Applications of Remote Sensing: The Cryosphere (Snow & Ice) Menglin Jin, San Jose Stte University...
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Applications of Remote Sensing:The Cryosphere (Snow & Ice)
Applications of Remote Sensing:The Cryosphere (Snow & Ice)
Menglin Jin, San Jose Stte University
Outline Physical principles International satellite sensors enabling remote sensing of
tropospheric aerosols– ESMR, SMMR, SSM/I, AVHRR, MODIS, AMSR
Instrument characteristics– Spacecraft, spatial resolution, swath width, sensor
characteristics, and unique characteristics Sea ice and snow retrieval from existing satellite systems Future capabilities Opportunities for the future
Credit: Michael D. King, NASA GSFC
Sparsely distributed ice floes as viewed from a ship in the Bering Sea
Sea Ice of Different Forms and Perspectives
Sea Ice of Different Forms and Perspectives
Photograph courtesy of Claire Parkinson
Expansive ice field, as viewed from an aircraft in the central Arctic
Sea Ice of Different Forms and Perspectives
Sea Ice of Different Forms and Perspectives
Photograph courtesy of Claire Parkinson
Close-up of newly formed ice in the Bering Sea
Sea Ice of Different Forms and Perspectives
Sea Ice of Different Forms and Perspectives
Photograph courtesy of Claire Parkinson
Ice floes separated by a lead, as viewed from an aircraft over the central Arctic
Sea Ice of Different Forms and Perspectives
Sea Ice of Different Forms and Perspectives
Photograph courtesy of Claire Parkinson
Thin sheets of ice, as viewed from an aircraft
Sea Ice of Different Forms and Perspectives
Sea Ice of Different Forms and Perspectives
Photograph courtesy of Koni Steffen
Several-months-old ice bearing the weight of a helicopter, as viewed from ground level in the Bering Sea
Sea Ice of Different Forms and Perspectives
Sea Ice of Different Forms and Perspectives
Photograph courtesy of Claire Parkinson
Nimbus 5– Electrically Scanning Microwave Radiometer (ESMR)
» December 1972-1976» single channel (19 GHz = 1.55 cm) conically scanning
microwave radiometer Nimbus 7
– Scanning Multichannel Microwave Radiometer (SMMR)» October 1978-August 1987» 10 channel (five frequency and dual polarization) conically
scanning microwave radiometer Defense Meteorological Satellite Program (DMSP)
– Special Sensor Microwave Imager (SSM/I)» June 1987-present» 7 channel (three frequencies with both vertical and
horizontal polarization + 1 frequency with horizontal polarization only)
Remote Sensing of Sea Ice from Passive Microwave RadiometersRemote Sensing of Sea Ice from Passive Microwave Radiometers
NASA, Aqua– launches July 2001– 705 km polar orbits, ascending
(1:30 p.m.) Sensor Characteristics
– 12 channel microwave radiometer with 6 frequencies from 6.9 to 89.0 GHz with both vertical and horizontal polarization
– conical scan mirror with 55° incident angle at Earth’s surface
– Spatial resolutions:» 6 x 4 km (89.0 GHz)» 75 x 43 km (6.9 GHz)
– External cold load reflector and a warm load for calibration
» 1 K Tb accuracy
Advanced Microwave Scanning Radiometer (AMSR-E)
Advanced Microwave Scanning Radiometer (AMSR-E)
Thicker snow results in lower microwave brightness temperatures
Microwave Scattering of Snow Cover
Microwave Scattering of Snow Cover
From Parkinson, C. L., 1997: Earth from Above
Higher rate of microwave emission from sea ice than from open water
Emissivities indicated are for wavelength of 1.55 cm (19 GHz)
Satellite Detection of Sea IceSatellite Detection of Sea Ice
From Parkinson, C. L., 1997: Earth from Above
