MONITORING HISTORICAL MOUNTAIN SNOWPACK EXTENT ACROSS WESTERN NORTH AMERICA:
CLIMATE DATA RECORD (CDR) DEVELOPMENT FROM LANDSAT AND MODIS
Christopher J. Crawford (1), (1) NASA Earth and Space Science Fellow, Department of Geography, University of Minnesota, Minneapolis, MN 55455
Problem Statement
The seasonal and perennial cyrosphere is showing longer-term retraction
in response to climatic warming. For western North America, this translate
into seasonal mountain snowpack extent and depth decline. Most of the
evidence for historical mountain snowpack decline originates from the
Snow Course-SNOTEL SWE observing network. Fortunately, satellite
remote sensing offers a means to spatially monitor mountain snowpack
change over remote mountainous terrain.
1. Satellite CDR Development
With a decadal satellite image archive now accessible back to the early 1970s
and growing daily, CDR development is urgent. A method for mountain
snowpack CDR construction from the Landsat mission (MSS, TM, ETM+) is
proposed. Five key image-processing steps are required before snow cover
area (SCA) can be estimated.
1) Image Registration
2) TOA reflectance conversion
3) Cloud Masking
4) Shadow Masking
5) Topographic Normalization
The Normalized Difference
Snow Index (NDSI) and ISO
data clustering are used to
retrieve and binary classify
snow cover present or absent.
Figure 3. Landsat SCA CDR during peak snowmelt for study region: a) June 1 SCA
estimates for full spatial domain; b) full domain standard normal SCA estimates; c) percent
visible coverage for full spatial domain; d) June 1 SCA estimates for alpine domain (>2500 m);
e) alpine domain standard normal SCA estimates; f) percent visible coverage for alpine domain.
Years 1973, 1974, 1978-1982, 1987, and 1988 are missing SCA and are marked with line ticks.
Figure 1. Central Idaho - southwestern Montana study region.
2. Cross-sensor Snow Map Validation
Snow map validation is imperative for establishing spatial and
temporal CDR accuracy. Landsat SCA estimates were compared
with MODIS Terra Collection 6 (C6) fractional snow cover (FSC)
estimates during the 2000s using linear fit modeling.
Figure 5. “Goodness of fit” with residuals between MODIS Terra C6 FSC and Landsat SCA for each map
comparison date.
3. Ground-based Climate Data Integration
Landsat SCA estimates during peak snowmelt were compared with June 1 bimonthly SNOTEL SWE and CRU TS3.1 mean monthly temperature and total
monthly precipitation for previous November through current June.
Figure 6. Scatterplots between A) full spatial domain and B) alpine domain June 1 Landsat
SCA and regional June 1 SNOTEL SWE, CRU TS 3.1 May mean temperature and March
total precipitation PC-1 (left to right) for 114-1120 W, 44-450 N during 1975-2011.
4. Discussion and the Way Forward
Figure 2. Possible Landsat image combinations for SCA estimation: a) cloudfree SCA map; b) partially cloudy SCA map; c) partially cloudy SCA map
with SLC-off; and d) partially cloudy SCA map with missing coverage.
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Figure 4. MODIS Terra C6 FSC and Landsat TM-ETM+ SCA map comparisons:
a) cloud-free; b) partially cloudy; c) thin snow and cloud cover disagreement;
d) temporary snow event disagreement.
High-resolution visible CDRs of mountain snowpack extent can be
developed from the Landsat mission. Landsat SCA and MODIS Terra FSC
show strong snow map agreement and multi-senor interoperability. Groundbased integration between Landsat SCA and gridded surface climate data
using least-squared regression techiquues yielded climatically significant
results. Satellite CDRs can be used to examine climate-driven snowpack
accumulation and melt trends since the early 1970s across western North
America.