Transcript Armstrong

Multi-Sensor Snow Mapping and
Global Trends
R. L. Armstrong, M.J. Brodzik, M. Savoie and K. Knowles
University of Colorado, Boulder, USA
WDC for Glaciology, Boulder – 30th Anniversary Workshop
Boulder, Colorado 25 October 2006
Outline
Legacy Data Sets for Hemispheric-Scale Snow Mapping
Cross-calibration of SMMR and SSM/I
Comparison of Optical and Microwave Snow Cover Time Series
Snow Mapping using Blended Optical and Passive Microwave
Trend Analysis
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Source for visible data: NOAA Weekly/Daily Snow Charts since 1966
Longest time-series of any parameter derived from satellite remote sensing
NOAA IMS – Interactive Multisensor Snow and Ice Mapping System –
http://www.ssd.noaa.gov/PS/SNOW
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Source for passive microwave data
SMMR, SSM/I &AMSR-E Brightness
Temperatures 1978-2006
EASE-Grid
(Equal-Area Scalable Earth Grid)
Full Global, North and South Hem. Projections
NOAA/NASA Pathfinder Level 3 Gridded Data
Available from NSIDC: http://nsidc.org
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Northern Hemisphere Satellite-Derived Snow Extent
1978 – 2006 Visible (NOAA) Passive Microwave (SMMR & SSM/I)
microwave (green/blue) vs. visible (pink) sensors,
Analysis of the microwave data is complicated by the change in sensors from
SMMR to SSM/I
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SMMR
SSM/I
Sensors are similar but exhibit important differences in spatial and temporal
coverage that affect the sampling density within the long-term record.
Daily passive microwave 37 GHz, horizontally polarized brightness temperatures,
July 19, 1987, showing smaller coverage area of SMMR (left) vs. SSM/I (right).
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SMMR-SSM/I Sensor Cross-Calibration Issues
• The SMMR swath width was about half that of SSM/I, further
reducing the probability of overlapping observations.
• Sensor overlap occurred in July and August 1987 (Northern
Hemisphere summer, essentially no seasonal snow cover).
• The overlap is about 40 days, effectively 20 days of data, since
SMMR was cycled on/off on a daily basis.
• Low latitude sites may require up to 5 or 6 days for one SMMR
overpass
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Tibet Plateau
snow extent
snow water
equivalent
Time series of Tibetan Plateau snow-covered area and average snow water equivalent (SWE)
derived from passive microwave sensors, 1978-2004.
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Time Series of 36/37 GHz at Dome C Antarctica, SMMR-SSM/I-AMSR-E
Time series of 36/37 GHz, vertically-polarized “cold pass” brightness temperatures at
Dome C (Antarctica) from SMMR (1978-1987, orange), various SSM/Is (1987-2006,
pink/brown/green), and AMSR-E (2002-2006, blue). Note cross-sensor calibration issues,
indicated by higher mean and compressed annual amplitude in SMMR data, compared to
SSM/I and AMSR-E.
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Sensor Cross-Calibration: Locating Suitable Calibration Targets over Land
Select land targets with surface characteristics that represent the cold through warm microwave
emission range. The final targets are chosen for temporal and spatial stability. This is done by way
of statistical analysis within a moving 3x3 array of pixels to determine the specific locations with
minimal spatial and temporal variability.

A sub-array ‘footprint’ area was defined as a set of 3 x 3 grid cells (approximately 75 km x 75 km) for a given day, with indices j, centered at a
particular grid point

The following statistics were considered:

Regions analyzed consisted of 100 x 100 arrays of 25-km EASE-Grid cells centered at 1.5°S, 21.6°E (Salonga, Zaire) and 75.0°S, 123.3°E
(Dome C, Antarctica)

Over land, the spatial homogeneity and temporal stability of brightness temperatures (Tb) over selected regions such as tropical forest and ice
sheet regions were examined
Spatial mean and standard deviation of Tb within a footprint on day i:
mi 
1
N
N
Tbij ;
i 
j1
1 N
 (Tbij  mi )2
N 1 j1
Temporal means (over one year) of mi and i:
1
m
ND

ND
 mi
;
i1
1
 
ND
+
ND
 i
i1
+
Temporal standard deviation of mi over one year:

m 
1
ND
 (mi  m )2
N D 1 i1
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Tropical Forest - Salonga, Zaire
Method to identify location of max. spatial and temporal
stability - Tb statistics,central African tropical forest.
(a) Upper left:
(K). (b) Upper right:  (K).
m
m
(c) Lower left: (K). (d) Lower right: Surface

topography (meters
above sea level).
Avg Daily Footprint Mean
m


Stddev of Daily Footprint Mean (time)
m

Statistics for the year 2003 at Salonga, Zaire, for
10 GHz and 37 GHz vertical polarizations,
descending passes: (a) Foot print mean, mi ; (b)
Foot print standard deviation, i. Units are in
Kelvins.
mi

