temperature Research Articles

Investigation into a new method for monitoring snow temperature on cross-country ski tracks

Accurate predictions of snow surface temperature on cross-country race courses are crucial for optimal race ski preparation. This study investigates an easy and inexpensive method to monitor snow temperature at strategic locations on a racing track (e.g., southerly aspects, shaded areas and the stadium). Small self-contained temperature sensor dataloggers collect the average subsurface temperature between 1 and 2 cm, often where a skating ski rides. Temperature, temperature changes and trends are monitored the day, or days, prior to competition and examined to aid in ski preparation for upcoming events. Solar absorption of the sensors was the primary challenge and techniques to mitigate this issue were tested and are discussed. Results from a field study are presented, which give insight into the benefits and limitations for the usefulness of iButton temperature data for ski teams.

Field investigation of near-surface metamorphism of snow

AbstractBuried surface hoar and near-surface faceted crystals are known to lead to deadly avalanches. Over the course of three winter seasons a field investigation detailing the environmental conditions leading to the formation of these crystals was performed. Weather stations on north- and south-facing aspects were established. The weather data were accompanied by detailed daily observations and grain-scale photographs of the snow surface. During the three seasons, 35 surface hoar and 47 near-surface facets events were recorded. The mean weather conditions for the entire dataset (all three seasons and both stations) were compared to the nights when surface hoar formed. The comparison yielded five parameters that were statistically linked to the formation of surface hoar: incoming longwave radiation, snow surface temperature, wind velocity, relative humidity and the air/snow temperature difference. A similar comparison between the daytime mean values for all days with near-surface facet events revealed three parameters with statistically significant differences. Thus, these parameters (short- and longwave radiation and relative humidity) could be statistically linked to facet formation. This research also suggests that environmental conditions in the daytime hours before and after surface hoar formation are statistically similar to the conditions causing near-surface facet formation.

SIMULATION OF INTERANNUAL VARIABILITY OF SNOW COVER AT VALDAI (RUSSIA) USING A DISTRIBUTED BIOSPHERE HYDROLOGICAL MODEL WITH IMPROVED SNOW PHYSICS

The distributed biosphere hydrological model (WEB-DHM) was physically improved in snow physics, by incorporating the 3-layer energy balance based snow module of the Simplified Simple Biosphere 3 (SSiB3) model and albedo module of the Biosphere Atmosphere Transfer Scheme (BATS). The new version of WEB-DHM is referred to as WEB-DHM-S. In this study, the WEB-DHM-S is evaluated at Valdai site (Russia) from 1966 to 1983, to check its performance in simulating the interannual variability of snow cover. Results show that the WEB-DHM-S is capable of reproducing the point-scale interannual variations of snow water equivalent, soil and snow surface temperature well. In addition, the good performance of WEB-DHM-S was further confirmed in simulating soil moisture (top 1 m) and evapotranspiration.

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: (a) original TIMESAT algorithm with original MODIS VI, (b) original TIMESAT algorithm with pre-processed MODIS VI, and (c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Observations and simulations of snow surface temperature on cross-country ski racing courses

Fast skis are essential for an Olympic cross-country skiing athlete. Accurate and timely estimates of the snow surface conditions on a race course are needed to prepare race skis. For training purposes prior to the 2010 Winter Olympics, snow surface and snowpack observations were collected on the cross-country racing track at the Whistler Olympic Park, British Columbia during February 2008 and 2009. During periods with clear skies, snow surface temperatures varied by more than 10 °C around the course while temperatures in the stadium area increased by more than 16 °C from morning to early afternoon. Simulations using the SNOWPACK model of snow surface temperature were within 1 °C of those measured during a four day observation period in the stadium area. Idealized simulations were completed varying cloud cover, slope and aspect. These simulations provided realistic appearing changes in snow surface temperature as a function of time of day.