Spectra of Polar Oceanic Surfaces over the SMMR Wavelengths
Spectra of Polar Oceanic Surfaces over the SMMR Wavelengths
50
0.50.00
1.0 2.5Wavelength (cm)
Bri
gh
tness
Tem
pera
ture
(K
)
100
150
200
250FY Ice V
FY Ice H
MY Ice V
MY Ice H
Open Ocean H
1.5 2.0 3.0 3.5 4.0 4.5 5.0
Open Ocean V
March 8-10, 1974 September 16-18, 1974
<132.5 K ≥ 281.5 K200 K160 K 240 K140 K 180 K 220 K 260 K
Brightness Temperature of Polar Regions from Nimbus 5 ESMR
Brightness Temperature of Polar Regions from Nimbus 5 ESMR
Tb (19 GHz)Parkinson ( 1997)
March 1986
September 1986
100%
80%
60%
40%
20%
≤12%
Monthly Average Sea Ice Concentrations from Nimbus 7
SMMR
Monthly Average Sea Ice Concentrations from Nimbus 7
SMMR
From Parkinson, C. L., 1997: Earth from Above
March 1986 September 1986100%
80%
60%
40%
20%
≤12%
Monthly Average Sea Ice Concentrations from Nimbus 7
SMMR
Monthly Average Sea Ice Concentrations from Nimbus 7
SMMR
From Parkinson, C. L., 1997: Earth from Above
February 1999 September 1999100%
80%
60%
40%
20%
≤12%
Monthly Average Sea Ice Concentrations from SSM/IMonthly Average Sea Ice
Concentrations from SSM/I
South Polar Region
North Polar Region
Location Maps for North and South Polar Regions
Location Maps for North and South Polar Regions
From Parkinson, C. L., 1997: Earth from Above
Decreases in Arctic Sea Ice Coverage as Observed from
Satellite Observations
Decreases in Arctic Sea Ice Coverage as Observed from
Satellite Observations
C. L. Parkinson, D. J. Cavalieri, P. Gloersen, H. J. Zwally, and J. C. Comiso, 1999: J. Geophys. Res.
November 1978 - December 1996
November 1978 - December 1996
Monthly Arctic Sea Ice Extent Deviations
Monthly Arctic Sea Ice Extent Deviations
–34300 ± 3700 km2/year
C. L. Parkinson, D. J. Cavalieri, P. Gloersen, H. J. Zwally, and J. C. Comiso, 1999: J. Geophys. Res.
Yearly and Seasonal Ice Extent TrendsYearly –2.8%/decadeWinter –2.2%/decadeSpring –3.1%/decadeSummer –4.5%/decadeAutumn –1.9%/decade
Trends in Arctic Sea Ice CoverageTrends in Arctic Sea Ice Coverage
C. L. Parkinson, D. J. Cavalieri, P. Gloersen, H. J. Zwally, and J. C. Comiso, 1999: J. Geophys. Res.
Data Sources For November 1978 – August
1987, the Scanning Multichannel Microwave Radiometer (SMMR) on NASA’s Nimbus 7 satellite
Since mid-August 1987, the Special Sensor Microwave Imagers (SSM/Is) on satellites of the Defense Meteorological Satellite Program
37,000 km2/year decrease of sea ice area over a 19.4 year period observed from satellite
19,000 km2/year decrease in sea ice area over a 46 year period based on Geophysical Fluid Dynamics Laboratory (GFDL) model
Observed Northern Hemisphere Sea Ice Decreases Placed in a
Climate Context
Observed Northern Hemisphere Sea Ice Decreases Placed in a
Climate ContextProbability that an observed sea-ice-extent trend results from natural climate variability, based on a 5000-year control run of the GFDL General Circulation Model (GCM) Open Circle
– Observed 1953-1998 trend, updated from Chapman and Walsh (1993)
Open Square– Observed 1978-
1998 trend, updated from Parkinson et al. (1999)
Sea Ice TrendsSea Ice Trends
Probability that observed trends result from natural climate variability– 1953 – 1998 trend < 0.1 %– 1978 – 1998 trend < 2 %
Demonstrates how scientists have attempted to take the satellite data record and put it into context of man’s impact on climate
Vinnikov, Robock, Stouffer, Walsh, Parkinson, Cavalieri, Mitchell, Garrett, and Zakharov, published in the December 3, 1999 issue of Science
19 GHz Vertical Polarization
