Calculations of snow reflectance in all 6 TM reflective bands (i.e., 1,2,3,4,5, and 7) using a delta Eddington model show that snow reflectance in bands 4,5, and 7 is sensitive to grain size. Efforts to interpret the surface optical grain size for the spectral extension of albedo are described. Results show the TM data include spectral channels suitable for snow/cloud discrimination and for snow albedo measurements that can be extended throughout the solar spectrum. Except for band 1, the dynamic range is large enough that saturation occurs only occasionally. The finer resolution gives much better detail on the snowcovered area and might make it possible to use textural information instead of the snowline as an index to the amount of snow melt runoff

Dozier, J.

English

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https://ntrs.nasa.gov/search.jsp?R=19830023873 2020-03-21T03:29:13+00:00Z
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SNOW REFLECTANCE FROM THEMATIC MAPPER
(id3- 10391) SHOW RRFLECTANCE FR011 IHEaa1.0 	 1183-32144
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Jeff Dozier
University of California
Santa Barbara
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-2- 	 ORIOINAL PAGE Is
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.
 QUAUTY
SNOW REPUCTANCE FROM THEMATIC MAPPER
MPRQN=ON
In California 75% of the agricultural water supply comes from the melting Sierra
Nevada snowpack. The California Cooperative Snow Survey uses measurements of snow
water equivalent from snow courses, snow depth from aerial survey markers, and
snowcovered area from satellite data to estimate the amount and the timing of the
spring runoff.
Our work on snow reflectance from the TM should lead to improved use of satellites
In snow hydrology.
1) Basin wide albedo measurements from the TM could be used to better forecast the
timing of the spring runoff, because these data can be combined with solar radiation
calculations to estimate the net radiation budget. The TM is better-suited for this
purpose than the MSS because of its larger dynamic range. Saturation still occurs
in bands 1-4, but is only severe in band 1.
2) TM band 5 can discriminate clouds from snow.
3) Measurements of snowcovered area should be better with the TM, because the 30m
spatial resolution can be used to estimate the contiguity of the snowcover above the
snowline.
SPECTRAL ALBEDO-QF SNOW
Calculations of snow reflectance in all 6 TM reflective bands (i.e. 1. 2. 3. 4. 5. and 7),
using a delta-Eddington model T , show that snow reflectance in bands 4, 5, and 7 is sensi-
tive to grain size. An objective in our investigation is to interpret surface optical grain
size of snow, for spectral extension of albedo. Our results so far are encouraging.
Table 1 and Figure 1 show calculations of integrated reflectance for snow over all
reflective TM bands, and water and ice clouds with thickness of lmm water equivalent
over TM bands 5 and 7. In the blue and green bands (1-2) snow reflectance is not sensi-
tive to grain size, so measurements in these wavelengths will show the extent to which
snow albedo is degraded by contamination from atmospheric aerosols, dust, pine pollen,
