Co-Retrieval of Surface Color and Aerosols from SeaWiFS Satellite Data

Outline of a Seminar Presentation at EPAMay 2003

Sean Raffuse and Rudolf HusarCAPITA, Washington University, St. Louis, MO

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

Components of the Sensed Radiation

1. Air scattering depends on geometry and can be calculated (Rayleigh scattering)

2. Clouds completely obscure the surface and have to masked out

3. Aerosols redirect incoming radiation by scattering and also absorb a fraction

4. Surface reflectance is a property of the surface but it is modified by aerosols

Apparent Surface Reflectance, R

R = (R0 + (e-– 1) P) e-

• The surface reflectance R0 is modified by aerosol scattering and absorption

• Aerosol acts as a filter of surface reflectance and as a reflector solar radiation

• The apparent reflectance , R, detected by the sensor is: R = (R0 + Ra) Ta

Aerosol as Reflector: Ra = (e-– 1) P

Aerosol as Filter: Ta = e-

Surface reflectance R0

• 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

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. …

Problem 1: Clouds and Haze are Highly variable in Space and Time

• Dominate reflectance wherever they occur; the cloud frequency very regional

• New England is cloudy much of the time

• Illinois is less cloudy

San Joaquin Valley

New Hampshire

Illinois Farmland

Surface Reflectance (0.67 um) x 1000

Surface Reflectance

Surface Reflectance

Jun JulMay OctApr OctSepAug

• S. California nearly cloud-free but it is hazy

• Advantage: The temporal variability of clouds/haze means that occasionally the surface reflectance is un-obscured and can be extracted from the noisy data.

Cloud Shadows

• Cloud shadows result in dark pixels, well below the normal surface reflectance

• Shadows are eliminated by enlarging the cloud mask and by the ‘jump’ filter

Problem 2: Vegetated Surface Reflectance Can Change Rapidly

• Vegetated surfaces change reflectance color and intensity with season

• The shape of the seasonal reflectance pattern depends on the surface

• Advantage: The seasonal reflectance pattern can be used to identify surface types.

April 29 July 18 October 16

0.55 m0.41 m

0.67 m

Problem 3: Surface and Haze Reflectance Depends on Geometry

sdhsdhg

Surface Retrieval Approach: Reflectance Time Series Analysis

• 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

• Next, a two-pass-two-directional ‘jump’ filter eliminates days with substantial haze or cloud shadows

• The remaining clear anchor days are interpolated to yield daily surface reflectance estimates

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

Spatial Variation: 9 pixel rectangle

• Adjacent pixels show similar pattern in some areas, more variable in others

US Surface Reflectance Map, April 1, 2000

• The resulting data are 8-channel cloud/surface-free surface reflectance

• The test dataset consists of daily values (Apr-Nov 2000) at ~ 1 km resolution for the conterminous US.

Seasonal Surface Reflectance, Eastern US

April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290

Seasonal Surface Reflectance, Western US

April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290

Surface Color Seasonality of Urban Pixels

Aerosol Retrieval

• See PPT – will be merged