Avg Daily Footprint Stddev (space) Avg Footprint Elevation

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i
Scatter plots of SSM/I vs. SMMR brightness temperature at stable targets
Scatter plots of SSM/I vs. SMMR brightness temperatures (19/18 GHz, left, and 37 GHz, right) at
Earth targets selected for spatial stability (Dome C (Antarctic ice sheet), Salonga (African tropical
forest), Canada (plains), Summit (Greenland ice sheet)).The large plus sign in each plot represents the
typical range (+/- 1 standard deviation) of wintertime brightness temperatures in seasonally snowcovered regions.
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SSM/I vs. AMSR-E
Comparison of NASA Aqua AMSR-E (36 GHz) and DMSP SSM/I (37
GHz) Dome-C Antarctica, June 2002 to December 2005.
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Visible vs. Microwave Sensors
Monthly snow extent climatology for NOAA and passive microwave data
(50% or more of the weeks in the particular month over the period of record classified as
snow covered).
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Blend of microwave and visible data -
Mean monthly snow extent and SWE for 1978-2005 from a blend of
passive microwave (SMMR & SSM/I) and visible (NOAA) data
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NSIDC Global Monthly EASE-Grid Snow Water Equivalent Climatology
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SSM/I + MODIS
AMSR-E + MODIS
Fall 2003
P.M. (grey) +
MODIS %
Winter 2003
Blended snow prototypes, SWE (from SSM/I (left side) and AMSR-E (right side)) with snow cover from MODIS,
Fall & Spring 2003. Top panels in each set of four represent SWE (mm), with additional area indicated as snow by
MODIS in red. Lower panels represent passive microwave snow extent in grey, with percent area of additional
pixels that MODIS classifies as snow in blues.
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Improved Shallow Snow Detection through Application of 89 GHz
AMSR-E Only
AMSR-E + MODIS
AMSR-E + MODIS +
89 GHz (Nagler)
Example of improvement in shallow snow SWE estimates by inclusion of 89 GHz data in combination with MODIS snow-covered area,
October 24-31, 2003. Top row includes standard SWE algorithm results, with zoomed area in Northern Europe and Western Russia.
Middle row includes additional snow-covered area (red), determined from at least 25% of component MODIS CMG pixels indicating snow
cover. Bottom row includes shallow SWE derived from additional information available from the 89 GHz channel. (select day w/ max diff
37 – 85 GHz)
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Trends
analysis includes:
•
•
autoregression, to avoid conclusions with false confidence in
significance, and
•
number of years required to detect significant trends (Weatherhead,
et al. 1998)
Changes in snow cover over the last several decades are being
detected, although the message is mixed: no clear hemispheric trends,
some regional trends, dependency on time period involved, consistent
decreasing trends in spring/summer months.
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There is no evidence of
a significant trend in
either series from
1978-2005, > 17 more
years required.
Northern Hemisphere Visible-Derived Snow Extent Standardized Anomalies
There is evidence of a significant decreasing trend in the full series (1966-2005)
of visible-derived snow cover, -1.7 %/decade
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For Western 11 U. S.
States, there is
evidence to suggest
significant decreasing
trends in both series
considered from 19782005, - 9 %/decade.
Western US Visible-Derived Snow Extent Standardized Anomalies
but no evidence of a significant trend in the full series (1966-2005) of visiblederived snow cover.
WDC for Glaciology, Boulder – 30th Anniversary Workshop
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Continuing Work, Multi-Sensor
• Refine cross-calibration of SMMR and SSM/I
brightness temperatures.
• Cross-calibrate SSM/I and AMSR-E and merge the
historical snow cover times series with NASA
EOS data and products.
• Continue work on optimal blending MODIS and
AMSR-E data including use of 89 GHz.
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Continuing Work, Trends
 Run snow extent trend analysis for specific
regions of interest, Arctic Basin, Tibet
Plateau, sub-regions within USA etc.
 Produce trend surface maps for snow
cover duration and snow water equivalent
to identify regions of greatest change.
 Compare results with surface temperature
trends.
 Latest results to be presented at Fall AGU
2006 (U09)
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Comparison of Monthly SWE Derived from SSM/I and the CCIN
Gridded North American Data Set, August 1987 – June 1997
(Brown, Brasnett and Robinson, 2003)
November
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th
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Anniversary Workshop
Comparison of Monthly SWE Derived from SSM/I and the CCIN
Gridded North American Data Set (Brown, Brasnett and Robinson, 2003)
January
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th
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Anniversary Workshop
Comparison of Monthly SWE Derived from SSM/I and the CCIN
Gridded North American Data Set (Brown, Brasnett and Robinson, 2003)
March
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Bare Dry Soil
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Medium Depth Snow (10-25 cm)
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Wet Snow
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Antarctica - Dome C
AMSR-E 10 GHz vertical polarization Tb statistics
for East Antarctica, descending passes, in 2003.
(a) Upper left: m (K). (b) Upper right:  m (K).
(c) Lower left: (K). (d) Lower right: Surface
topography (meters above sea level).
Avg Daily
 Footprint Mean
m