<132.5 K ≥ 281.5 K200 K160 K 240 K140 K 180 K 220 K 260 K
Brightness Temperature of Polar Regions from SSM/I
Brightness Temperature of Polar Regions from SSM/I
37 GHz Vertical Polarization
March 14, 1997
<132.5 K ≥ 281.5 K200 K160 K 240 K140 K 180 K 220 K 260 K
Brightness Temperature of Polar Regions from SSM/I
Brightness Temperature of Polar Regions from SSM/I
March 14, 1997
85 GHz Vertical Polarization
180
240200180 220
Brightness Temperature Scatter Diagram for Odden Region and
Greenland Sea
Brightness Temperature Scatter Diagram for Odden Region and
Greenland SeaEarly Ice Maximum Extent of
Bulge
260 240200180 220 260Brightness Temperature (V37) Brightness Temperature (V37)
Bri
gh
tness
Tem
pera
ture
(V
19
)
200
220
240
260November 21, 1996
January 18, 1997
O O
A
A
D
D
Odden
Stu
dy
Are
a
(pan
cakes
or
nilas
)
Thick ic
e
(conso
lidate
d regio
n)
180
240200180 220
Brightness Temperature Scatter Diagram for Odden Region and
Greenland Sea
Brightness Temperature Scatter Diagram for Odden Region and
Greenland SeaMaximum Extent of
TongueIce Melt & Formation of Ice
Island
260 240200180 220 260Brightness Temperature (V37) Brightness Temperature (V37)
Bri
gh
tness
Tem
pera
ture
(V
19
)
200
220
240
260March 14, 1997
April 14, 1997
Brightness Temperature of Polar Regions from SSM/I
Brightness Temperature of Polar Regions from SSM/I
February 26, 1987
SSM/I AVHRR
Brightness Temperature of Polar Regions from SSM/I
Brightness Temperature of Polar Regions from SSM/I
March 15, 1987
SSM/I AVHRR
Pucahirca, Peru– October 1991– Latitude of 9°S– Foreground altitude is
5325 m
Photograph courtesy of Lonnie Thompson
Snow Cover in the Northern AndesSnow Cover in the Northern Andes
Nimbus 7/SMMR– Uses two horizontally polarized microwave frequencies (18
and 37 GHz)» snow scatters less at the lower frequency (longer
wavelength)» the thicker the snow the greater the difference in
brightness temperature between 18 and 37 GHzz = 1.59[Tb(18 GHz) – Tb(37 GHz)]
wherez = snow thickness in cm
» restricted to ice-free land with snow thickness 5 ≤ z ≤ 70 cm
Remote Sensing of Snow Cover & Thickness from Passive Microwave
Radiometers
Remote Sensing of Snow Cover & Thickness from Passive Microwave
Radiometers
February 1986 March 1986
70 cm
55 cm
40 cm
25 cm
10 cm
≤4 cm
Monthly Average Snow Thickness from Nimbus 7 SMMR
Monthly Average Snow Thickness from Nimbus 7 SMMR
From Parkinson, C. L., 1997: Earth from Above
April 1986 May 1986
70 cm
55 cm
40 cm
25 cm
10 cm
≤4 cm
Monthly Average Snow Thickness from Nimbus 7 SMMR
Monthly Average Snow Thickness from Nimbus 7 SMMR
From Parkinson, C. L., 1997: Earth from Above
Location Map for North Polar Region
Location Map for North Polar Region
From Parkinson, C. L., 1997: Earth from Above
February 1979 February 1981
70 cm
55 cm
40 cm
25 cm
10 cm
≤4 cm
Monthly Average Snow Thickness from Nimbus 7 SMMR
Monthly Average Snow Thickness from Nimbus 7 SMMR
From Parkinson, C. L., 1997: Earth from Above
NOAA/AVHRR-3– Uses reflectance at 1.6 µm where snow and ice absorb solar
radiation much greater than water or vegetation» Advantage
• high spatial resolution (4 km GAC, 1.1 km LAC)» Disadvantage
• affected by cloud cover• observations possible only at night• difficult to detect snow in deep forests
Terra/MODIS– Uses reflectance at 1.6 µm– Higher spatial resolution of AVHRR (global at 1 km)– Makes use of better cloud mask for distinguishing clouds
from snow and land surfaces (and shadows)
Remote Sensing of Snow Cover from Shortwave Infrared
Radiometers
Remote Sensing of Snow Cover from Shortwave Infrared
Radiometers