etc. In the red and near-infrared, snow reflectance is sensitive to grain size but not to
contaminants, so grain size estimates in these wavelengths can be used to spectrally
extend albedo measurements.
The reason that snow reflectance in bands 1 and 2 is not sensitive to grain size is
that ice is so transparent in these wavelengths that increasing the size of a snow crystal
does not significantly change the probability that a photon impinging on the crystal will
be absorbed. Impurities are much more absorptive than ice in these wavelengths, how-
ever, so small amounts of contaminants will affect reflectance 6 . In the near-infrared,
bands 3 and 4, ice !s slightly absorptive, so an incident photon is more likely to be
absorbed if the crystal is larger, and snow reflectance is therefore sensitive to grain size.
Impurities are not so important in these wavelengths because their absorption
coefficients are not much larger than those of ice.
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Table 1
TU Integrated Hen -tenet 60=60°
clears semiW ite snow
Optical grain radius (µm)
band 50 100	 +200	 500 1400
1 .990 .988	 .983	 .974 .963
2 .988 .983	 .977	 .964 .949
3 .978 .969	 .957	 .932 .906
4 .934 .909	 .873	 .809 .741
5 .223 .130	 .067	 .024 .011
7 .197 06	 Ob6	 19 0
I
water cloud, lmm water
optical droplet radius (µm)
band 1 2	 5	 10 20
5 .891 .866	 .769	 .661 .547
7 .784 .750	 .650	 .481 .345
ice cloud, Irnm water equivalent
optical crystal radius (µm)
band 1	 1 2	 5	 10 20
5 .817 .780	 .665	 .513 .383
7 .765 .730	 .642	 .478 .341
• Integrated reflectance p is
Pc f Px f j%(i) Ex d A
a
P=
µ0 f 't '\W Ea d X
0
where pa is direct-beam spectral snow reflectance at illumination angle
130=cos - 'µfl , Ea is spectral solar constant, and 4 A,\W is instrument response
function for band j. (The µ0 's of course cancel.)
DYNAMIC RANGE
Table 2 gives characteristics of the Thematic Mapper, and, for background informa-
tion, the Multispectral Scanner and NOAA Advanced Very High Resolution Radiometer. In
the radiance columns of the table, the quantization errors and saturation radiances of
the sensor bands are compared with the solar constant, integrated through the sensor
response functions. Solar constant spectral distributions are from the NASA standards,
adjusted to f1t the integrated values measured from the Nimbus -7 cavity radiometer of
the earth radiation budget experiment2 . The last column in the table expresses the sen-
sor saturation radiance as a percentage of the solar constant, integrated through the
band response function.
O f POf 	 ()1_1_1At- 1TY	-b-
Table 2
TM. NIM and AVHRR Spectral Characteristics
[spectral distribution of solar constant from Ref. 5]
7Aematic Mapper
wavelengths radiances (Wm-2AM- Isr -1)
bared 59 a sat,	 solar
1 .452	 -	 .516	 .63	 161	 621 25.9
2 .529	 -	 .610	 1.24	 316	 540 56.5
3 .624	 -	 .693	 .95	 241	 468 51.5
4 .776	 -	 .905	 .92	 234	 320 7341
5 1.568	 -	 1.764	 .13	 31.7	 66.5 47.7
7 `-2.097	 2.347	 .067	 16.9	 24.4 69.3
6 10.422	 -	 11.661	 (thermal band)
Landsat -2 Multispectral Scanner
4 .5	 -	 .6 4.0	 259	 574 45.1
5 .6	 -	 7 2.6	 179	 491 36.5
6 .7	 -	 .6 2.3	 149	 401 37.2
7 1	 .8	 -	 1.0 3.0	 192	 285 67.4
NO.4A-r Advanced very ftigh Resolution Radiometer
1 .56	 -	 .72	 .51
	