Stddev of Daily Footprint Mean (time)

m
Statistics for the year 2003 at Dome ‘C’,
Antarctica, for 10 GHz and 37 GHz vertical
polarizations: (a) Footprint mean, mi; (b)
Footprint standard deviation, i. Units are in
Kelvins.
mi

Avg Daily Footprint Stddev(space) Avg Footprint Elevation

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i
Statistics Definitions for Stable Targets Study
•
Footprint mean = the mean of the brightness temperatures of each grid cell
in a footprint.
•
Footprint standard deviation =spatial variance at an instant in time or how
the grid cells change in brightness temperature over the footprint. Needs to
be small to have a homogeneous target.
•
Average daily footprint mean = time average of the footprint mean, e.g. over
one year
•
Standard deviation of the daily footprint mean = standard deviation of the
footprint mean over time. Sites with a low SDDFM when the time period is
long are sites that generally have low seasonal brightness temperature
variation.
•
Average daily footprint standard deviation = the average of each footprint
standard deviation over some time series. Sites that are spatially
homogeneous or have low brightness temperature gradients across a
footprint will have low values of ADFSD. An area with a large ADFSD would
not be a suitable calibration target, such as locations near land/water
boundaries.
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Fall Season Example
Blended snow product prototypes, SWE (mm) from AMSR-E with additional snow extent from
MODIS in red (October 24-31, 2003, max). Lower image represents AMSR-E snow extent in grey, with
percent area of additional pixels that MODIS classifies as snow in blues. (AMSR-E @ 25 km, MODIS
CMG @ 5 km)
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Winter Season Example
Blended snow product prototypes, SWE (mm) from AMSR-E with additional snow extent from
MODIS in red (Feb 26 – Mar 5, 2003, max). Lower image represents AMSR-E snow extent in grey,
with percent area of additional pixels that MODIS classifies as snow in blues. (AMSR-E @ 25 km,
MODIS CMG @ 5 km)
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Fall Season Example
October 24-31, 2003
AMSR-E Only
AMSR-E + MODIS
Mask area where MODIS detects snow
and select day w/ max diff 37 – 89 GHz
to minimize atmospheric influence
AMSR-E +MODIS+ Nagler
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Passive Microwave Remote Sensing of Snow
• Radiation emitted from the soil is scattered by the snow cover
• Scattering increases in proportion to amount (mass) of snow
• Brightness temperature decrease, negative spectral gradient,
polarization difference
Tb = ε * T p
(brightness temperature equals emissivity x physical temperature)
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Northern Hemisphere monthly average snow water equivalent derived from SSM/I
(left) and AMSR-E (right), November 2003 – February 2004.
SSM/I
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AMSR-E
Snow Cover Mapping on the Tibetan
Plateau Using Optical and Passive
Microwave Satellite Data
R. L. Armstrong, M.J. Brodzik, M. Savoie, K. Knowles
University of Colorado and World Data Center for
Glaciology, Boulder -- USA
Asian Conference on Permafrost, Lanzhou, China, August 7-9, 2006
Acknowledgements – Research Support: NASA
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Microwave
Visible
01 Nov 2001
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06 Dec 2001
Tibetan Plateau, Winter vs. Summer
November
July
\ Climatology differences, November and July, showing the anomalous snow
cover to be a cold-season phenomenon and not due to land cover type.
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Tibetan Plateau, Air Temperature and Frozen Soil
Clockwise from upper left: SWE derived from SSM/I, ECMWF average daily
surface temperatures, MODIS snow cover with frozen soil derived from
SSM/I, and NOAA
IMS product.
30, 2001)
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Possible Solution: Atmospheric Correction
Atmosphere should be
considered when validating
satellite retrieval algorithms
based on surface
and low-alt. aircraft
Measurements (Wang&
Manning, 2003)
Conversely, atmosphere needs
to be “removed” when applying
algorithms empirically adjusted
to perform through a full
atmosphere at very high
elevations.
Atmospheric correction to
microwave spectral
gradient, modelled at
altitudes of 1 km and 4 km.
James Wang, GSFC
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AMSR-E SWE, Before & After Atmospheric Correction
Result of atmospheric correction, November 29, 2003. SWE derived from uncorrected
AMSR-E (left) and corrected AMSR-E (right).
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Tibet Plateau Region Snow Cover
Jan 9-16, 2003
AMSR-E
SSM/I
MODIS
NOAA
IMS
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Snow Cover Derived from SSM/I January 9-16 2003
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Snow Cover Derived from AMSR-E January 9-16 2003
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