Menghua Wang NOAA/NESDIS/STAR Camp Springs, MD 20746, USA

Transcription

1 Ocean EDR Product Calibration and Validation Plan Progress Report: VIIRS Ocean Color Algorithm Evaluations and Data Processing and Analyses Define a VIIRS Proxy Data Stream Define the required in situ data stream for Cal/Val Tuning of algorithms and LUTS (Vicarious calibration and SDR feedback) Menghua Wang NOAA/NESDIS/STAR Camp Springs, MD 20746, USA Support: Wei Shi, SeungHyun Son The Ocean Cal Val EDR Meeting, March 23-25, IPO, Centre Building, 8455 Colesville Road, Silver Spring, Maryland Ocean Algorithm, stability evaluation and uncertainty Product validation and product longterm stability Satellite intercomparisons, robustness, seasonal and product stability 1

2 Ocean EDR Product Calibration and Validation Plan VIIRS Ocean Color Algorithm Evaluations and Data Processing and Analyses Menghua Wang FY 09 - Plans and deliverables 1) Algorithm Evaluation and Development 2) Vicarious Calibration (VC) Technique Demonstration using the MODIS Data 3) VIIRS Data Processing System and VIIRS Proxy Data Set Define a VIIRS Proxy Data Stream Define the required in situ data stream for Cal/Val Tuning of algorithms and LUTS (Vicarious calibration and SDR feedback) Ocean Algorithm, stability evaluation and uncertainty Product validation and product longterm stability Satellite intercomparisons, robustness, seasonal and product stability 2

3 Ocean EDR Product Calibration and Validation Plan VIIRS Ocean Color Algorithm Evaluations and Data Processing and Analyses Menghua Wang Plans and deliverables (FY09) 1) Algorithm Evaluation and Development Determine effects of VIIRS sensor spectral response characterization on the lookup tables (Status: report/presentation, p work continue) Delivery of VIIRS Rayleigh lookup table generation for ocean color products to IPO (Rayleigh LUTs) (April 10) Test NIR atmospheric correction algorithm evaluation using the SWIR and NIR-SWIR approaches, compare with VIIRS atmospheric correction methods (Status: report/presentation, work continue) Define a VIIRS Proxy Data Stream Define the required in situ data stream for Cal/Val Tuning of algorithms and LUTS (Vicarious calibration and SDR feedback) Ocean Algorithm, stability evaluation and uncertainty Product validation and product longterm stability Satellite intercomparisons, robustness, seasonal and product stability 3

4 Ocean EDR Product Calibration and Validation Plan Plans and deliverables (FY09) (Cont.) 2) Vicarious Calibration Technique Demonstration using the MODIS Data Implement software for ocean color VC algorithm Update and improve the VC algorithms Determine how the methods will be automated and document (Status: significant progress, work continue) 3) VIIRS Data Processing System and VIIRS Proxy Data Set (Status: not started yet) Coordinate with GRAVITE and SME s to obtain and evaluate VIIRS proxy software with MODIS Test and refine software using VIIRS data formats, establish proxy data flow Define a VIIRS Proxy Data Stream Define the required in situ data stream for Cal/Val Tuning of algorithms and LUTS (Vicarious calibration and SDR feedback) Ocean Algorithm, stability evaluation and uncertainty Product validation and product longterm stability Satellite intercomparisons, robustness, seasonal and product stability 4

5 Algorithm Evaluation and Development 5

6 VIIRS Rayleigh Lookup Table Delivery VIIRS sensor response function data were received in the mid of January ar this year. The Rayleigh lookup table for VIIRS ocean bands is planned to be delivered in the mid of April. 6

7 10 0 VIIRS Spectral Response Function (1) 10 0 Spectral Res sponse Function n (a) VIIRS Band M1 (Effective) 410 nm Spectral Res sponse Function n (b) VIIRS Band M2 (Effective) 443 nm Wavelength (nm) 10 0 Wavelength (nm) Spectral Respon nse Function VIIRS Band M3 (Effective) 486 nm (c) Wavelength (nm) pectral Respon nse Function S 551 nm 10-1 VIIRS Band M4 (Effective) (d) Wavelength (nm)

8 10 0 VIIRS Spectral Response Function (2) 10 0 ponse Function n nm VIIRS Band M5 (Effective) ponse Function n VIIRS Band M6 (Effective) e) 745 nm Spectral Res (e) Spectral Res (f) () Wavelength (nm) Wavelength (nm) S pectral Respon nse Function VIIRS Band M7 (Effective) (g) 862 nm S pectral Respon nse Function 10-1 VIIRS Band M nm (h) Wavelength (nm) Wavelength (nm) 8

9 Band # VIIRS Spectral Band Characterization Band Range (FWHM) (nm) VIIRS Spectral Bands Width (FWHM) (nm) Peak Wavelength (nm) Nominal Center (nm) Ğ (20) Ğ (15) Ğ (19) Ğ (20) Ğ (19) Ğ (14) Ğ (38) Ğ (26) Ğ (14) Ğ (60) Ğ (46)

