Istanbul 2004
ystals are
1 forested
restimated
as under-
cause the
whelming
The most
» northern
1 the north
st factor in
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
the new algorithm, considerably more SWE is
estimated in the taiga or boreal forest region.
4. CONCLUSIONS
This study corrects an existing SWE model for known
systematic errors. Dense vegetation was shown to be
the major source of systematic error, while
assumptions about snow crystal size and how crystals
evolve with the progression of the season also
contribute significant biases. The proposed unbiased
algorithm is applied to SSM/I data in a case study for
snow season 1990-91, with an associated uncertainty
estimate (not shown here). These results have been
evaluated in taiga, prairie and maritime regions of
Canada using snow data from the Meteorological
Service of Canada. In the most densely forested areas
of the taiga and maritime classes of eastern Canada,
SWE may still be underestimated using the new
algorithm. As more complete data on forest density
becomes available, separate forest factors could be
prescribed for taiga and maritime sub-classes to better
account for SWE in densely forested areas.
Acknowledgements
This work was performed under the auspices of NASA
Grant NASA NRA 99-OES-04.
References
Armstrong, R. L. and M. J. Brodzik, 1995: An earth-
gridded SSM/I data set for cryospheric
studies and global chauge monitoring,
Advances in Space Research Vol. 10, 155-
163.
Brown, R., B. Brasnett and D. Robinson, Gridded
North American monthly snow depth and
snow water equivalent for GCM
evaluation, Atmos.-Ocean, Vol. 41, 1-14,
2003.
Chang, A. T. C., P. Gloersen, T. Schmugge, T,
Wilheit, and J. Zwally, 1976: Microwave
emission from snow and glacier ice,
Journal of Glaciology, Vol. 16, 23-39.
Chang, A.T.C., J.L. Foster and D.K. Hall, 1987:
Nimbus-7 derived global snow cover
parameters, Annals of Glaciology, Vol. 9:
39-44,
Chang, A.T.C., JL. Foster and DK. Hall, 1996:
Effects of forest on the snow parameters
derived from microwave measurements
during the BOREAS winter field
experiment, Hydrological Processes,
Vol.10: 1565-1574.
Chang, A.T.C. and Rango, A. 2000: Algorithm
Theoretical Basis Document (ATBD) for
the AMSR-E Snow Water Equivalent
Algorithm, NASA/GSFC, Nov. 2000.
Foster, J. L., D. K. Hall, A. T. C. Chang, A. Rango,
W. Wergin, and E Erbe, 1999: Effects of
snow crystal shape on the scattering of
passive microwave radiation, IEEE
Transactions on Remote Sensing, Vol.37
(2), 1165-1168.
Kelly, R., and A. Chang, L.Tsang, and J. Foster, 2003:
A prototype AMSR-E global snow area
and snow depth algorithm, IEEE
Transactions on Geoscience and Remote
Sensing, Vol. 41 (2), 230-242.
Loveland, T. R., B. C. Reed, J. F. Brown, D. O. Ohler,
J. Zhu, L. Yang, and J. W. Merchant
(2000): Development of a global land
cover characteristics dataset, International
Journal of Remote Sensing, Vol. 21 (6/7),
1303-1330..
Pulliainen, J. and M. Hallikainen, 2001: Retrieval of
regional snow water equivalent from
space-borne passive microwave
observations, Remote — Sensing of
Environment, Vol. 75: 76-85.
Rango, A., A. Chang, J. Foster, 1979: The utilization
of space-borne radiometers for monitoring
snowpack properties, Nordic Hydrology,
Vol. 10, 25-40.
Sturm, M., J. Holmgren and G. E. Liston, 1995: A
seasonal snow cover classification system
for local to regional applications, Journal
of Climate, Vol. 8, 1261-1283.
Sun, C. I Walker and P. Houser, 2003: A
methodology for initializing snow in a
global climate model: Assimilation of
snow water equivalent observations. J.
Geophys. Res. (In press).
Tsang, L., C. Chen, A.T.C. Chang, J. Guo and K.
Ding, 2000: Dense media radiative transfer
theory based on quasicrystalline
approximation with applications to passive
microwave remote sensing of snow, Radio
Science, Vol. 35: 731-749.
