Under the direction of Rudolf Husar Estimation of Daily Surface Reflectance Over the United States from the SeaWiFS Sensor April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290 Sean Raffuse Under 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 Science May 23, 2003

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 R = (R0 + (e-t – 1) P) e-t Aerosol as Filter: Ta = e-t Aerosol as Reflector: Ra = (e-t – 1) P 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 Chlorophyll Absorption Soil Vegetation Water

Scattering angle correction 1 Surface reflectance is dependent on sun-target-sensor angle (non-Lambertian) Time series shows dependence

Scattering angle correction 2 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 R = (R0 + (e-t – 1) P) e-t

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 L1A Key Create Geometry Rayleigh Correction Scattering Angle Corr. A  B Conversion Georeference L1B Georef. geometry Filter bad pixels L1A Geometry File Filtered L1B Warp Points Georeferenced Daily Radiance Image Rayleigh Corrected Reflectance Splice, merge, crop Daily Reflectance Mask clouds Calibration File Cloud Mask Cloud/Cloud Shadow Mask Enlarge cloud mask Select anchor points Initial Anchor Points Clean Surface Reflectance Refine anchor points Haze Reduction Refined Anchor Points Surface Images Interpolate Key Inputs multiple swaths from a single day Outputs single file Inputs all daily files from the time span Inputs single file Outputs daily file for each day in the time span Elevation Data

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 – Seasonal surface reflectance urban pixels

Results – Eight month animation

Results – Spatial variation: 9 pixel rectangle Adjacent pixels show similar values in some areas, more variability in others

Results – Comparison with clean air mass Surface reflectance estimates should be similar to apparent reflectance values for days with clean air Daily reflectance Ch1 difference Surface reflectance 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 mm) 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!