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Estimation of Daily Surface Reflectance Over the United States from the SeaWiFS
Sensor
Sean RaffuseUnder the direction of Rudolf Husar
Thesis presented to the Henry Edwin Sever Graduate School of Washington University in partial fulfillment of
the requirements of the degree of Master of ScienceMay 23, 2003
April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
Outline
• Goal
• Introduction
• Approach
• Methodology
• Results
• Discussion
Goal• Development of a procedure for the automated production of daily surface
reflectances from SeaWiFS satellite data• Applications of surface reflectance data
– Vegetation mapping– Aerosol retrieval– Radiative balance/climate
• Domain of study– Continental United States– April – August 2000
Introduction – SeaWiFS Satellite Platform
• SeaStar satellite maps the world daily in 24 polar swaths, carrying the Sea-viewing Wide Field-of-view Sensor (SeaWiFS)
• The 8 channels of the sensor are in the transmission windows between the atmospheric gas absorption bands in the visible & near IR
Swath
2300 KM
Polar Orbit: ~ 1000 km, 99 min.
Equator Crossing: Local Noon
Radiation detected by satellites
• Air scattering depends on geometry and can be calculated (Rayleigh scattering)
• Clouds completely obscure the surface and have to be masked out
• Aerosols redirect incoming radiation by scattering and also absorb a fraction
• Surface reflectance is a property of the surface
Apparent Surface Reflectance, R• The surface reflectance R0 is obscured by aerosol scattering and absorption before it reaches the sensor
• Aerosol acts as a filter of surface reflectance and as a reflector solar radiation
Aerosol as Reflector: Ra = (e-– 1) P
R = (R0 + (e-– 1) P) e-
Aerosol as Filter: Ta = e-
Surface reflectance R0
• The apparent reflectance , R, detected by the sensor is: R = (R0 + Ra) Ta
• Under cloud-free conditions, the sensor receives the reflected radiation from surface and aerosols
• Both surface and aerosol signal varies independently in time and space
• Challenge: Separate the total received radiation into surface and aerosol components
Spectra of surface reflectances
• Surface reflectance R0 is dependent on wavelength, surface type, and scattering angle
• Aerosol (haze) modifies sensed reflectance
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.4 0.6 0.8 1
Wavelength (m)
Ref
lect
ance Chlorophyll
Absorption
VegetationSoil
Water
Scattering angle correction 1
• Surface reflectance is dependent on sun-target-sensor angle (non-Lambertian)
• Time series shows dependence
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
1-Aug 11-Aug 21-Aug 31-Aug 10-Sep 20-Sep 30-Sep 10-Oct
Ch
an
ne
l 6 R
efl
ec
tan
ce
120
130
140
150
160
170
180
Sc
att
eri
ng
An
gle
(d
eg
ree
s)
Reflectance Scattering Angle
Scattering angle correction 2
0.20.220.240.260.28
0.30.320.340.360.38
0.4
1-Aug 11-Aug 21-Aug 31-Aug 10-Sep 20-Sep 30-Sep 10-Oct
Ch
an
ne
l 6 R
efl
ec
tan
ce
Uncorrected Corrected
Uncorrected Variance = 0.0010
Corrected Variance = 0.00026
• Pixels are normalized to a scattering angle of 150°
Preprocessing
Transform raw SeaWiFS data • Georeferencing – warping data to
geographic lat/lon coordinates with a pixel resolution of ~ 1.6 km
• Splicing – mosaic data from adjacent swaths to cover entire domain
• Rayleigh correction – remove scattering by atmospheric gases and convert to reflectance units
• Scattering angle correction – normalize all pixels to remove reflectance dependence on sun-target-sensor angles
Result is daily apparent reflectance, R for all 8 channels
Approach – Time Series Analysis 1
• For any location (pixel), the sensor detects a “clean” day periodically– Aerosol scattering (haze) is near zero, thus R ≈ R0
– Pixel must also be free of other interferences• Clouds• Cloud shadows
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
4/15/00 5/25/00 7/4/00 8/13/00 9/22/00 11/1/00
Re
fle
cta
nc
e
Daily Raw Reflectance Presumed Surface
R = (R0 + (e-– 1) P) e-
Approach – Time Series Analysis 2
Methodology – Cloud shadows• Clouds are easily detected by their high reflectance values• Cloud shadows are found in the vicinity of clouds• We enlarge the cloud mask by a three-pixel ‘halo’ to remove cloud
shadows• Cloud shadows reduce the apparent surface reflectance considerably in all
channels
Methodology – Preliminary anchor days• Surface reflectance is retrieved for individual pixels from time series
data (e.g. year) • The procedure first identifies a set of ‘preliminary clear anchor’ days