Změna teplot sněhu při běhu na lyžích v obraze sekvenční termografie

Twenty years ago it was shown that friction increases the temperature of snow skis. Change of state permits for water skis glide. Th e aim of this pilot study was to determine whether dynamic infrared thermography is able to capture rapid changes in temperature of snow some areas (not just at one point), the ski tracks before and aft er passing. We monitored the snow on the track aft er 60 races two runners in various weights (86 and 73 kg) on two types of skis and lubrication (for classic and skating cross) and three diff erent speeds (3,04±0,180, 3,81±0,273 a 5,30±0,314 m.s-1). Measurements of infrared radiation and the calculation of snow surface temperature immediately before and aft er passing skier was carried out thermo-graphic system FLIR SC620 with 30 Mhz. Nonparametric Wilcoxon test for paired comparison of values was found signifi cant increases in temperature aft er the snow cleared the skies: runner A from -3,71 to -3,50 °C (p=0,000000) and runner B from -3,75 na -3,50 °C (p=0,000000). To return to the baseline temperature is within 5 seconds aft er passing. Diff erences were demonstrated in the heat of snow between runners with diff erent masses and for the use of diff erent types of skis. Th e study confi rmed the ability of dynamic infrared thermography to register a very rapid temperature changes of snow in an area immediately before and aft er crossing the cross-country skiers. Dimensional dynamic thermography would seem to contribute to the choice of the correct strategy and lubrication in ski racing. However, it is necessary to make a series of validation studies.

Improving the snow physics of WEB-DHM and its point evaluation at the SnowMIP sites

Abstract. In this study, the snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is significantly improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics is hereafter termed WEB-DHM-S. Since the in-situ observations of spatially-distributed snow variables with high resolution are currently not available over large regions, the new distributed system (WEB-DHM-S) is at first rigorously tested with comprehensive point measurements. The stations used for evaluation comprise the four open sites of the Snow Model Intercomparison Project (SnowMIP) phase 1 with different climate characteristics (Col de Porte in France, Weissfluhjoch in Switzerland, Goose Bay in Canada and Sleepers River in USA) and one open/forest site of the SnowMIP phase 2 (Hitsujigaoka in Japan). The comparisons of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff at the SnowMIP1 sites reveal that WEB-DHM-S, in general, is capable of simulating the internal snow process better than the original WEB-DHM. Sensitivity tests (through incremental addition of model processes) are performed to illustrate the necessity of improvements over WEB-DHM and indicate that both the 3-layer snow module and the new albedo scheme are essential. The canopy effects on snow processes are studied at the Hitsujigaoka site of the SnowMIP2 showing that the snow holding capacity of the canopy plays a vital role in simulating the snow depth on ground. Through these point evaluations and sensitivity studies, WEB-DHM-S has demonstrated the potential to address basin-scale snow processes (e.g., the snowmelt runoff), since it inherits the distributed hydrological framework from the WEB-DHM (e.g., the slope-driven runoff generation with a grid-hillslope scheme, and the flow routing in the river network).

Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge

The hydrological budget of Himalayan rivers is dominated by monsoonal rainfall and snowmelt, but their relative impact is not well established because this remote region lacks a dense gauge network. Here, we use a combination of validated remotely‐sensed climate parameters to characterize the spatiotemporal distribution of rainfall, snowfall, and evapotranspiration in order to quantify their relative contribution to mean river discharge. Rainfall amounts are calculated from calibrated, orbital, high‐resolution Tropical Rainfall Measurement Mission data, and snow‐water equivalents are computed from a snowmelt model based on satellite‐derived snow cover, surface temperature, and solar radiation. Our data allow us to identify three key aspects of the spatiotemporal precipitation pattern. First, we observe a strong decoupling between the rainfall on the Himalayan foreland versus that in the mountains: a pronounced sixfold, east‐west rainfall gradient in the Ganges plains exists only at elevations <500 m asl. Mountainous regions (500 to 5000 m asl) receive nearly equal rainfall amounts along strike. Second, whereas the Indian summer monsoon is responsible for more than 80% of annual rainfall in the central Himalaya and Tibetan Plateau, the eastern and western syntaxes receive only ∼50% of their annual rainfall during the summer season. Third, snowmelt contributions to discharge differ widely along the range. As a fraction of the total annual discharge, snowmelt constitutes up to 50% in the far western (Indus area) catchments, ∼25% in far eastern (Tsangpo) catchments, and <20% elsewhere. Despite these along‐strike variations, snowmelt in the pre‐ and early‐monsoon season (April to June) is significant and important in all catchments, although most pronounced in the western catchments. Thus, changes in the timing or amount of snowmelt due to increasing temperatures or decreasing winter precipitation may have far‐reaching societal consequences. These new data on precipitation and runoff set the stage for far more detailed investigations than have previously been possible of climate‐erosion interactions in the Himalaya.

