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Synergistic Use of Datasets fromMultiple Instruments for Earth
Science Research
Ivan A. Csiszar, University of Maryland : HazardsSimon J. Hook, Jet Propulsion Lab. : Hydrology - PresenterVince J. Realmuto, Jet Propulsion Lab : HazardsThomas Schmugge, New Mexico State University: Ecology
Outline• Hazards
– Mapping volcanic plumes (V. Realmuto)– Active Fire monitoring (I. Csiszar)
• Ecology– Calculating broadband emissivities for climate models (T.
Schmugge)• Hydrology
– Measuring nutrient distribution in large lakes (S. Hook)• Critical factors limiting use of multi-instrument data• Wrap-up and ways to increase multi-instrument studies
OBJECTIVES OF PLUME MAPPING
Track Changes in SO2 Emission RateDetect Passive Emissions Before an Eruption Occurs
Eruptions May be Preceded by Changes in Emission Rate
Few Volcanoes are Monitored with Necessary Frequency toEstablish Baseline Emission Rates
Satellite Remote Sensing –> Facilitate Monitoring
Study the Fate of SO2 in AtmosphereConversion to Sulfate Aerosols
Local/Regional Hazard to Respiratory Health
Regional/Global Climate Forcing
Nucleation Sites for Polar Stratospheric Clouds ->Catalysts for Ozone Depletion
Mapping Passive SO2Emissions from SpacePu’u O’o Plume MapDerived from ASTER 90mTIR Data
High Spatial Resolution (90m) => High Sensitivity toPresence of SO2
Mitigating Factors
Small Plume: typically 1 kmin thickness and width
Low Altitude (typically 1.5km asl): Low TemperatureContrast
Warm, Humid TropicalAtmosphere: DecreasedTemperature Contrast,Increased AtmosphericAbsorption and Emission
Mapping Passive SO2Emissions from SpacePu’u O’o Plume MapDerived from ASTER 90mTIR Data
High Spatial Resolution (90m) => High Sensitivity toPresence of SO2
Mitigating Factors
Small Plume: typically 1 kmin thickness and width
Low Altitude (typically 1.5km asl): Low TemperatureContrast
Warm, Humid TropicalAtmosphere: DecreasedTemperature Contrast,Increased AtmosphericAbsorption and Emission
AIRS Data Acquired over Mount Etna EruptionPlume: 28 October 2002Constituents of Volcanic Plumes Amenable to SatelliteRemote Sensing: SO2, Silicate Ash, Sulfate Aerosol
Rare in “Normal” Atmosphere - Relatively LowConcentrations Can Be Detected in the Thermal IR (TIR)
Forward ModelingResultsMODTRAN, CHARTS,LBLTran run at 1 cm-1
resolution
SO2 concentrationsbetween 0.25 – 0.50mg/m3
Silicate Ash Loading >>Sulfate Aerosol Loading
3 November 2002
ASTER VNIR (15 m)True-Color Composite
2002-2003 Eruption Of Mount Etna
27 Oct 2002 – 29 Jan 2003
Terra/Aqua Record:
At least one daytime MODIS overpass perday
At least one daytime AIRS overpass every2 days
Two MISR overpasses (one day apart)every 16 days
90 ASTER acquisitions between June andDecember 2002
TIR False-ColorComposite
True-Color Composite
Plume Top Altitude: 6 kmPlume Base Altitude: 5 km
TIR False-Color CompositeIndicates Dominance of ashover SO2
MODIS-Terra
27 October 200210:02 UTC
MODIS-Aqua
28 October 2002, 12:15 UTC
Plume Top Altitude: 6 kmPlume Base Altitude: 5 km
TIR False-Color CompositeIndicates Dominance of ashover SO2
TIR Color Composite
True Color Composite
AIRS28 October 2002
Plume Top Altitude: 6 kmPlume Base Altitude: 5 km
AIRS Misfit to Data is ~ 2XThat of MODIS
Need to Upgrade Version ofMODTRAN Used in MAP_SO2
True Color Composite
MODIS-Aqua vs. AIRS28 October 2002
MODIS SO2 Map Re-Sampled to ~ 17 km
Lower Spatial Resolution of AIRS Results in LessSensitivity to Small (~ 3 g/m2) Changes in SO2 Burden
MODTRAN Upgrade will Improve the Sensitivity of AIRS-Based SO2 Retrievals
True Color Composite
TIR Color Composite
MODIS-Terra
29 October 2002, 09:45 UTC
Plume Top Altitude: 6 kmPlume Base Altitude: 5 km
TIR False-Color CompositeIndicates Dominance ofAsh over SO2
SUMMARY REMARKS…
Retrievals Appear to be ConsistentThree Days of Activity; Three Instruments
MAP_SO2 Does Not Introduce Systematic Bias
AIRS-Based Retrievals in General Agreement withMODIS- Based Retrievals
Future EffortsFocus on Days with Terra and Aqua Overpasses(eg. 27 Oct 2002)
Begin ASTER Processing (30 December 2002)
Incorporation of MISR-Based Plume Geometry
MODTRAN Upgrade
Incorporation of AIRS-Based AtmosphericProfiles
Key Benefits
• ASTER can be used for detecting active firesthat are much smaller than the lower MODISdetection limit
