MAPPING SNOW AND ICE FROM GEOSTATIONARY SATELLITES: GETTING READY FOR GOES-R
1
MAPPING SNOW AND ICE FROM GEOSTATIONARY SATELLITES: GETTING READY FOR GOES-R MAPPING SNOW AND ICE FROM GEOSTATIONARY SATELLITES: GETTING READY FOR GOES-R Peter Romanov 1,2 and Dan Tarpley 1 1 Office of Research and Applications, NOAA/NESDIS 2 Cooperative Institute for Climate Studies, University of Maryland e-mail: [email protected], www: http://www.orbit.nesdis.noaa.gov/smcd/emb/snow/HTML/snow.htm Background Automated snow-mapping with GOES Imager Daily GOES-based snow cover products Physical basis - Specific spectral signature of snow, high reflectance in the visible (vis) and low reflectance in the middle- infrared (Mid-IR), which is different from spectral reflectance of most other natural surfaces. - Observations in the infrared (IR) are used to assess the surface temperature and to separate the reflected component in the Mid-IR outgoing radiance. Known limitations - Day-time only method - Clouds obscure the scene - Dense forests mask snow cover Spectral reflectance of natural surfaces. 1 .0 1 0 .0 W avelen g th , µm 0 20 40 60 80 100 R efle ctan c e (% ) GOES ch .1 GOES ch .2 w ater so il veg etatio n snow Basics of snow detection in vis/mid-IR/IR Other snow products G O ES Im ager VIS/M IR /IR 30 m in obs. D A ILY SN O W MAP (Snow /N o snow /C loud) IM AGE CO M PO SITING SNOW IDENTIFICATIO N (Spectralthresholds) TEMPORAL STA B ILITY TEST (“Potentialsnow ” only) GOES Snow mapping scheme SI=R 1 /R 3 : snow index R 1 : visible reflectance (0.6 m) R 2 : middle infrared reflectance (3.9 m) T 4 : infrared brightness temperature (10.7 m) R 1T and SI T are location dependent T 4 < 230 K SI > SI T and R 1 > R 1T and R 2 < 5% and T 4 < 290K Snow Yes Yes No No T 4 <265K No Yes Cloud T 4 >285K Yes Land R 1 >25% or R 2 >10% Yes No No GOES Snow identification Snow cover distribution and snow cover properties (particularly, snow cover fraction, depth and SWE) are critical boundary conditions for numerical weather prediction and hydrological models. Snow cover and snow pack properties are important for climate and for various environmental studies. Since 1966 NESDIS has produced interactive maps of snow cover over the Northern Hemisphere. However, the increasing demand of the modeling community for higher spatial and temporal resolution of information on the snow cover has stimulated development of automated satellite-based techniques for snow mapping and monitoring. These techniques were initially intended and are now used to complement the interactive product and to facilitate the work of a human analyst, but eventually are expected to replace the interactive technique. Observations from the Imager instrument onboard Geostationary Operational Environmental Satellites (GOES) are among major sources of data utilized by NESDIS analysts when generating interactive snow and ice cover maps. This poster presents the progress in automated snow and ice mapping with GOES data. Perspectives for using the GOES-R Advanced Baseline Imager (ABI) to improve snow and ice maps are discussed. Utilizes observations in the vis (0.6μm), Mid-IR (3.9 μm) and IR (11.5 μm) spectral bands Makes maximum use of frequent observations available from GOES Ice cover mapping was added in 2004. The algorithm is similar to the one for snow Snow mapping algorithm Russia Mongolia China Russia Mongolia Using Meteosat-8 (MSG) SEVIRI as GOES-R ABI prototype Getting ready for GOES-R GOES-based blended daily snow maps. Year 2000 snow season Duration of the snow season Snow cover over South America Far East snow maps from GOES-Pacific (GOES-9) Daily RGB composite Red: R2 (MidIR); Green:R1(vis); Blue: T4 (IR) Snow map Green: land White: snow Blue: water Gray: clouds Surface observations : Red: non-zero snow depth Yellow: no snow on the ground GOES-9 Feb 24, 2004 Feb 24, 2004 Accuracy of snow maps : The agreement between GOES-based automated snow maps and ground-based snow cover observations ranges from 92% to 95% depending on the season, the land surface cover type and the snow depth. Snow cover fraction - Derived from Ch.1 (visible) reflectance - Sub-pixel snow fraction - Linear mixture approach F = (R obs - R land ) / (R snow - R land ) - 10% accuracy (estimated) Daily snow fraction map Tree cover fraction (UMd data) for areas affected by frequent seasonal snow Max snow fraction for areas