10 Quantitative Assessments of the VIIRS Sensor Out-of-Band Effects Sensor-Measured Solar Irradiance can be computed: Full-Band Solar Irradiance = F ( 0 λ)s ( i λ )dλ Full S i Out-of-Band Solar Irradiance = F ( 0 λ)s ( i λ )dλ () λ Out-of-Band S i In-Band Solar Irradiance = F ( 0 λ)s ( i λ )dλ VIIRS Sensor Out-of-Band Effects: Out-of-Band Solar Irradiance / Full Band Solar Irradiance In-Band S i () λ () λ VIIRS results are compared with those from SeaWiFS and MODIS-Aqua. 10

11 5 VIIRS Ocean Color Spectral Out-of-Band Effects Compared with SeaWiFS and MODIS Sensor-Measured Solar Irradiance Out-of-B Band Eff fect (%) VIIRS 4 SeaWiFS MODIS-Aqua Wavelength (nm) 11

12 % Diffe erence in <F0> VIIRS Ocean Color Spectral Band Detector Variations: Solar Irradiance Compared with the Detector Average Value VIIRS Effective RSRs M1 (410 nm) M2 (443 nm) M3 (486 nm) M4 (551 nm) M5 (671 nm) M6 (745 nm) M7 (862 nm) (a) VIIRS Detector # 12

13 <taur> VIIRS Ocean Color Spectral Band Detector Variations: Rayleigh Optical Thickness (Scattering) Compared with the Detector Average Value VIIRS Effective RSRs (b) rence in 0 % Differ -0.5 M1 (410 nm) M2 (443 nm) M3 (486 nm) M4 (551 nm) M5 (671 nm) M6 (745 nm) M7 (862 nm) VIIRS Detector # 13

14 The Ocean Color and Other Useful Spectral Bands for VIIRS, MODIS, and SeaWiFS VIIRS MODIS SeaWiFS Ocean Bands Other Bands Ocean Bands Other Bands Ocean Band (nm) (nm) (nm) (nm) (nm) Ñ SWIR Bands 551 SWIR Bands VIIRS has similar SWIR bands as MODIS 14

15 Atmospheric Correction: SWIR Bands At the shortwave IR (SWIR) wavelengths (>~1000 nm), ocean water has much strongly absorption and ocean contributions are significantly less. Thus, atmospheric correction can be carried out tfor coastal regions without t using the bio-optical model. Water absorption for 869 nm, 1240 nm, 1640 nm, and 2130 nm are 5 m -1, 88 m -1, 498 m -1, and 2200 m -1, respectively. Examples using the MODIS Aqua 1240 and 2130 nm dt data to derive the ocean color products are provided. We use the SWIR band (1240 nm) )for the cloud masking. This is necessary for coastal region waters. Wang, M. and W. Shi, Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies, Geophys. Res. Lett., 32, L13606, doi: /2005gl022917, Wang, M., Remote sensing of ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations, Appl. Opt., 46, , Wang, M. and W. Shi, Cloud masking for ocean color data processing in the coastal regions, IEEE Trans. Geosci. Remote Sensing, 44, ,

16 Comparisons of MODIS Ocean Color Products from NIR, SWIR, and NIR-SWIR Combined Methods Example: U.S. East Coast Wang, M. and W. Shi (2007), The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing, Optics Express, p 15,

17 MODIS-Aqua Chlorophyll-a Images QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. 17

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20 Validation Effort in the Chesapeake Bay nlw(443) nlw(488) nlw(531) QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. 20

21 Validation Effort in the Chesapeake Bay nlw(551) nlw(667) QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. 21

22 New Kd(490) for U.S. East Coastal Region Wang, M., S. Son, and L. W. Harding Jr. (2009), Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications, J. Geophys. Res., 114, C10011, doi: /2009JC

23 On-Orbit Vicarious Calibration 23

24 Vicarious Calibration (VC) For ocean color remote sensing, post-launch vicarious calibration is necessary for visible bands. VC: Calibration of whole system: Sensor + Algorithms Account for (by direct measurement or prediction) all of the components of the TOA radiance and Compare the results with the sensor-measured radiance. Sensor-measured reflectance: Meas ρ ( ) λ t ( )=) 1+ a ( λ ) Computed reflectance: ( ) λ ρ Computed t [ ]ρ t ( λ ), a ( λ) ) calibration error ( ) = ρ r ( λ ) + ρ λ ) ρ a( λ)+ ρ ra ( λ) tρ wc( λ) 123 tρ ( w λ 12 3 ) Computed Predicted Using Models Computed Measured H. R. Gordon, In-orbit calibration strategy for ocean color sensors, Remote Sens. Environ., 63, , Calibration Site, e.g., MOBY

