(data from Markham and Baricer, 1987)
REMOTE SENSING OF ALPINE SNOW COVER
INVISIBLE AND NEAR-INFRARED WAVELENGTHS
Jeff Dozier
NASA Goddard Space Flight Center
Greenbelt MD 20771
ABSTRACT
Mapping of snow and estimation of snow characteristics from satellite remote sensing
data require that we distinguish snow from other surface cover and from clouds and
compensate for the effects of the atmosphere and rugged terrain. The spectral signature of
the snowpack is calculated from a radiative transfer model, accounting for scattering and
absorption by the ice grains, water inclusions, and particulates. Large surface grain
sizes can be distinguished from areas where the grain size is finer at the snow surface,
using Landsat Thematic Mapper bands 2 (green), 4 (near-infrared), and 5 (short-wave
infrared). Because of saturation in band 1 (blue), estimation of the degree of
contamination by absorbing aerosols is not feasible.
INTRODUCTION
Water in its frozen states accounts for more than 80 per cent of the total fresh water
on Earth and is the largest contributor to rivers and ground water over major portions of
the middle and high latitudes. Snow and ice also play important interactive roles in the
Earth's radiation balance, because snow has a higher albedo than any other natural
surface. Over 30 percent of the Earth's land surface is seasonally covered by snow, and 10
percent is permanently covered by glaciers. Snow cover represents a changing atmospheric
output resulting from variability in the Earth's climate, and it is also a changing
boundary condition in climate models. Thus understanding of global and regional climates
and assessment of water resources require that we monitor the temporal and spatial
variability of the snow cover over land areas, from the scale of small drainage basins to
continents.
The alpine snow cover and alpine glaciers in the mid-latitudes are important in both the
climatic and hydrologic contexts--to examination of global and regional climates and to
use of water resources (Colbeck et al., 1979; Walsh, 1984). Much of the uncertainty and
sensitivity in the global hydrologic cycle lies in these reservoirs of frozen water, and
the melting of alpine glaciers during the last half-century appears to account for much of
the corresponding rise in sea level (Meier, 1984).
Over the last two decades satellite remote sensing has opened the possibility of data
acquisition at regular intervals, and operational as well as research-oriented satellites
have provided information on snow cover. Remote sensing of the seasonal snow cover has
been used to improve the monitoring of existing conditions and has been incorporated into
several runoff forecasting and management systems. Satellite sensors in the visible and
near-infrared wavelengths provide information on the spatial distribution of parameters of
hydrologic importance. From these reflectance measurements, we can measure snow-covered
area, rates of snow-cover depletion, and surface albedo. The Landsat systems, in
particular, are a source of data for hydrological and glaciological research at the scale
of drainage basins.
SCATTERING AND ABSORPTION OF LIGHT BY SNOW AND ATMOSPHERE
The scattering and absorption of light by the snowpack, clouds, and the atmosphere are
analyzed with a multiple-scattering model, a two-stream approximation to the radiative
transfer equation (Meador and Weaver, 1980). The fundamental scattering properties of the
ice grains and water inclusions in the snowpack, the water droplets or ice crystals in
clouds, and the aerosols in the atmosphere are calculated by the complex angular momentum
approximation to the Mie equations (Nussenzveig and Wiscombe, 1980). The LOWTRAN model
(Kneizys et al., 1988) is used to obtain values for molecular absorption in the atmosphere
at the desired wavelengths.
OPTICAL PROPERTIES OF SNOW AND CLOUDS
Snow is a collection of ice grains and air, and, when at 0 degrees C, it also has a
significant fraction of liquid water. Snow also often includes particulate and chemical
impurities - dust, soot, pollen and other plant material, and small amounts of the major
cations and anions. Thus the optical properties of snow depend on the bulk optical
properties and the geometry of the ice grains, the liquid water inclusions, and the solid
and soluble impurities. Similarly, clouds are composed of water droplets, sometimes ice
crystals, and they may contain impurities.