Predicting Snow Velocity in Large Chute Flows Under Different Environmental Conditions

Summary Observations, model evaluations and expert judgements are combined to make predictions of snow velocity in large chute experiments. Different experimental variables, namely the environmental conditions snow density and snow surface temperature, affect all aspects of this inference. We show how the effect of these two variables can be incorporated in our judgements regarding the uncertain parameters of the physical model, the discrepancy between the physical model and reality and the observation error. We adopt a Bayes linear approach to avoid the necessity of fully probabilistic belief specifications and demonstrate visual tools for statistical validation. Our results represent an important first step in improving the specification of uncertainty in model-based avalanche hazard mapping.

Evaluation of alternative formulae for calculation of surface temperature in snowmelt models using frequency analysis of temperature observations

Abstract. The snow surface temperature is an important quantity in the snow energy balance, since it modulates the exchange of energy between the surface and the atmosphere as well as the conduction of energy into the snowpack. It is therefore important to correctly model snow surface temperatures in energy balance snowmelt models. This paper focuses on the relationship between snow surface temperature and conductive energy fluxes that drive the energy balance of a snowpack. Time series of snow temperature at the surface and through the snowpack were measured to examine energy conduction in a snowpack. Based on these measurements we calculated the snowpack energy content and conductive energy flux at the snow surface. We then used these estimates of conductive energy flux to evaluate formulae for the calculation of the conductive flux at the snow surface based on surface temperature time series. We use a method based on Fourier frequency analysis to estimate snow thermal properties. Among the formulae evaluated, we found that a modified force-restore formula, based on the superimposition of the force-restore equation capturing diurnal fluctuations on a gradually changing temperature gradient, had the best agreement with observations of heat conduction. This formula is suggested for the parameterization of snow surface temperature in a full snowpack energy balance model.

The development of new algorithms for remote sensing of snow conditions based on data from the catchment of Øvre Heimdalsvatn and the vicinity

The catchment of Ovre Heimdalsvatn and the surrounding area was established as a site for snow remote sensing algorithm development, calibration and validation in 1997. Information on snow cover and snowmelt are important for understanding the timing and scale of many lake ecosystem processes. Field campaigns combined with data from airborne sensors and spaceborne high-resolution sensors have been used as reference data in experiments over many years. Several satellite sensors have been utilised in the development of new algorithms, including Terra MODIS and Envisat ASAR. The experiments have been motivated by operational prospects for snow hydrology, meteorology and climate monitoring by satellite-based remote sensing techniques. This has resulted in new time-series multi-sensor approaches for monitoring of snow cover area (SCA) and snow surface wetness (SSW). The idea was to analyse, on a daily basis, a time series of optical and radar satellite data in multi-sensor models. The SCA algorithm analyses each optical and synthetic aperture radar (SAR) image individually and combines them into a day product based on a set of confidence functions. The SSW algorithm combines information about the development of the snow surface temperature and the snow grain size (SGS) in a time-series analysis. The snow cover algorithm is being evaluated for application in a global climate monitoring system for snow variables. The successful development of these algorithms has led to operational applications of snow monitoring in Norway and Sweden, as well as enabling the prediction of the spring snowmelt flood and thus the initiation of many lake production processes.

Road Surface Condition Forecasting in France

Abstract A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.

Monitoring Snow Characteristics With Ground-Based Multifrequency Microwave Radiometry

Long-term microwave and infrared radiometric measurements of snowpack were carried out with ground-based sensors in winter 2006-2007 and 2007-2008, together with conventional measurements of snow-cover profiles. The first experiment focused on the behavior of snow emission during the destructive and constructive metamorphisms. The second involved a correlation analysis of the small fluctuations related to diurnal solar cycle in order to obtain the time delay of microwave brightness temperatures Tb with respect to the snow surface temperature. From this analysis, it was possible to estimate an effective (weighed average) temperature and the thickness of the layer that mostly contributed to microwave emission at 19 and 37 GHz. The ratio of the brightness temperature to the effective temperature can be assumed to be an equivalent emissivity of the snowpack. Data collected in both years have been compared with simulations carried out using the advanced Institute of Applied Physics (IFAC) Radiative Advanced Dry Snow Emission (IRIDE) model driven by data collected on ground. The model is based on the advanced integral equation method to represent soil, coupled to a layer of dry snow whose electromagnetic properties are described by the dense medium radiative transfer theory with quasi-crystalline approximation applied to a medium (air) filled with sticky particles. Simulations performed by using ground data as inputs to the model have been found to be well in agreement with experimental data. Moreover, the comparison of model simulations with experimental data allowed one to understand some peculiar characteristics of microwave emission from the snowpack related to its physical conditions.