in a 17-day moving window– The main interferences (clouds and haze) tend to increase the apparent
surface reflectance, especially in the low wavelength channels– The anchor day is chosen as the day with the minimum sum of the
lowest four channels
Methodology – Refinement/Interpolation• Anchor days are further refined using a jump filter on the channel 1 (blue)
time series– Surface reflectance in channel 1 does not change rapidly– Channel 1 is strongly affected by haze– If an anchor day is over 0.025 reflectance units greater than the previous good
anchor day, it is assumed to be influenced by haze and is removed.• Values are interpolated between the refined anchor days to create the daily
surface reflectances
Methodology – Residual haze reduction 1• In some regions, aerosol haze is persistent throughout over long periods
e.g. Southeast in the summer
• Anchor days chosen by the routine may still contain small amounts of haze, especially vegetation and water pixels
• Spectral analysis is used to reduce the residual haze over these surfaces
Methodology – Residual haze reduction 2
• Vegetated surfaces do not have a channel 1 reflectance greater than 0.03
• Haze increases the apparent reflectance most in channel 1 and somewhat less at each subsequent band
• Vegetation pixels with excess channel 1 reflectance are reduced to 0.03
• All other channels are reduced proportionately
Methodology – Residual haze reduction 3• Hazy water pixels are reduced using the assumption that water is
completely black (reflectance = 0) at channel 8 (near-IR)• The residual haze reduction does not completely eliminate haze, but
provides a good estimate
Process Flow Diagram
Create Geometry
Rayleigh Correction
Scattering Angle Corr.
A B Conversion
Georeference L1B Georef. geometry
Filter bad pixels
L1A
GeometryFile
FilteredL1A
L1B
WarpPoints
GeoreferencedL1B
GeoreferencedGeometry
Daily RadianceImage
Rayleigh CorrectedReflectance
Splice, merge, crop
Mask clouds
Enlarge cloud mask
Daily Reflectance
CloudMask
Cloud/Cloud Shadow Mask
InitialAnchor Points
Select anchor points
Clean SurfaceReflectance
Refine anchor points
Haze Reduction
RefinedAnchor Points
SurfaceImages
Interpolate
Inputs multiple swaths from a single dayOutputs single file
Inputs all daily files from the time spanOutputs single file
Inputs single fileOutputs daily file for each day in the time span
Key
Calibration
File
Elevation
Data
April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
Results – Seasonal surface reflectance, Eastern US
April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
Results – Seasonal surface reflectance, Western US
Results – Seasonal surface reflectance urban pixels
Results – Eight month animation
Adjacent pixels show similar values in some areas, more variability in others
Results – Spatial variation: 9 pixel rectangle
Results – Comparison with clean air mass• Surface reflectance estimates should be similar to apparent reflectance
values for days with clean air
Daily reflectance Surface reflectance Ch1 difference
Channel 1 difference is near zero except at clouds and areas of rapid surface change (max difference ~ 0.02)
Discussion - Advantages
• Resolution independent – adaptable to other datasets that operate at different resolutions that provide appropriate spectral coverage (available bands near 0.4, 0.6, and 0.85 m)
• Fully automated, requiring no user input once initiated
• Spatial, spectral, and temporal resolution of the sensor data are maintained
• Minimal need for a priori aerosol knowledge
• Detects surface color change on the order of days/weeks when cloud free data exist
Discussion – Limitations
• Requires 30 – 60 consecutive days of input data
• Does not fully remove the haze influence from the surface reflectance
• Currently tuned to SeaWiFS
Limitations – Cloud shadows• Some cloud shadows remain in the surface reflectance data
• Could be removed in future studies with a final spike filter on the time
series.
Daily Image
Surface Reflectance
Limitations – Georeferencing
• Quality of results is dependent on accuracy of georeferencing• Process preferentially selects dark pixels, creating a spreading effect at
sharply contrasting images
Daily Image Surface reflectance
Future Work
• Test other regions and years– Compare year-to-year results
• Improve cloud shadow filtering
• Aerosol retrieval– Using surface reflectance data and aerosol model
• Refined surface reflectance– Using retrieved aerosol
Acknowledgements
• Fang Li
• Eric Vermote
• Rudolf Husar
Thank You!