516	 465 106.8
2 .71	 -	 .98	 .33	 341
	
364 93.7
3 3.53	 3.9A (thermal bands)
}	 =
=
10.3	 1 i.36 
1	 5 11.45	 -	 12.42
For the TM and AVHRR, the solar constant values were integrated over the sen-
sor response f1metions. We do not have response: function data for the MSS, so
the sensor was assumed to have a square-wave response.
Snow will frequently
 saturate in band 1, but in bands 2, 3, and 4 the saturation prob-
lem is not nearly as severe as with the MSS, so the TM can be used to measure snow
albedo and thus allow basin-wide energy budget snowmelt calculations. Bands 5 and 7
will not saturate over snow.
Snow does stretch the dynamic range of the TN however. Figure 2 shows histo-
graens of all 6 reflective bands for the the southern Sierra Nevada on 10 December 1982
(northwest portion of path 41, row 35) and Table 3 lists portions of the image that are
saturated in aIt 7 bands. In band 1 fully 1/8 of the pixels are saturated, and the
saturated portion would increase as sun elevations get higher in the spring.
TM 4
I
TM 2
I
TM 7
too	 Zoo	 soo o	 too	 Zoo	 soo
Radiance Numbers 	 Radiance Numbers
r
Y
.oL . 0
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Agure 2. Histograms of digital radiance numbers for all reflective bands (1-5, 7) for the
scene in Figure 2. Th,: saturation percentages are in Table 3.
•	 i
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ORIGINAL  PAGE IS
OF POOR QUAUYY
Table 3
Sierra Nevada and ate Mountains. 10 Doe ilk
Saturation. TN Beflectivo Bad
percentage
band	 saturated
1	 12.25
2	 0.15
3	 1.40
4	 0.07
5	 0.00
7	 0.00
SNOWIC AM INSCHNNATION
Figure 3 shows TM bands 2 and 5 of the Sierra Nevada. Owens Valley, and the White
Mountains on 10 December 1982 (northwest quarter of path 41, row 35). In band 2 both
snow and clouds are bright, while in band 5 the clouds are bright but snow is dark.
Table 1 and Figure 1 also analyze reflectance of snow and ice clouds. In both
"shortwave infrared" bands, 5 and 7. snow is much darker than clouds, and water clouds
are brighter than ice clouds in band 5. In both of these bands ice is highly absorptive.
and snow reflectance'-ls'low and sensitive to grain size for small sizes, which explains the
higher reflectance of ice clouds than snow. In band 5 water is less absorptive than ices,
so water clouds are more reflective than ice clouds.
EFFECT OF RESMU rION ON SNOWCOVKR NAPPING
The major operational use of satellite data in snow hydrology has been in mapping
snowcovered area, as an index to the amount of snowmelt runoff ,". In mountainous
regions the usual approach has been to use the satellite data to identify the snowline
(above which the ground is snowcovered, below which it is snowfree) and to then measure
the area within the snowline as the snowcovered area.
Unfortunately the elevation of the snowline is not always a good index to the volume
of snow (or snow water equivalence) in the watershed. Sometimes a cold storm deposits
small amounts of snow over a wide area, whereas a warmer storm will deposit much more
snow but over a smaller area. The finer spatial resolution of the Landsat 4 Thematic
Mapper might make it possible to use textural information about the snowcovered area,
and thereby to better use satellite data to estimate the volume of snow in a watershed.
Even above the snowline, there are differences between years of large accumulation and
normal or lean years. Rocks, bushes, trees, etc may or may not be covered. Ridges may
or may not be blown clean. The sptiat contiguUy of the snow is also an index of its
volume, and the TM should help us estimate this.
CONCLUSION
Landsat-4 Thematic Mapper data include spectral channels suitable for snow/cloud
discrimination and for snow albedo measurements that can be extended throughout the
solar spectrum. Except for band 1, the dynamic range io large enough that saturation
occurs only occasionally. The finer spatial resolution gives much better detail on the
snowcovered area.
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l0;>r1:t .NCKS
Hale, C.M., and M.R. Querry, 1973. Optical constants of water in the 200-mm to 200-
µm wavelength region, Applied Optics, 12.555-56:3.
2. Hickey, J.R., L.L. Stowe, H. Jacobowitz, P. Pellegrino, R.H. Maschoff, P. House, and
T.H. VonderHaar, 1980, Initial solar irradiance determinations from Nimbus 7 cavity
radiometer measurements, Science, 203.281-283.
3. Rango, A., and K.I. Itten, 1976, Satellite potentials in snowcover monitoring and
runoff prediction, Nordic Hydrology, 7.209-230.
4. Rango, A., and J. Martine, 1979, Application of a snowmelt-runoff model using
Landsat data, Nordic Hydrology, 10.225-238.
5. Thekaekara, M.P., ed., 1970, The solar constant and the solar spectrum measured
from a research aircraft, NASA TR-R-351.
B. Warren, S.C., and W.J. Wiscombe, 1980, A model for the spectral albedo of snow, 2.
Snow containing atmospheric aerosols, Journal of the Atmospheric Sciences, 37.
2734-2745.
7. Wiscombe, W.J., and S.G. Warren, 1980, A model for the spectral albedo of snow, 1,
Pure snow, Journal of the Atmospheric Sciences, 37.2712-2733.


Snow reflectance from thematic mapper

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