Influence of regional parameters on the surface mass balance of the Eurasian ice sheet during the peak Saalian (140 kya)

Recent geologically-based reconstructions of the Eurasian ice sheet show that during the peak Saalian (≈ 140 kya) the ice sheet was larger over Eurasia than during the Last Glacial Maximum (LGM) at ≈ 21 kya. To address this problem we use the LMDZ4 atmospheric general circulation model to evaluate the impact on the Saalian ice sheet's surface mass balance (SMB) from proglacial lakes, dust deposition on snow, vegetation and sea surface temperatures (SST) since geological records suggest that these environmental parameters were different during the two glacial periods. Seven model simulations have been carried out. Dust deposition decreases the mean SMB by intensifying surface melt during summer while proglacial lakes cool the summer climate and reduce surface melt on the ice sheet. A simulation including both proglacial lakes and dust shows that the presence of the former parameter reduces the impact of the latter, in particular, during summer. A switch from needle-leaf to tundra vegetation affects the regional climate but not enough to significantly influence the SMB of the nearby ice margin. However, a steady-state vegetation in equilibrium with the climate should be computed to improve the boundary conditions for further evaluations of the vegetation impact on the ice sheet's SMB. Finally, changes of the SST broadly affect the regional climate with significant consequences for the SMB.

Radiative and turbulent surface heat fluxes over sea ice in the western Weddell Sea in early summer

The radiative and turbulent heat fluxes between the snow‐covered sea ice and the atmosphere were analyzed on the basis of observations during the Ice Station Polarstern (ISPOL) in the western Weddell Sea from 28 November 2004 to 2 January 2005. The net heat flux to the snowpack was 3 ± 2 W m−2 (mean ± standard deviation; defined positive toward snow), consisting of the net shortwave radiation (52 ± 8 W m−2), net longwave radiation (−29 ± 4 W m−2), latent heat flux (−14 ± 5 W m−2), and sensible heat flux (−6 ± 5 W m−2). The snowpack receives heat at daytime while releases heat every night. Snow thinning was due to approximately equal contributions of the increase of snow density, melt, and evaporation. The surface albedo only decreased from 0.9 to 0.8. During a case of cold air advection, the sensible heat flux was even below −50 W m−2. At night, the snow surface temperature was strongly controlled by the incoming longwave radiation. The diurnal cycle in the downward solar radiation drove diurnal cycles in 14 other variables. Comparisons against observations from the Arctic sea ice in summer indicated that at ISPOL the air was colder, surface albedo was higher, and a larger portion of the absorbed solar radiation was returned to the atmosphere via turbulent heat fluxes. The limited melt allowed larger diurnal cycles. Due to regional differences in atmospheric circulation and ice conditions, the ISPOL results cannot be fully generalized for the entire Antarctic sea ice zone.

ADEOS-II/GLI Snow/Ice Products and the Scientific Implications

The algorithm principles, calibration and validation (Cal/Val) experiments, and retrieval results of ADEOS-II/GLI snow/ice products are reported. The GLI snow/ice products are snow surface temperature, two types of snow grain sizes for topmost and shallow snow layers, and mass fraction of snow impurities. The snow grain size and snow impurities as satellite standard products are unique, but very important because snow surface albedo essentially depends on those parameters. From the analyses of GLI snow/ice products, the new findings on spatial distributions of clean snow, drastic spatial and seasonal evolution of snow grain size, and the possibility of detecting spatial distributions of vertical inhomogeneity in the top several centimeters of snow cover in the northern hemisphere were obtained. These snow parameters are expected to be used an indicator of climate change by long-period monitoring. From the Cal/Val experiments in Alaska and eastern Hokkaido, Japan from 2001 to 2005 using MODIS and GLI data, it was found that snow surface temperature and grain size for shallow layer agreed well with in-situ measured values, while the accuracies of mass fractions of snow impurities and grain size at topmost layer were not so good compared with those for the former two products. The Cal/Val experiments also revealed some scientific results which are spectral or broadband snow albedos depending on snow grain size and snow impurities, relationship between snow surface temperature and snow grain size, and spectral and directional features of emissivity depending on snow types.

