FR3TO5.1.pdf

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FR3TO5.1.pdf

  1. 1. Prototyping GOES-R Albedo Algorithm Based on MODIS Data Tao Hea, Shunlin Lianga, Dongdong Wangaa. Department of Geography, University of Maryland, College Park, USA Hongyi Wub b. University of Electronic Science and Technology of China, China Yunyue Yuc c. NOAA/NESDIS/STAR, USA Presented by Tao He the@umd.edu Jul 29, 2011
  2. 2. Contents 1 Introduction 2 Methodology 3 Data and Results 4 Summary and Conclusions
  3. 3. Introduction§  Surface albedo is defined as the ratio of outgoing and incoming radiation at Earth surface. §  Essential in energy budget §  Climate change studies §  Hydrology cycle §  Weather forcast §  …§  Current satellite albedo products §  MODIS, MISR, MERIS, MSG/SEVIRI, …
  4. 4. GOES-R ABI§  The Advanced Baseline Imager (ABI) is the primary instrument onboard GOES-R for weather, climate and environmental studies. §  Temporal resolution: 15min §  Spatial resolution: 0.5 – 2km §  Spectral bands: 6 bands in solar range (0.4 – 2.3 µm)§  Abundant spectral and angular information can be available within a small time period to derive surface spectral BRDF and broadband albedo.
  5. 5. ABI vs MODISGOES-R GOES-R ABI MODIS Channel Central Wavelength Spatial Channel Central Wavelength Spatial Number (µm) Resolution Number (µm) Resolution 1 0.47 1 km 3 0.47 0.5 km 2 0.64 0.5 km 1 0.65 0.25 km 3 0.86 1 km 2 0.86 0.25 km 4 1.38 2 km N/A 5 1.61 1 km 6 1.64 0.5 km 6 2.26 2 km 7 2.13 0.5 km N/A 4 0.56 0.5 km N/A 5 1..24 0.5 km
  6. 6. Existing Methods 1.  Atmospheric correction 2.  Surface BRDF modelingA 3.  Narrow-2-broadband conversion (e.g. Schaaf et al. 2002, Geiger et al. 2008) 1.  Direct estimation of broadband albedos (e.g. Liang et al. 2005)B 1.  Atmospheric correction with surface BRDF modelingC 2.  Narrow-2-broadband conversion (e.g. Govaerts et al. 2010)
  7. 7. Objectives§  Using MODIS TOA data as proxy to prototype the future GOES-R albedo algorithm based on atmospheric correction with BRDF modeling;§  Estimating instantaneous albedo/reflectance as well as instantaneous aerosol optical depth;§  Improving the albedo estimation over rapidly- changing surfaces;§  Validating/verifying albedo/reflectance estimates with multiple datasets.
  8. 8. Methodology Cost Function:§  x: coefficients of the surface BRDF model and AOD,§  r(x): calculated surface albedo using the BRDF model§  rb: background values of albedo from albedo climatology§  B: uncertainty matrix of the albedo background values§  ρ: satellite observed TOA reflectance§  ρ(x): calculated TOA reflectance from the radiative transfer equation§  R: error matrix of the calculated TOA reflectance§  Jc: cost function to account for various constraints (physical meanings of BRDF parameters, and AODs, etc.).
  9. 9. Atmospheric Radiative Transfer Solution with Land Surface BRDF Modeling §  Atmospheric Radiative Transfer Formulation for better modeling the interaction between atmosphere and non-Lambertian surfacesTOA Path Transmittance Surface Reflectance MatrixReflectance Reflectance Matrix (Qin, et al. 2001) Spherical Albedo Transmittance Matrix Surface Reflectance Matrix i, v refer to the incoming and outgoing light directions respectively; all atmospheric variables in the above model were simulated for each major aerosol type using 6S software and stored in LUT for computational purpose.
  10. 10. Atmospheric Radiative Transfer Solution with Land Surface BRDF Modeling (cont.)§  Surface BRDF Modeling §  Kernel models used with consideration of hot spot effects (Maignan, et al. 2004) Where
  11. 11. Flowchart TOA Reflectances RadiativePrior AOD Prior BRDF Transfer Model Optimal BRDF Albedo Spectral Optimization ParametersClimatology Reflectance and AODNarrow-2- Spectral AngularBroadband Albedo IntegrationConversion Broadband Albedo