Bulk Optical Properties of Ice and Water
In the visible and near-infrared wavelengths the bulk optical properties of ice and
water are very similar, so the reflectance and transmittance of the snowpack in this
region of the electromagnetic spectrum depend on the wavelength variation of the
refractive index of ice, the grain size distribution of the snow, the depth and density of
the snowpack, and the size and amount of those impurities whose refractive indices are
substantially different from those of ice and water. The reflectance of wet snow in the
near-infrared is lower than that of dry snow, but mainly because of microstructural
changes caused by the water, except in some narrow spectral regions where the optical
properties of water are different than those of ice. Similarly, the reflectance and
transmittance of clouds depend on the geometric thickness, the number density of the
droplets, and their size distribution.
The most important optical property of ice and water, which causes spectral variation in
the reflectance of snow and clouds in visible and near-infrared wavelengths, is that the
absorption coefficient (i.e. the imaginary part of the refractive index) varies by seven
orders of magnitude in the wavelengths from 0.4 to 2.5 ~m. Figure 1
shows the complex refractive index n + i k for ice and water. The important
properties to note are:
1. the spectral variation in the real part n is small, and the difference
between ice and water is not significant;
2. the absorption coefficients k of ice and water are very similar, except for
the region between 1.55 and 1.75 um, where ice is slightly more absorptive;
3. in the visible wavelengths both ice and water are highly transparent so k is small;
4. in the near-infrared wavelengths ice and water are moderately absorptive, and the
absorption increases with wavelength.
Spectral Reflectance of Snow
The spectral and angular variation in snow reflectance are modeled by the radiative
transfer equation, as shown to be appropriate by Bohren and Barkstrom (1974) and Warren
(1982). In the visible wavelengths ice is highly transparent, so the albedo of snow is
sensitive to small amounts of absorbing impurities (Warren and Wiscombe, 1980). In the
near-infrared wavelengths ice is more absorptive, so the albedo depends primarily on grain
size (Wiscombe and Warren, 1980). We make the following assumptions in modeling the
reflectance of snow. Most of these assumptions have yet to be tested by rigorous
measurements of physical properties and spectral reflectance of the same snowpack, but the
model produces reflectance spectra that match those of snow (Warren, 1982).
1. The reflectance of snow is modeled as a multiple scattering problem. Scattering
properties of irregularly-shaped grains are mimicked by Mie calculations for an
"equivalent sphere," for which the best candidate in the wavelength region from
0.4 to 1.1 um is apparently the sphere with the same surface-to-volume ratio
(Dozier et al., 1988), which can be measured by stereological methods applied to snow
samples (Davis and Dozier, 1989). Although snow grains are irregularly shaped, they are
usually not oriented, so the assumption that their scattering properties can be mimicked
by some spherical radius r is reasonable, especially when we want to describe the general
spectral properties. When we want details about the angular characteristics of the
reflectance, the spherical assumption could become more critical.
2. Near-field effects are assumed unimportant. The fact that the ice grains in a snowpack
touch each other apparently does not affect the snow's reflectance, because the center-to
center spacing is still much larger than the wavelength. That is, snow reflectance is
independent of density up to about 650 kg m-3. Reflectance measurements carried
out under field conditions over a season and simply analyzed statistically will show
a significant inverse relationship between density and reflectance, but the physical model
shows that the explanation for changes in reflectance lies in other properties of the snow
cover, namely an increase in grain size and in the amount of contaminants near the
surface.
3. The effect of absorbing impurities (dust, soot) can be modeled either as separate
spheres (smallest effect) or as concentric spheres with the impurity in the center
(largest effect). These should bound the magnitude.
Figure 2 shows the spectral reflectance of pure, deep snow for snow
grain radii from 50 to 1000 um (0.05 to 1.0 mm), representing a range from new
snow to spring snow, although the grain clusters in coarse spring snow can exceed 5 mm in
radius. Because ice is so transparent in the visible wavelengths, increasing the grain
size does not appreciably affect the reflectance. The probability that a photon will be
absorbed, once it enters an ice grain, is small, and that probability is not increased
very much if the ice grain is larger. In the near-infrared, however, ice is moderately
absorptive. Therefore, the reflectance is sensitive to grain size, and the sensitivity is
greatest at wavelengths from 1.0 to 1.3 um. Because the ice grains are strongly
forward-scattering in the near-infrared, reflectance increases with illumination angle,
especially for larger grains, as shown in Figure 2.