Surface energy balance of seasonal snow cover for snow-melt estimation in N-W Himalaya

This study describes time series analysis of snow-melt, radiation data and energy balance for a seasonal snow cover at Dhundi field station of SASE, which lies in Pir Panjal range of the N-W Himalaya, for a winter season from 13 January to 12 April 2005. The analysis shows that mean snow surface temperature remains very close to the melting temperature of snow. It was found close to −1°C for the complete observational period which makes the snow pack at Dhundi moist from its beginning. The average air temperature over this period was found to be 3.5°C with hourly average variation from −5.5°C to 13°C. The snow surface at this station received a mean short wave radiation of 430W m−2, out of which 298W m−2 was reflected back by the snow surface with mean albedo value of 0.70. The high average temperature and more absorption of solar radiation resulted in higher thermal state of the snowpack which was further responsible for faster and higher densification of the snowpack. Net radiation energy was the major component of surface energy budget with a mean value of 83W m−2. Bulk transfer model was used to calculate turbulent fluxes. The net energy was utilized for satisfying cold content and snow-melt by using measured snow surface temperature and density of snow pack. The mean square error between calculated and measured daily snow-melt was found to be approximately 6.6mm of water equivalent.

Evaluation of the NOHRSC Snow Model (NSM) in a One-Dimensional Mode

Abstract The National Operational Hydrologic Remote Sensing Center (NOHRSC) Snow Model (NSM) is an energy- and mass-balance model used by the National Oceanic and Atmospheric Administration’s National Weather Service for moderate-resolution spatially distributed snow analysis and data assimilation over the United States. The NSM was evaluated in a one-dimensional mode using meteorological and snowpit observations from five sites in Colorado collected during 2002–03. Four parameters estimated by the NSM [snow water equivalent (SWE), snow depth, average snowpack temperature, and snow surface temperature] were compared with snowpit observations and with estimates from another snow energy and mass-balance model, SNTHERM. Root-mean-squared differences (RMSDs) between snowpit SWE observations (January–June) at all sites and estimates from the NSM were about 11% (RMSD = 0.073 m) of the average maximum observed SWE from all sites of 0.694 m. SNTHERM exhibited only a slightly better agreement (RMSD = 0.066 m). During the winter and early spring period before snowpacks became isothermal at 273.15 K, both NSM and SNTHERM simulated significantly cooler average snowpack temperatures than observed (RMSD = 3 and 2 K, respectively). During this snow accumulation period estimates of SWE by both models were very similar. Differences in modeled SWE were traced to short periods (5–21 days) during isothermal conditions in early spring when the two models diverged. These events caused SWE differences that persisted throughout the ablation period and resulted in a range in melt-out times of 0.2–7.2 days between depth observations and modeled estimates. The divergence in SWE resulted from differences in snowmelt fluxes estimated by the two models, which are suggested to result from 1) liquid water fractions within a snowpack being estimated by the NSM using an internal energy method and by SNTHERM using a semiempirical temperature-based approach, and 2) SNTHERM, but not the NSM, accounting for the small liquid water fraction that coexists in equilibrium with snow when the snowpack surface is dry (<273.15 K).

Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland

The most practical way to get spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived “clear-sky” LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air temperature observations from Greenland Climate Network (GC-Net) automatic weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from − 40 to 0 °C. The satellite-derived LSTs agree within a relative RMS uncertainty of ~ 0.5 °C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a “point” while the satellite instruments record data over an area varying in size from: 57 × 57 m (ETM+), 90 × 90 m (ASTER), or to 1 × 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty ~ 2 °C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.

Retrieving snow surface temperature based on MODIS data

On the basis of simplification of the Planck function in a low temperature range, this paper revises the practical split-window algorithm and presents a method for retrieving snow surface temperature (Ts) based on MODIS data in the middle-latitude region. The application of this method in Qinghai Lake region reveals that it is feasible for the retrieval of Ts. Results of correlation analysis indicate that there was strong negative relationship between Ts and altitude. By analyzing three typical areas in which land cover was relatively homogenous, this paper discusses the relationship between Ts and normalized difference snow index (NDSI) and then presents a new concept named “NDSI-Ts space”.