The Landsat Data Continuity Mission(LDCM):
January 2012
Landsat 8
Not your fathers Landsat
John R Schott, Aaron Gerace and Nima Pahlevan
Sponsor: United States Geological Survey (USGS) and NASA/Goddard
LDCM carries two pushbroom instruments:
The 9 band Orbital Land Imager (OLI) and
The 2 band Thermal Infrared Sensor (TIRS)
ETM+
•
•
OLI
Same swath width, same resolution as L 7
global coverage, merged OLI-TIRS product
2
Size
Matters
Rochester Embayment (Lake Ontario)
Terra‐MODIS (500m)
Landsat 7&8 (30m)
Daily coverage
16 day repeat
Free web access
Free web access
3
3
LDCM Features: New bands
LDCM Response
1
Response
0.8
0.6
0.4
0.2
0
350
850
1350
Wavelength
1850
2350
4
LDCM Features: 12 bit Quantization
vs. 8 bit on L7
Landsat 7 (8-bit)
OLI (12-bit)
5
46 vs. 68 units of chlorophyll
OLI’s pushbroom
design leads to
significantly better
SNR
Mean/Std
SNR for ~ 3% refelector
350
300
250
200
150
100
50
0
Blue
Green
OLI
Red
CA
L7
NIR
0
1
2
3
4
5
6
Band numbers
6
Modeling the Constituent
Retrieval Process: Hydrolight
Solar location
Sensor location
Wind Speed
Water IOPs
CHL
SM
CDOM
-Absorb
-Scatter
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Modeling the Constituent
Retrieval Process: At the Sensor
Hydrolight
output
Sensor
response
Spectral
sampling
Add Noise
Quantize
AVIRIS
ETM+
OLI
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Modeling the Constituent
Retrieval Process: CHL
Modtran
0
0
0
.5
.5
.5
1
1
.75
3
2
1
5
4
2
7
8
4
12
10
7
24
14
10
46
20
12
68
24
14
M
Top of Atmosphere
CDOM
(g/L)
SM
(mg/L)
CHL=3
SM=4
CDOM=7
CDO
M
Air/Water Interface
CHL
9
Interpolation Process
λ
TRUE
min [(ST - SP)2 ]
FALSE
LUT
SQ Error
[CHL]
[CHL] [SM] [CDOM]
[CDOM ]
[ SM ]
Sp predicted
10
Modeling the Constituent
Retrieval Process: Summary
Spectral
Hydrolight Sensor
Output response sampling
AVIRIS
X 2000
Add Noise
Quantize
Constituent
retrieval
ETM+
OLI
– Average residuals can be expressed as a percent of the total range of constituents.
CHL [0 – 68], SM [0 – 24], CDOM [0 – 14]
– 10% error is our target for this experiment.
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Results: Perfect atmospheric
compensation
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SNR Margins
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Results: Perfect atmospheric
compensation
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Atmospheric Compensation Issues
Hydrolight-generated water pixels as seen
through a 40km visibility atmosphere.
•
•
(Left) TOA Radiance, (Right) Resampled to six LDCM bands.
Three Atmospheric compensation algorithms developed to take
advantage of deep blue band ,near zero reflectance in NIR/SWIR and
model based empirical line method (ELM).
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OLI Atmospheric Compensation
Experiment 3: Real Data
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Temperature is a driving factor impacting
hydrodynamics and therefore materials
transport and water quality
True Color Composite
Thermal Channel
Cold center
Warm ring
•
Hydrodynamic models calibrated with surface thermal maps can
help bridge temporal gaps and provide estimates of subsurface
behavior
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One Possible Solution to Landsa’st Temporal Resolution
– Integrate Landsat data with hydrodynamic (HD) modeling
 Hydrodynamic modeling, when calibrated, can compensate low temporal resolution of
Landsat
 A well‐calibrated model enables pre‐casting and forecasting of the state of the environment
 Hydrodynamic models can estimate 3‐D flow and material transport
360‐hours gap of imagery
HD
Day 1
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Day 16
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Hydrodynamic Modeling: Inputs
– Nudging Vectors (hourly).
• Whole lake simulation provides nudging vectors for small scale simulation.
ALGE Hydrodynamic
Model
Lake Ontario simulation: Surface Currents
Landsat 5: July 13th, 2009
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Collaborations for 2012, 2013
& 2014 Field Seasons
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