Because the complex indices of refraction of ice and water are similar, liquid water per
se has little effect on the reflectance of snow. Except where meltwater ponds in
depressions when melting snow overlies an impermeable substrate, or when rain falls on
fine-grained snow, liquid water content in snow rarely exceeds 5 or 6%. This small amount
of water does not affect the bulk radiative transfer properties, except possibly in a few
narrow wavelength regions where the absorption coefficients are appreciably different
(Hyvarinen and Lammasniemi, 1987). Instead, the decreases in reflectance that occur as
snow melts result from the effective size-increase caused by the two- to four-grain
clusters that form in wet, unsaturated snow (Colbeck, 1979, 1986). These apparently behave
optically as single grains.
Although the reflectance in the visible wavelengths is insensitive to grain size, it is
affected by two variables, finite depth and the presence of absorbing impurities. Figure 3 shows spectral reflectance for a range of grain sizes of snow
water equivalences from 10 to 100 mm, over a black surface. For large grains, r = 1 mm,
reflectance of snow with a water equivalence of as large as 100 mm is less than that of a
deep snowpack. In a similar manner, minute amounts of absorbing impurities reduce snow
reflectance in the visible wavelengths (Warren and Wiscombe, 1980). Soot concentrations as
low as 0.1 ppmw (parts per million by weight) are enough to perceptibly reduce
reflectance. The effect of the absorbing impurities is apparently enhanced when they are
inside the snow grains, because refraction focuses the light on the absorbers (Grenfell et
al., 1981; Chylek et al., 1983; Bohren, 1986).
Spectral Reflectance of Clouds and Snow/Cloud Discrimination
In visible satellite data, clouds can usually be distinguished from snow by texture,
but not when both snow and clouds saturate the sensor, as might be the case in the spring.
Moreover, in a computer image-processing system, texture is more difficult to analyze than
spectral information. Hence we seek wavelength bands where snow and clouds have different
spectral signatures. Clouds may be either warmer or colder than the snow surface, so one
cannot reliably distinguish clouds from snow in the thermal wavelengths. Properties that
cause clouds to have different spectral reflectance than snow are, in order of importance:
1. Cloud droplets or ice crystals are smaller than snow grains. Cloud droplets usually
have size radii less than 10 um; crystals in cirrus clouds can be as large as 40 um,
but most of them are smaller. A smaller scattering element-droplet, crystal, or grain-is
likely to absorb less radiation, but the difference is greatest at wavelengths where the
medium is modestly absorptive.
2. Most clouds are composed of water droplets, even at temperatures below 0 degrees C. At
most wavelengths in the optical region water and ice have similar refractive indices, but
ice is slightly more absorptive from 1.55 to 1.7 um. The difference in the size
of the scatterers between clouds and snow, however, is more important than the difference
in composition.
3. Snow on the ground is usually optically thicker than clouds. Therefore in the visible
wavelengths snow is sometimes brighter, because some of the light incident on the cloud is
transmitted through it. Thick clouds, however, are as bright as snow, so they cannot be
dependably distinguished in this wavelength region by a lower reflectance. Cirrus clouds
are usually thinner and have lower amounts of water per column of unit cross-sectional
area.
Figure 4 shows spectral reflectances for water and ice clouds. The water
clouds used in the calculation have a water equivalent thickness of 10 mm, while the
cirrus clouds have 1 mm. Therefore the water clouds are brighter. In the visible
wavelengths the water clouds and the snowpack are of comparable reflectance.
CHARACTERISTICS OF THE THEMATIC MAPPER
The Thematic Mapper first orbited on Landsat-4, launched in 1982. Landsat-5 was
launched in 1985, and its Thematic Mapper is still functioning, although it was designed
only for a five-year life. Orbital altitude is 705 km, spatial resolution at the surface
is 30 m, and the orbit has a 1 day repeat cycle. Swath width is 185 km. There are seven
spectral bands: three in the visible (1, 2, and 3); one in the near-infrared (4); two in
the short-wave infrared (5 and 7); and one in the thermal infrared (6). Table 1 shows the
wavelen~ths and radiometric characteristics.
Table 1. Landsat-5 TM
Radiometric Characteristics
(data from Markham and Baricer, 1987)
TM band 1 will usually saturate over snow, except in the shadows, during all months.
TM2 and TM3 will not usually saturate in December or January, will occasionally saturate
in February, and will frequently saturate on slopes exposed to the Sun throughout the
spring. TM4 will only occasionally saturate, after new snow in the spring, and TM5 and TM7
should never saturate over snow in the mid-latitudes, but may saturate over clouds or
bright soils.