affected by seasonal snow. Multiyear composited image Max snow fraction is strongly correlated with the tree cover fraction (R=0.79), Snow cover temperature Helps to identify areas of snow melt. Spring-time snow melt in Quebec Snow depth Model: D = exp (aF+b) -1, where a and b are 3 rd degree polynomials of the forest fraction, D is the snow depth, F is snow fraction Snow depth vs snow fraction for different forest cover fraction. Deciduous forest. Over non-forested and sparsely forested areas the snow fraction is mostly controlled by grass protrusions through the snow pack and therefore is closely related to the snow depth. Correlation between snow fraction and snow depth is also observed over moderately forested areas (up to 50-80% forest cover fraction). The relationship between snow depth and snow fraction was established from matched surface and satellite observations. The accuracy of snow depth estimates is about 30%. Retrievals are generally limited to 30-50 cm of snow depth. Snow depth map over Great Plains and Canadian prairies. Light gray: clouds Dark gray: forested area Daily image compositing: helps to reduce cloud contamination - Observations with maximum IR brightness temperature (T 4 ) are retained (“max temperature” compositing) Snow Identification - Threshold-based decision-tree algorithm (see picture on the right) - Retrievals are checked against the land surface temperature climate data and snow cover climatology Temporal stability test - Looks for observations, that are spectrally similar to the “max temperature” observation - Observations are spectrally similar if ΔT 4 < 10K, ΔR 1 < 5% and ΔR 2 < 1.5% - Three spectrally similar observations are needed to flag the pixel as “confirmed snow” Details Snow map features Generated daily at 4 km resolution Coverage up to 66 0 N Daily snow maps have gaps due to clouds North America blended snow/ice maps Focus on Great Lakes Green: land, White: snow, Blue: water, Gray: clouds Yellow: ice, Red: broken ice Blending is performed by filling in cloudy pixels in the current day snow map with the most recent cloud-clear retrievals RGB composite Red: R2 (MidIR), Green: R1(vis), Blue: T4 (IR); Snow-covered surfaces appear in green and dark green Snow/ice map Green: land, White: snow, Blue: water Yellow: ice, Gray: clouds North America snow/ice maps from combined GOES-E and GOES-W data Snow in New Mexico Texas New Mexico Colorado Texas New Mexico Colorado Left: RGB composite Right: Snow map Feb 25, 2004 Feb 25, 2004 Snow depth distribution. Snow depth reported from ground-based stations is shown in black Stations reporting snow depth in Great Plains area. Forest cover fraction is shown in shades green Mar, 14 Mar, 16 Mar, 18 Mar, 20 Mar, 22 Mar, 24 Feb 21, 2005 Feb 21, 2005 Required in estimates of albedo of snow covered areas 0 20 40 60 80 100 S n o w fra c tio n , % 0 10 20 30 40 50 S n o w d ep th ,cm F o re s t C o v er, % 0 1-1 0 10-20 20-40 40-60 60-79 80 and over This poster is available for download at http://www.orbit.nesdis.noaa.gov/smcd/emb/snow/presentations/GOES- R_2006_poster.ppt Improvements are expected owing to (1) Larger selection of spectral channels, particularly - Near-Infrared (0.88μm): better detection of snow in forests - Shortwave infrared (1.6μm): better snow-cloud discrimination than with 3.9 μm channel. - Split window channels: a more accurate estimation of the land surface temperature Location of ground-based stations used for snow map validation 1 2 3 F o re st co ve r ca te go ry 0 5 10 15 20 25 30 T o ta ld isa g re e m e n t, % C om issio n e rro rs, % O m issio n e rro rs , % G O E S sn o w product 1 2 3 F o re st c o v e r ca teg ory 0 5 10 15 20 25 30 T o ta ld isa g re e m e n t, % C o m iss io n e rro rs , % O m is sio n e rro rs , % M O D IS sn o w product (2) On-board calibration of shortwave channels - Will enhance potentials for detecting long-term trends in the snow extent Errors of snow mapping with GOEs Imager and MODIS Terra estimated through comparison with ground-based data. Forest cover categoriesare (1): Below 20% tree cover*, (2)2: From 20% to 50% tree cover, (3). Over 50% tree cover Nov-2000 J a n -2 00 1 M a r-2 0 01 M ay-2001 -1 0 -8 -6 -4 -2 0 A rea ch an g e , % S en s itiv ity lo ss 2% 5% 10% N ov-2000 Jan-2001 M a r-2 00 1 M ay-2001 -80 0 -60 0 -400 -200 0 A re a, th o u san d s sq . k m . S en s itiv ity lo