International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
3.1 Error due to forest cover
Tundra
| ait
:|
SWE error (%)
©
-40
-50
OCT NOV DEC JAN FEB MAR APR MAY
Taiga
50
40
s t1
5 3 3 3 3
-10
go | + i
-30
SWE error (%)
OCT NOV. DEC JAN FEB MAR APR MAY
Prairie
SWE error (%)
e
-10 +
zs tt
E -LELI
OCT NOV DEC JAN FEB MAR APR MAY
Alpine
50
40 |
30
= criidd
Br $
SWE error (%)
OCT NOV DEC JAN FEB MAR APR MAY
Maritime
e ; 2 3 55
mes $
SWE error (%)
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OCT NOV DEC JAN FEB MAR APR MAY
Ephemeral
SWE error (%)
©
»3 333833383
OCT NOV DEC JAN FEB MAR APR MAY
Figure 2. SWE overestimation or underestimation for the six Strum classes due to the assumption of constant grain size.
The primary source of systematic error in SWE is the
masking effect of vegetation, which reduces the
brightness temperature difference term in (1). In the
PM portion of the electromagnetic spectrum, the error
due to forest cover is expected to be very high,
upwards of 50%, since the emissivity of the overlying
forest canopy can overwhelm the scattering signal
from the snowpack (Chang et al., 1996; Brown at al.,
2003). Where forests are scant or absent PM estimates
of SWE are more accurate.
For each forested pixel, a fractional forest cover fi is
calculated using the International Geosphere-
Biosphere Program (IGBP) Land Cover Data Set
described by Loveland et al. (2000). These data, at !
km x 1 km, are averaged to the 19 x IP
latitude/longitude grid used in this study. The
Inter
—
pe
ca
cl: