Co-retrieval of Aerosol and Surface Reflectance:Analysis of Daily US SeaWiFS Data for 2000-2002

Sean Raffuse, Erin Robinson and Rudolf B. Husar CAPITA, Washington University

Presented at A&WMA’s 97th Annual Conference and ExhibitionJune 22-27, Indianapolis, IN

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

SeaWiFS Satellite Platform and Sensors

• Satellite maps the world daily in 24 polar swaths

• The 8 sensors are in the transmission windows in the visible & near IR

• Designed for ocean color but also suitable for land color detection, particularly of vegetation

Swath

2300 KM

24/day

Polar Orbit: ~ 1000 km, 100 min.

Equator Crossing: Local NoonChlorophyll Absorption

Designed for Vegetation Detection

The image was synthesized from the blue (0.412 μm), green (0.555 μm), and red (0.67 μm) channels of the 8 channel SeaWiFS sensor. Air scattering has been removed to highlight the haze and surface reflectance.

Aerosol effects on surface colorand

Surface effects on aerosol color

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

General Approach: Co-Retrieval of Surface and Aerosol Reflectance

1. Surface Reflectance Retrieval by Time Series Analysis – (Sean Raffuse, MS Thesis 2003)

2. Aerosol Retrieval over Land – Radiative transfer model + Surface data

3. Refined Surface Reflectance

– Iteration back to 1., 2. …

Scattering angle correction 2

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0.30.320.340.360.38

0.4

1-Aug 11-Aug 21-Aug 31-Aug 10-Sep 20-Sep 30-Sep 10-Oct

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ance

Uncorrected Corrected

Uncorrected Variance = 0.0010

Corrected Variance = 0.00026

• Pixels are normalized to a scattering angle of 150°

Approach – Time Series Analysis

• For any location (pixel), the sensor detects a “clean” day periodically– Aerosol scattering (haze) is near zero– Pixel must also be free of other interferences

• Clouds• Cloud shadows

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

Surface Reflectance in Blue & Red, Illinois

• Haze perturbation of the surface reflectance is most pronounced at 0.41 m, ‘blue’

• In some cases, haze is evident in blue, but not in red (0.67 m).

• Hence, the blue channel is used to identify the anchor days.

• For the selected days, the pixel’s reflectance is retained for each of the 8 channels (need better explanation)

Clouds

Haze

Methodology – Residual haze reduction

• In some regions, aerosol haze is persistent throughout over long periods e.g. Southeast in the summer

• Spectral analysis is used to reduce the residual haze over these surfaces

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 – Eight month animation

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

Aerosol Retrieval: Idaho Smoke Event, Aug 2000

1. Generate daily total reflectance image with air reflectance removed, R

2. Generate surface reflectance image, R0

3. Subtract daily total reflectance image from surface reflectance image to get aerosol optical thickness,

4. Filter , removing clouds and other interferences

)ln(0 PR

PR

--

-=

July 5, 2002 July 6, 22002

July 7, 2002 July 8, 2002

July 9, 2002 July 10, 2002

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

Satellite Applications for US AQ Management:Pros and Cons

• Geographic Coverage– Pros:

• High spatial resolution of (~ 1 km)• Global domain (covers intercontinental transport)

–  Cons:• Low fraction (~50%), spatially spotty and unpredictable (wherever clouds are)

• Vertical Coverage– Pros:

• Vertical integral: Provides climate-relevant integral parameter, e.g. Aerosol Optical Thickness (AOT)

– Cons:• Vertical integral: Not easily related to surface aerosol concentration

• Temporal Coverage– Pros:

• Time resolution (30 min for GOES, 24 hrs for Wide-POS, 7 days for Narrow-POS)• Time domain (1980s for AVHRR, 1990s for TOMS, 2000s for Terra/Aqua suite)

–  Cons:• Time resolution (30 min for GOES, 24 hrs for Wide-POS, 7 days for Narrow-POS)• Time domain (1980s for AVHRR, 1990s for TOMS, 2000s for Terra/Aqua suite)•

• Parameter Coverage

• Dependence on Humidity: Sulfates, nitrates and other hygroscopic aerosols produce humidity-dependent satellite signal (haloes).