ss 2% 5% 10% Five percent of unaccounted sensitivity loss of the visible sensor, causes up to 3% error in the total mapped snow extent. For comparison, the average rate of decrease of the snow cover extent over North America in the last 30 years was about 0.25%/year. The average year-to-year variation in the total snow extent was 4.2%. With 16 channels the accuracy of snow/ice mapping with GOES-R ABI should be similar to the accuracy of snow/ice mapping with MODIS onboard Terra and Aqua satellites. (3) Higher spatial resolution - Will improve ability to reproduce small-scale variability in the snow cover/snow depth distribution Highly desired: (4) Improvement in the navigation and registration of the image data. With current errors of registration between repeated images of up to 6 km within 24 hours, max temperature image compositing causes the “loss” of some snow along the snow line. Decrease of the mapped snow cover area (in percent, left and in square km, right) due to an unaccounted sensitivity loss of the visible sensor. GOES data over North America. Estimates are made for the 2000-2001 winter season MSG SEVIRI spectral bands: No. λ(μm) 1 0.635 (*) 2 0.81 (*) 3 1.64 (*) 4 3.90 (*) 5 6.25 6 7.35 7 8.70 8 9.66 9 10.80 (*) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data from MSG satellite can be used for testing a snow and ice mapping algorithm for GOES-R ABI. SEVIRI makes observations in all spectral bands that will be utilized for mapping snow and ice from GOES-R, i.e, visible, near IR, shortwave IR, Mid IR and far IR. MSG (automated) IMS (interactive) MSG blended snow map: latest cloud clear observation is retained for every location Snow cover distribution derived from MSG data with an automated algorithm agrees well to NOAA interactive snow cover analysis data (IMS system). 1-Jan 21-Jan 10 -F e b 2-M ar 22-M ar 11-A pr 1-M ay 21-M ay Y e a r 2 0 0 5 0 100,000 200,000 300,000 A re a , km ^2 M e t-8 (a uto ) s n o w cover IM S (in te ra ctive ) s n o w cover
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Yes. T 4 < 230 K. No. SI > SI T and R 1 > R 1T and R 2 < 5% and T 4 < 290K. Yes. No. GOES-9. Yes. T 4 285K. Texas. No. Yes. R 1 >25% or R 2 >10%. No. New Mexico. Colorado. Texas. Land. New Mexico. Feb 25, 2004. - PowerPoint PPT Presentation
Transcript of MAPPING SNOW AND ICE FROM GEOSTATIONARY SATELLITES: GETTING READY FOR GOES-R
MAPPING SNOW AND ICE FROM GEOSTATIONARY SATELLITES: GETTING READY FOR GOES-RMAPPING SNOW AND ICE FROM GEOSTATIONARY SATELLITES: GETTING READY FOR GOES-RPeter Romanov1,2 and Dan Tarpley1
1Office of Research and Applications, NOAA/NESDIS2Cooperative Institute for Climate Studies, University of Maryland
e-mail: [email protected], www: http://www.orbit.nesdis.noaa.gov/smcd/emb/snow/HTML/snow.htm
Background
Automated snow-mapping with GOES Imager
Daily GOES-based snow cover products
Physical basis- Specific spectral signature of snow, high reflectance in the visible (vis) and low reflectance in the middle-infrared (Mid-IR), which is different from spectral reflectance of most other natural surfaces. - Observations in the infrared (IR) are used to assess the surface temperature and to separate the reflected component in the Mid-IR outgoing radiance.
Known limitations - Day-time only method - Clouds obscure the scene - Dense forests mask snow cover Spectral reflectance of natural surfaces.
1 . 0 1 0 . 0Wavelength, µm
0
20
40
60
80
100
Ref
lect
ance
(%
)
GOESch.1
GOESch.2
water
soil
vegetation
snow
Basics of snow detection in vis/mid-IR/IR
Other snow products
GOES ImagerVIS/MIR/IR30 min obs.
DAILY SNOW MAP(Snow / No snow / Cloud)
IMAGECOMPOSITING
SNOWIDENTIFICATION
(Spectral thresholds)
TEMPORALSTABILITY TEST
(“Potential snow” only)
GOES Snow mapping scheme
SI=R1/R3: snow index
R1: visible reflectance (0.6 m)
R2: middle infrared reflectance (3.9 m)
T4: infrared brightness temperature (10.7 m)
R1T and SIT are location dependent
T4 < 230 K
SI > SIT and R1 > R1T andR2 < 5% and T4 < 290K
Snow
Yes
Yes
No
No
T4<265K
No
Yes
Cloud
T4>285KYes
Land
R1>25% or R2>10%
Yes
No
No
GOES Snow identification
Snow cover distribution and snow cover properties (particularly, snow cover fraction, depth and SWE) are critical boundary conditions for numerical weather prediction and hydrological models. Snow cover and snow pack properties are important for climate and for various environmental studies.
