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 97 th Annual Conference and Exhibition June 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

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 Chlorophyll Absorption Designed for Vegetation Detection Swath 2300 KM 24/day Polar Orbit: ~ 1000 km, 100 min. Equator Crossing: Local Noon

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

Aerosol effects on surface color and Surface effects on aerosol color 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.

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

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 Surface Reflectance Retrieval by Time Series Analysis (Sean Raffuse, MS Thesis 2003) Aerosol Retrieval over Land Radiative transfer model + Surface data Refined Surface Reflectance Iteration back to 1., 2. …

Surface Reflectance Retrieval by Time Series Analysis

(Sean Raffuse, MS Thesis 2003)

Aerosol Retrieval over Land

Radiative transfer model + Surface data

Refined Surface Reflectance

Iteration back to 1., 2. …

Scattering angle correction 2 Pixels are normalized to a scattering angle of 150 °

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

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

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 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

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)

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

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

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

Results – Eight month animation

Apparent Surface Reflectance, R The surface reflectance R 0 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: R a = ( e -  – 1 ) P R = ( R 0 + ( e -  – 1 ) P ) e -  Aerosol as Filter: T a = e -  Surface reflectance R 0 The apparent reflectance , R , detected by the sensor is: R = ( R 0 + R a ) T a 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

The surface reflectance R 0 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

The apparent reflectance , R , detected by the sensor is: R = ( R 0 + R a ) T a

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 Generate daily total reflectance image with air reflectance removed, R Generate surface reflectance image, R 0 Subtract daily total reflectance image from surface reflectance image to get aerosol optical thickness,  Filter  , removing clouds and other interferences

Generate daily total reflectance image with air reflectance removed, R

Generate surface reflectance image, R 0

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

Filter  , removing clouds and other interferences

July 10, 2002 July 9, 2002 July 8, 2002 July 7, 2002 July 6, 22002 July 5, 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

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