  12. 12. Data§  Satellite data & products §  MODIS L1B data (TOA radiance, geometry) §  MODIS cloud mask§  Ancillary data §  Albedo climatology maps from multi-year MODIS albedo products (2000 – 2009) §  NCEP water vapor
  13. 13. Albedo Climatology and Uncertainty§  Ten-year average shortwave albedo (a) and its one-year standard deviation (b) for Julian Day 121–128 from MODIS albedo product 2000–2009 over North America and Greenland.
  14. 14. Validation Datasets§  Ground measurements §  AmeriFlux §  Surface Radiation (SURFRAD) Network §  Greenland Climate Network (GC-Net)§  Satellite data calibrated with in-situ aerosol data §  MODASRVN (Wang et al. 2009)§  Finer resolution satellite data §  Landsat data from LEDAPS (Vermote et al. 2007)
  15. 15. Validation Results: Vegetated Surface Bondville Lat:40.05 Lon:-88.37 1 Ground measured albedo Shortwave Albedo 0.8 Estimates MODIS 16-day albedo 0.6 0.4 0.2 0 50 100 150 200 250 300 350 Julian DayExample of time series shortwave albedo from MODIS observations in 2005 over six SURFRAD sites Mead(Rain fed) Lat:41.1797 Lon:-96.4396 1 Ground measured albedo 0.8 Estimates Visible Albedo MODIS 16-day albedo 0.6 0.4 0.2 0 50 100 150 200 250 300 350 Julian DayExample of time series total visible albedo from MODIS 15 observations in 2005 over four AmeriFlux sites
  16. 16. Validation Results: Snow Surface Saddle 1Shortwave Albedo 0.8 •  Greenland 0.6 sites (GC-Net) 0.4 Ground measured albedo 0.2 Estimates MODIS 16-day albedo •  2003 50 100 150 200 250 300 350 Julian Day •  Comparison with MODIS albedo NASA-SE 1Shortwave Albedo 0.8 products and 0.6 Ground measured albedo ground measurements 0.4 Estimates MODIS 16-day albedo 0.2 50 100 150 200 250 300 350 Julian Day
  17. 17. . Validation Results: Surface Reflectance BrattsLake (50.28,-104.7) Cropland Egbert (44.226,-79.75) Cropland 1 1 Estimated Red Band IBRF 0.9 Estimated Red Band IBRF 0.9 MODASRVN Red Band IBRF MODASRVN Red Band IBRF 0.8 Estimated NIR Band IBRF 0.8 Estimated NIR Band IBRF Example of time series MODASRVN NIR Band IBRF MODASRVN NIR Band IBRF 0.7 0.7 ReflectanceReflectance 0.6 0.5 0.6 0.5 instantaneous reflectance 0.4 0.4 from MODIS observations in 2005 over AERONET 0.3 0.3 0.2 0.2 0.1 0.1 sites 0 0 0 50 100 150 200 250 300 0 50 100 150 200 250 300 350 Julian Day Julian Day All Sites Band1 All Sites Band2 Comparison of estimated 1 1 0.9 y=1.0177x+0.0065 0.9 y=0.91535x+0.024 and MODASRVN R-squared=0.698 R-squared=0.732 0.8 Bias=0.0084 0.8 Bias=0.0025 RMSE=0.0269 RMSE=0.0471 0.7 Estimated IBRF 0.7 Estimated IBRF instantaneous 0.6 0.6 bidirectional reflectance 0.5 0.5 0.4 0.4 for MODIS band1&2 over 0.3 0.2 0.3 0.2 16 AERONET sites during 0.1 0.1 2005 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 MODASRVN IBRF MODASRVN IBRF
  18. 18. Comparison with Landsat Data Comparison of aggregated Landsat shortwave albedo with retrieved 1km albedo from MODIS observations over SURFRAD sites (a)3 by 3 pixels; (b)7 by 7 pixels; (c)11 by 11 pixels; (d)21 by 21 pixels; (e)31 by 31 pixels.
  19. 19. Summary of Validation Results Albedo Our Retrievals F&PS RequirementAccuracy (Bias) 0.0137 0.08Precision (RMSE) 0.0618 10% R2 0.8208 N/A Reflectance Our Retrievals F&PS RequirementAccuracy (Bias) 0.0084 (Red) 0.08 0.0025 (NIR)Precision (RMSE) 0.0269 (Red) 5% 0.0471 (NIR) R2 0.698 (Red) N/A 0.732 (NIR)
  20. 20. Conclusions§  Framework of retrieving surface albedo/ BRDF was established using MODIS TOA observations as proxy;§  Extensive validation/verification was made over various land cover types with ground measurements from multiple network and good preliminary results were shown;§  Good agreement was found in comparison of surface reflectance estimates with MODASRVN data.
  21. 21. Future Work§  More validations on albedo and reflectance;§  Validation of aerosol optical depth estimation;§  Sensitivity analysis;§  Improvement of diurnal albedo estimation based on geostationary satellite data (e.g. MSG/SEVIRI).
  22. 22. Questions?

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