25 Vicarious Calibration (cont.) Corrected reflectance after vicarious calibration (VC): ( Corrected ρ ) t ( λ)= G ( VC ) Meas ( λ)ρ t ( )= G ( VC ) ( λ ) 1+ a ( λ ) ( ) [ ] Computed ] 1 = ρ t ( λ) = [ 1+ a ( λ) ]ρ t λ ( ) where for a given calibration site (solar and viewing geometry) a λ [ ( ) ( λ ) ] ( VC ) ρ t ( λ) ( ) 1 which depends only on the correctness of aerosol model and on a(865), but not on a(λ) for λ < 865 nm (or SWIR 2130 nm). Results of VC are independent of the sensor pre-launch calibration for λ < 865 nm (or SWIR 2130 nm)!! Wang, M. and H. R. Gordon, Calibration of ocean color scanners: How much error is acceptable in the near-infrared, Remote Sens. Environ., 82, ,

26 On-Orbit Vicarious Calibration for Ocean Color Satellite Sensors It has been demonstrated that VC is necessary for producing accurate satellite ocean color products (e.g., MERIS experience). Post-launch vicarious calibration has been carried out for SeaWiFS and MODIS (Eplee et al., 2001), and will also be carried out for the MERIS. The VC has been carried out for the works to account for the aerosol polarization effects (SeaWiFS) (Wang, 2006), and all SWIR related results (MODIS-Aqua) (e.g., Wang et al., 2007, 2009). We have implemented the VC method dfor routinely deriving i the VC gains for the MODIS-Aqua data products. 26

27 Normalized Water-leaving Radiance Spectra Sources: From MODIS-Aqua Standard Products from Hawaii Calibration Site Methodology described in: M. Wang, Aerosol polarization effects on atmospheric correction and aerosol retrievals in ocean color remote sensing, Appl. Opt., 45, ,

28 SWIR vs. NIR-Based Calibration Gains from (21x21 box) STD nlw(λ) SWIR-based NIR-based

29 SWIR vs. NIR-Based Calibration Gains from (21x21 box) STD nlw(λ) SWIR-based NIR-based

30 SWIR vs. NIR-Based Calibration Gains from (21x21 box) STD nlw(λ) SWIR-based NIR-based

31 SWIR vs. NIR-Based Calibration Gains from (21x21 box) STD nlw(λ) SWIR-based NIR-based

32 Normalized Water-leaving Radiance Spectra Sources: MOBY In Situ Data in Hawaii Calibration Site 32

33 SWIR vs. NIR-Based Calibration Gains from MOBY In Situ nlw(λ) SWIR-based NIR-based

34 SWIR vs. NIR-Based Calibration Gains from MOBY In Situ nlw(λ) SWIR-based NIR-based

35 SWIR vs. NIR-Based Calibration Gains from MOBY In Situ nlw(λ) SWIR-based NIR-based

36 SWIR vs. NIR-Based Calibration Gains from MOBY In Situ nlw(λ) SWIR-based NIR-based

37 Vicarious Gains Derived from Four Different Ways (Preliminary) SWIR and NIR VC Gains Wavelength nl w ( ) Source from MODIS-Aqua nl w ( ) Source from MOBY In Situ (nm) SWIR- NIR-derived SWIR- NIR-derived derived Gains Gains derived Gains Gains Ğ Ğ Ğ Ğ Ğ Ğ 37

38 Calibration Gains vs. AOT (tau_869) (Sensitivity Study-1)

39 Calibration Gains vs. Solar-Zenith Angle (Sensitivity Study-2)

40 Calibration Gains vs. Sensor-Zenith Angle (Sensitivity Study-3)

41 Calibration Gains vs. Wind Speed (Sensitivity Study-4)

42 nlw(443) scale: (mw/cm 2 μm sr) July, 2005 Standard Data Processing Wang, M., S. Son, and W. Shi (2009), Evaluation of MODIS SWIR and NIR- SWIR atmospheric correction algorithms using SeaBASS data, Remote Sens. Environ., 113, July, 2005 NIR-SWIR Data Processing 42

43 Chlorophyll-a (mg/m 3 ) (Log scale) July, 2005 Standard Data Processing Wang, M., S. Son, and W. Shi (2009), Evaluation of MODIS SWIR and NIR- SWIR atmospheric correction algorithms using SeaBASS data, Remote Sens. Environ., 113, July, 2005 NIR-SWIR Data Processing 43

44 On-Orbit Vicarious Calibration System Built a vicarious calibration system to automat produce the vicarious gains with inputs of True nlw(λ) data. We can/will SUPPORT VIIRS ocean color on-orbit calibration activities! Some more studies (e.g., some further sensitivity studies) will need to be carried out. Vicarious calibration facility, e.g., MOBY, to provide accurate nlw(λ) data is required and necessary!! 44

45 Thank You! 45