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!