• ASTER and MODIS on same platform (perfecttemporal coincidence)
• Disadvantages: ASTER has limited angularrange, saturates (partly addressed with multiplegain settings) and limited availability
Coincident MODIS and ASTER activefire observations
ASTER, 8-3-1 RGB (30m) + 1km MODIS grid:MODIS detections in color
July 23 2002 03:18 UTC62.57N 125.72E (Siberia)
MODIS v4 detection:
Yellow gridcells:“fire”, high confidence
Blue gridcells:“fire” nominal confidence
Gridcell with vertical shading:“cloud”
Black gridcells:“clear land”
Csiszar et al., submitted
Morisette et al., in press
ASTER imageand MODIS grid
of BrazilianACRE file
Aug 29 2003 14:56 UTC9.71S 67.15W (Brazil)
Note: fires visiblethrough clouds in lowerleft corner. No standardASTER fire products
Morisette et al., in press
ASTER firemask and
MODIS grid
Aug 29 2003 14:56 UTC9.71S 67.15W (Brazil)
ASTER firedetectionalgorithmdeveloped byLouis Giglio
Comparison with coincident high-resolution satellite observations
Probabilities of detection as a function ofASTER fire pixels within MODIS pixel
?á: MODIS “fire”; ?�:MODIS “no fire”
P(MNF|AF)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 100 200 300 400 500 600 700ASTER threshold
Pro
babi
lity
Pixel-based accuracy assessmentcurve with 95% exact confidenceintervals: omission error rate
NPOESS/VIIRS/LANDSAT active fire validation!
Csiszar et al., submitted
Calculating BroadbandEmissivities for Climate Models
Tom SchmuggeNew Mexico State University
Approach: Extend from small to largetarget, from ASTER to MODIS
ASTER TIR White Sands May 21, 2001
GroundMeasure-mentSite
Red > 10.7 umGrn > 9.1 umBlu > 8.6 um
Study area: Sahara Desert in NorthAfrica
200 scenes of ASTER data acquired from 2000 to2002Size of area 400 x 1500 km
35°
30°
25°
5° 10° 15°
study area
ASTER emissivity imageCh.10 8.3 µm Ch.11 8.6 µm Ch.12 9.1 µm Ch.13 10.7 µm Ch.14 11.3 µm
0.6
1.0
0.8
0.7
0.9
Emissivity of ASTER channels
Broad band EmissivityDerived FromRegression of
5 ASTER Bands
0.85
1.00
0.90
0.95
Comparison: MODIS Estimated vsASTER Observed
Bias = -0.0083RMSE = 0.0181
MODIS
ASTER
Conclusions
• ASTER and TES work very well– Quantitative agreement ( 1 – 2%) withlab and field measures at White Sands
– ASTER Results are repetitive– Emissivity mapping on a regional scale– Extend to Global scale with MODIS
Infrastructure:• Four NASA buoys• Two UCD buoys• Multiple meteorological stationsaround lake
Temperature Trace from TR4 forJanuary of 2000TR4 Jan 6 2000 - Feb 10 2000
4.00
4.50
5.00
5.50
6.00
6.50
7.00
7.50
8.00
1/7/0010:30
1/11/0010:30
1/15/0010:30
1/19/0010:30
1/23/0010:30
1/27/0010:30
1/31/0010:30
2/4/0010:30
2/8/0010:30
Time
h303007
MODIS 7/21/2001, 19:00 GMT MODIS 7/22/2001, 06:05 GMT
ASTER 7/22/2001, 06:05 GMTETM+ 7/21/2001, 18:28 GMT
Current Tracking withLandsat ETM+ and ASTERData using MaximumCorrelation Method
Critical Factors Limiting Use ofMulti-Instrument Data
• Interoperability/Protocols/Standards• Information Assurance and Security• Hardware/Software• Infrastructure/Bandwidth• Human and Institutional Capacity
From: Strategic Plan for the U.S. Integrated EarthObservation System
Critical Factors – Real World• Geospatial data but cannot be accessed geospatially
– Need to subset down to pixel level when order!• Every pixel needs a latitude and longitude
– No subset lattices or corner points – use double precision.• Data stored in multiple projections
– Need default, common projection• Data ordering should not require a human in the loop
– Implement SIMPLE subscription service• Software lacks scripting
– More toolkits with scripting support e.g. Python and PIL• Open access
– Allow users to FTP for free small subsets e.g. USGS NED approach
Wrap-Up• Need Leadership from the top input with input
from the bottom.• Need clearly articulated vision for where we want
to go and why.• Need a system which can be used by the
individual without requiring large institutionalinfrastructure
• Need clear milestones with tangible results– Roadmaps, Data Fusion working group?
With Special Thanks to:
Ivan A. Csiszar, University of MarylandAshley Davies, Jet Propulsion LabVince J. Realmuto, Jet Propulsion LabRob Sohlberg, University of MarylandThomas Schmugge, New Mexico State University