Since 1966 NESDIS has produced interactive maps of snow cover over the Northern Hemisphere. However, the increasing demand of the modeling community for higher spatial and temporal resolution of information on the snow cover has stimulated development of automated satellite-based techniques for snow mapping and monitoring. These techniques were initially intended and are now used to complement the interactive product and to facilitate the work of a human analyst, but eventually are expected to replace the interactive technique.
Observations from the Imager instrument onboard Geostationary Operational Environmental Satellites (GOES) are among major sources of data utilized by NESDIS analysts when generating interactive snow and ice cover maps. This poster presents the progress in automated snow and ice mapping with GOES data. Perspectives for using the GOES-R Advanced Baseline Imager (ABI) to improve snow and ice maps are discussed.
Utilizes observations in the vis (0.6μm), Mid-IR (3.9 μm) and IR (11.5 μm) spectral bands
Makes maximum use of frequent observations available from GOES
Ice cover mapping was added in 2004. The algorithm is similar to the one for snow
Snow mapping algorithm
Russia
Mongolia
China
Russia
Mongolia
Using Meteosat-8 (MSG) SEVIRI as GOES-R ABI prototype
Getting ready for GOES-R
GOES-based blended daily snow maps. Year 2000 snow season
Duration of the snow season
Snow cover over South America
Far East snow maps from GOES-Pacific (GOES-9)
Daily RGB composite
Red: R2 (MidIR); Green:R1(vis); Blue: T4 (IR)
Snow map
Green: landWhite: snowBlue: waterGray: clouds
Surface observations:
Red: non-zero snow depthYellow: no snow on the ground
GOES-9
Feb 24, 2004
Feb 24, 2004
Accuracy of snow maps: The agreement between GOES-based automated snow maps and ground-based snow cover observations ranges from 92% to 95% depending on the season, the land surface cover type and the snow depth.
Snow cover fraction
- Derived from Ch.1 (visible) reflectance- Sub-pixel snow fraction- Linear mixture approach F = (Robs - Rland) / (Rsnow - Rland)- 10% accuracy (estimated)
Daily snow fraction map
Tree cover fraction (UMd data) for areas affected by frequent seasonal snowMax snow fraction for areas affected by
seasonal snow. Multiyear composited image
Max snow fraction is strongly correlated with the tree cover fraction (R=0.79),
Snow cover temperatureHelps to identify areas of snow melt.
Spring-time snow melt in Quebec
Snow depth
Model:
D = exp (aF+b) -1,
where a and b are 3rd degree polynomials of the forest fraction, D is the snow depth, F is snow fraction
Snow depth vs snow fraction for different forest cover fraction. Deciduous forest.
Over non-forested and sparsely forested areas the snow fraction is mostly controlled by grass protrusions through the snow pack and therefore is closely related to the snow depth. Correlation between snow fraction and snow depth is also observed over moderately forested areas (up to 50-80% forest cover fraction). The relationship between snow depth and snow fraction was established from matched surface and satellite observations. The accuracy of snow depth estimates is about 30%. Retrievals are generally limited to 30-50 cm of snow depth.
