1. Co-Retrieval of Surface Color and Aerosols from SeaWiFS Satellite Data Outline of a Seminar Presentation at EPA May 2003 Sean Raffuse and Rudolf Husar CAPITA, Washington University, St. Louis, MO

2. 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 Noon Chlorophyll Absorption Designed for Vegetation Detection

3. 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

4. Apparent Surface Reflectance, R R = (R 0 + (e -  – 1) P) e -  The surface reflectance R 0 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 = (R 0 + R a ) T a Aerosol as Reflector: R a = (e -  – 1) P Aerosol as Filter: T a = e -  Surface reflectance R 0 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

5. 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. …

6. 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 JunJulMayOctAprOctSepAug 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.

7. 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

8. 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 29July 18October 16 0.55  m 0.41  m 0.67  m

9. Problem 3: Surface and Haze Reflectance Depends on Geometry sdhsdhg

10. 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

11. 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

12. Spatial Variation: 9 pixel rectangle Adjacent pixels show similar pattern in some areas, more variable in others

13. 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.

14. Seasonal Surface Reflectance, Eastern US April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290

15. Seasonal Surface Reflectance, Western US April 29, 2000, Day 120July 18, 2000, Day 200October 16, 2000, Day 290

16. Surface Color Seasonality of Urban Pixels

17. Aerosol Retrieval See PPT – will be merged