Coastal Characterization Using EO-1 Hyperion Data

Transcription

1 Coastal Characterization Using EO-1 Hyperion Data Dr. Hsiao-hua K. Burke EO-1 SVT Meeting November 2002 Sponsor: NOAA NESDIS GOES

2 Channel Positions of Various Ocean- Color Sensors, * For a multi spectral sensor Many spectral bands are identified for various applications Selection of band location and width are also important HSI provides contiguous spectral coverage and thus comprehensive information content GOES * IOCCG Report #1

3 Why HSI for Coastal Characterization? Most ocean feature algorithms are semi-empirical retrievals, HSI has all bands to: Provide legacy with previous sensors Explore new information Coastal features are more complex than those of deep (open) ocean Coupled effects best resolved with HSI With contiguous spectral coverage, atmospheric compensation can be done with more accuracy and confidence Most ocean characterization algorithms utilize water-leaving radiance Aerosol effect most pronounced in shortwave visible where ocean color measurements are made GOES

4 Illustration of Enhanced Information Content from MSI to HSI Two examples shown 1. Two similar broad bands ( and um) Information contents are different HSI allows adaptive band selection: no more competing requirements µm µm 2. MSI vs. HSI (EO-1 ALI and Hyperion) More feature information available despite lower SNR of Hyperion GOES ALI Hyperion

5 Principal Components Analysis (PCA) - Reduction of HSI Dimensionality - Principal Components Transform A linear transform that projects data onto an orthogonal set of basis functions that are eigenvectors of the spectral covariance matrix: Covariance Matrix: Σ = E{(X - M)(X - M) Τ } Eigenvectors and Eigenvalues: Σ e = Λ e Apply to Image Cube: PC (x,y) = e Τ X (x,y) Standard multivariate analysis technique Dimensionality reduction Contrast enhancement Anomaly detection Separates significant scene information from noise dominated components large eigenvalues -> components that greatly contribute to overall variance small eigenvalues -> noise dominated components GOES

6 Principal Components Analysis (PCA) - Reduction of HSI Dimensionality - Cont. Eigenstructure Computed from example imagery Eigenvalues Eigenvectors PC: Indicative of the amount of spectral variability represented by corresponding eigenvector large values! scene components that greatly contribute to overall variance small values! noise dominated components intermediate values "? anomalous features GOES Groups spectral channels that have similar contributions to overall spectral variability Ordered by decreasing eigenvalue Can help to identify important spectral regions Full Full spectral information can can be be recovered

7 An Illustration: Similar Bands, Different Information Content Broad Images µm µm GOES Images simulated by integrating (18 & 28) AVIRIS HSI bands Animation to follow

8 An Illustration: Similar Bands, Different Information Content Broad PC Images µm µm GOES

9 Cascaded Principal Component Analysis Minimum Noise Fraction (MNF) Transform Principal Components Transformation (PCT) Covariance Matrix: Σ = Ε {(Χ Χ m )(Χ Χ m ) Τ } Eigenvectors and Eigenvalues: Σ = Φ Λ Φ Τ MNF: Two cascaded PCTs The first based on estimated noise covariance Band-to-band de-correlated noise in transformed data The second PCT on noise-whitened data Unit noise variance Result: Image information aggregated in leading components Noise segregated in trailing components Inherent dimensionality of HSI data determined GOES

10 EO-1 Data from Chesapeake Bay Selected area (~6 x 15 km 2 ), 0.43 to 0.93 µm only ALI 200 samples/line 512 lines 6 bands (MS-1,1,2,3,4,4 ) Band Pan MS-1 MS-1 MS-2 MS-3 MS-4 MS-4 MS-5 MS-5 MS-7 Wavelength GSD (m) (nm) GOES Hyperion 194 samples/line 496 lines 50 bands ( µm)

11 MNF Bands 1 & 2 ALI MNF Band 1 Hyperion MNF Band 1 ALI MNF Band 2 Hyperion MNF Band 2 GOES Animation to follow

12 MNF Components for ALI and Hyperion ALI MNF GOES Hyperion MNF More feature information available despite lower SNR of Hyperion

13 Additional Advantages: Full Spectral and Color Exploitation Abundant spectral information in HSI offers potential for Comprehensive feature extraction Anomaly detection Often new phenomena occur, full color coverage allows for new investigation and data exploitation Red Tide observed recently along NSW coast of Australia Suspected algae outbreak The effect turns to eerie green at night GOES

14 Location of EO-1 Data from Chesapeake Bay GOES

15 Hyperion and ALI Radiance Point Spectra Radiance W/m 2 -sr-µm) GOES Wavelength (µm)

16 Hyperion and ALI TOA Reflectance Point Spectra Reflectance GOES Wavelength (µm)

17 Atmospheric Compensation: ATREM Highlights Physical radiative transfer model (Gao, et al., 1993) Originally designed for AVIRIS Mature algorithm, used extensively by remote sensing community Simple interface and fast execution Required input: Model atmosphere Haze/aerosol conditions Solar and viewing geometry Sensor platform and characteristics Conducts column water vapor retrieval based on ratioing the absorption and wing regions of two weak water vapor absorption bands (0.94 and 1.14 µm) Constructs pixel-by-pixel reflectance spectra based on derived total spectral transmittance GOES

18 Schematic Flow of ATREM (Atmospheric REMoval Program) Input: Sensor info SZA, Aerosol 6S RT Code w/ Std Atm Constituents LUT w/ H 2 O (0-10 cm) ρ a, S, T(θ s ), T(θ u ) Image Radiance Cube TOA Solar Irr Apparent Reflectance 3-Ch H 2 O 0.94 & 1.14µm Average Col. H 2 O Pixel-by-Pixel Tg TOA: TOA: Top Top of of Atmosphere Atmosphere T(θ T(θ s ): s ): Downward Downward scattering scattering transmittance transmittance LUT: LUT: Look-up Look-up Table Table T(θ T(θ u ): u ): Upward Upward scattering scattering transmittance transmittance ρ a : a : Atm Atm Reflectance Reflectance Tg: Tg: Total Total transmittance transmittance S: S: Atm Atm Spherical Spherical Albedo Albedo 6S: 6S: Second Second Simulation Simulation of of the the Satellite Satellite Signal Signal in in the the Solar Solar Spectrum Spectrum Surface Reflectance Cube GOES

19 Sea Surface Reflectance (ATREM) 478nm 600nm 763nm 478nm 600nm 763nm GOES

20 Chlorophyll-a Calculations (HYPERION) 478nm 447nm 478nm 600nm 488nm 600nm Line Line nm 763nm 550nm Line Line nm7 Line nm Line 400 Line 475 Line 400 Line Range: 2.1 to 3.6 mg/m3 Calculated using SeaWIFS OC4 GOES

21 Supporting Data Chesapeake Bay Remote Sensing Program CBRSP 2/19/02 SeaWIFS Data averaged for the week 2/18/02-2/25/02 Chlorophyll-a immediately inside the Bay is between 2 to 4 mg/m3. EO-1 data area is shown in red box GOES to 4.0 mg/m3 (Level 3 calculations use OC4 algorithm) Initial chlorophyll retrieval from Hyperion data demonstrated: good agreement with published data

22 Recommendations for Future EO-1 Data Collection Select coastal regions of interest Conduct multiple collections to observe temporal changes Assemble (or collect) ground truth (ancillary/auxiliary) data for validation Comprehensive utility assessment of HSI to coastal characterization GOES