Snow depth map over Great Plains and Canadian prairies. Light gray: clouds Dark gray: forested area
Daily image compositing: helps to reduce cloud contamination
- Observations with maximum IR brightness temperature (T4) are retained (“max
temperature” compositing)
Snow Identification
- Threshold-based decision-tree algorithm (see picture on the right)
- Retrievals are checked against the land surface temperature climate data and snow cover
climatology
Temporal stability test
- Looks for observations, that are spectrally similar to the “max temperature”
observation
- Observations are spectrally similar if ΔT4 < 10K, ΔR1 < 5% and ΔR2 < 1.5%
- Three spectrally similar observations are needed to flag the pixel as “confirmed snow”
Details
Snow map features
Generated daily at 4 km resolution
Coverage up to 660N
Daily snow maps have gaps due to clouds
North America blended snow/ice maps Focus on Great Lakes
Green: land, White: snow, Blue: water, Gray: cloudsYellow: ice, Red: broken ice
Blending is performed by filling in cloudy pixels in the current day snow map with the most recent cloud-clear retrievals
RGB compositeRed: R2 (MidIR), Green: R1(vis), Blue: T4 (IR);Snow-covered surfaces appear in green and dark green
Snow/ice mapGreen: land, White: snow, Blue: waterYellow: ice, Gray: clouds
North America snow/ice maps from combined GOES-E and GOES-W data
Snow in New Mexico
Texas
New Mexico
Colorado
Texas
New Mexico
Colorado
Left: RGB composite
Right: Snow map
Feb 25, 2004Feb 25, 2004
Snow depth distribution. Snow depth reported from ground-based stations is shown in blackStations reporting snow depth in
Great Plains area. Forest cover fraction is shown in shades green
Mar, 14
Mar, 16
Mar, 18
Mar, 20
Mar, 22
Mar, 24
Feb 21, 2005
Feb 21, 2005
Required in estimates of albedo of snow covered areas
0 20 40 60 80 100Snow fraction, %
0
10
20
30
40
50
Sn
ow
dep
th,c
m
Forest Cover, %
0
1-10
10-20
20-40
40-60
60-79
80 and over
This poster is available for download at http://www.orbit.nesdis.noaa.gov/smcd/emb/snow/presentations/GOES-R_2006_poster.ppt
Improvements are expected owing to
(1) Larger selection of spectral channels, particularly
- Near-Infrared (0.88μm): better detection of snow in forests- Shortwave infrared (1.6μm): better snow-cloud discrimination than with 3.9 μm channel. - Split window channels: a more accurate estimation of the land surface temperature
Location of ground-based stations used for snow map validation
1 2 3Forest cover category
0
5
10
15
20
25
30
Tot
al d
isag
reem
ent,
%
C om ission errors, %
O m ission errors, %
GOES snow product
1 2 3Forest cover category
0
5
10
15
20
25
30
Tot
al d
isag
reem
ent,
%
C om ission errors, %
O m ission errors, %
MODIS snow product
(2) On-board calibration of shortwave channels
- Will enhance potentials for detecting long-term trends in the snow extent
Errors of snow mapping with GOEs Imager and MODIS Terra estimated through comparison with ground-based data. Forest cover categoriesare (1): Below 20% tree cover*, (2)2: From 20% to 50% tree cover, (3). Over 50% tree cover
Nov-2000 Jan-2001 Mar-2001 May-2001
-10
-8
-6
-4
-2
0
Are
a ch
ang
e, %
Sensitivity loss
2 %
5 %
1 0 %
Nov-2000 Jan-2001 Mar-2001 May-2001
-800
-600
-400
-200
0
Are
a, t
ho
usa
nd
s sq
. km
.
Sensitivity loss
2%
5%
10%
Five percent of unaccounted sensitivity loss of the visible sensor, causes up to 3% error in the total mapped snow extent. For comparison, the average rate of decrease of the snow cover extent over North America in the last 30 years was about 0.25%/year. The average year-to-year variation in the total snow extent was 4.2%.
With 16 channels the accuracy of snow/ice mapping with GOES-R ABI should be similar to the accuracy of snow/ice mapping with MODIS onboard Terra and Aqua satellites.
(3) Higher spatial resolution - Will improve ability to reproduce small-scale variability in the snow cover/snow depth distribution
Highly desired: (4) Improvement in the navigation and registration of the image data. With current errors of registration between repeated images of up to 6 km within 24 hours, max temperature image compositing causes the “loss” of some snow along the snow line.
Decrease of the mapped snow cover area (in percent, left and in square km, right) due to an unaccounted sensitivity loss of the visible sensor. GOES data over North America. Estimates are made for the 2000-2001 winter season
MSG SEVIRI spectral bands:
No. λ(μm)
1 0.635 (*) 2 0.81 (*) 3 1.64 (*) 4 3.90 (*) 5 6.25 6 7.35 7 8.70 8 9.66 9 10.80 (*) 10 12.00 (*) 11 13.40 12 VIS
(*) Used insnow/ice mapping
Spinning Enhanced Visible and Infrared Imager (SEVIRI) data from MSG satellite can be used for testing a snow and ice mapping algorithm for GOES-R ABI. SEVIRI makes observations in all spectral bands that will be utilized for mapping snow and ice from GOES-R, i.e, visible, near IR, shortwave IR, Mid IR and far IR.
MSG (automated) IMS (interactive)
MSG blended snow map: latest cloud clear observation is retained for every location
Snow cover distribution derived from MSG data with an automated algorithm agrees well to NOAA interactive snow cover analysis data (IMS system).
1-Jan 21-Jan 10-Feb 2-Mar 22-Mar 11-Apr 1-May 21-MayYear 2005
0
100,000
200,000
300,000
Are
a, k
m^
2
M et-8 (auto) snow cover
IM S (in teractive) snow cover