MEASUREMENT OF SNOW PROPERTIES BY REMOTE SENSING
Satellite remote sensing in the visible and near-infrared wavelengths has become
increasingly important to snow hydrologists because the data provide information on the
spatial distribution of parameters of hydrologic importance. In snow and ice studies,
remote sensing has been used
to improve the monitoring of existing conditions and has been incorporated into several
runoff forecasting and management systems. The principal operational use of remote sensing
of snow properties has been to map the extent of the snow cover. Throughout the world, in
both small and large basins, maps of the snow cover throughout the snow season are used to
forecast melt, both in areas with excellent ancillary data and in remote areas with no
ancillary data (Rango et al., 1977; Andersen, 1982; Martinec and Rango, 1986).
Since the first mapping of snow cover from satellite, the spectral and spatial resolution
of the available sensors has been much improved. The high spatial resolution satellites
such as Landsat and SPOT and the medium resolution sensors such as the NOAA AVHRR are
widely used for mapping snow cover. The selection of the appropriate sensor depends on a
tradeoff between spatial and temporal resolution (Rott, 1987). The Landsat Multispectral
Scanning System (MSS) has a spatial resolution of about 80 m and is suitable for snow
mapping in basins larger than about 10km2 (Rango et al., 1983). Improved spatial
resolution has been available since 1982 from the Landsat Thematic Mapper (30m) and since
1984 from the French SPOT satellite (20m in the multispectral mode and 10 m in the
panchromatic mode). SPOT has the finest spatial resolution, but the Thematic Mapper has
the best spectral coverage. Dozier (1989) explains how snow cover and snow properties can
be mapped from the TM.
Incorporation of Topographic Effects
In all but very gentle terrain, significant variation in remotely sensed images in visible
and near infrared wavelengths results from local topographic effects that cause variation
in illumination angle and shadowing from local horizons (Williams et al., 1972; Dozier and
Outcalt, 1979; Olyphant, 1984; Dozier and Frew, 1990).
In the solar spectrum, irradiance in alpine terrain has three sources: (1) direct
irradiance from the sun; (2) diffuse irradiance from the sky, where a portion of the
overlying hemisphere is obscured by terrain; and (3) direct and diffuse irradiance, on
nearby terrain, that is reflected toward the point whose radiation flux we want to
calculate.
Estimation of Grain Size and Detection of Absorbing Impurities
In the visible wavelengths, we should be able to estimate the extent to which the
reflectance of snow has been degraded, either by absorbing impurities or by shallow depth.
However, this sensitivity would be best for the blue wavelengths, where the low saturation
values of the Thematic Mapper make its use for this purpose difficult. In the
near-infrared wavelengths, we should be able to estimate the grain size, and thus extend
the estimate of the spectral albedo throughout the solar wavelengths. Moreover, this
information would help us interpret the spectral signature of snow at microwave
frequencies.
SUMMARY
Multispectral measurements in visible and near-infrared wavelengths have been used to
map snow for more than two decades. Improved spectral coverage from the Landsat Thematic
Mapper has allowed better estimation of snow properties and discrimination between snow
cover and cloud cover. Future sensors with better spectral resolution should allow
estimation of grain size and contamination by absorbing impurities, which in turn can be
used to calculate spectral reflectance through the wavelengths of the solar spectrum.
Figure 1. Complex refractive index (n + i k) of ice and
water
Ice data are from Warren (1984); water data are from Hale and Querry (1973), Palmer and
Williams (1974), and Downing and Williams (1975). Upper: real part of refractive index (n).
Lower imaginary part of refractive index (k).
Figure 2. Spectral reflectance of deep snow at illumination
angles 60 degrees and 30 degrees
The curves represent grain radii of 50 um (upper), 200 um,
500 um, and 1,000 um (lower). In the visible wavelengths (0.4 to 0.7 um)
reflectance is insensitive to grain size. In the near infrared, especially from 0.9 to 1.3
um, reflectance is very sensitive to grain size. From 1.55 to 1.7 um
reflectance is sensitive, but only for small sizes. The effect of illumination angle is
greatest in the near-infrared.
Figure 3. Spectral reflectance of shallow snow
Figure 4. Spectral reflectance of ice and water clouds
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