Prototyping GOES-R Albedo Algorithm Based on MODIS Data Tao He a, Shunlin Liang a, Dongdong Wang a

Transcription

1 Prototyping GOES-R Albedo Algorithm Based on MODIS Data Tao He a, Shunlin Liang a, Dongdong Wang a a. Department of Geography, University of Maryland, College Park, USA Hongyi Wu b b. University of Electronic Science and Technology of China, China Yunyue Yu c c. NOAA/NESDIS/STAR, USA Presented by Tao He the@umd.edu Jul 29, 2011

2 Contents Introduction Methodology Data and Results 4 Summary and Conclusions

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 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 ( µm) Abundant spectral and angular information can be available within a small time period to derive surface spectral BRDF and broadband albedo.

5 ABI vs MODIS GOES-R GOES-R ABI MODIS Channel Number Central Wavelength (µm) Spatial Resolution Channel Number Central Wavelength (µm) Spatial Resolution km km km km km km km N/A km km km km N/A km N/A km

6 Existing Methods A 1. Atmospheric correction 2. Surface BRDF modeling 3. Narrow-2-broadband conversion (e.g. Schaaf et al. 2002, Geiger et al. 2008) B 1. Direct estimation of broadband albedos (e.g. Liang et al. 2005) C 1. Atmospheric correction with surface BRDF modeling 2. Narrow-2-broadband conversion (e.g. Govaerts et al. 2010)

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 rapidlychanging surfaces; Validating/verifying albedo/reflectance estimates with multiple datasets.

8 Methodology Cost Function: x: coefficients of the surface BRDF model and AOD, r(x): calculated surface albedo using the BRDF model r b : 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 J c : cost function to account for various constraints (physical meanings of BRDF parameters, and AODs, etc.).

9 Atmospheric Radiative Transfer Solution with Land Surface BRDF Modeling Atmospheric Radiative Transfer Formulation for better modeling the interaction between atmosphere and non-lambertian surfaces TOA Reflectance Path Reflectance Transmittance Matrix Surface 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 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 Flowchart TOA Reflectances Prior AOD Radiative Transfer Model Prior BRDF Albedo Climatology Optimization Optimal BRDF Parameters and AOD Spectral Reflectance Narrow-2- Broadband Conversion Spectral Albedo Angular Integration Broadband Albedo

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 ( ) NCEP water vapor

13 Albedo Climatology and Uncertainty Ten-year average shortwave albedo (a) and its one-year standard deviation (b) for Julian Day from MODIS albedo product over North America and Greenland.

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 Validation Results: Vegetated Surface Shortwave Albedo Bondville Lat:40.05 Lon: Ground measured albedo Estimates MODIS 16-day albedo Julian Day Example of time series shortwave albedo from MODIS observations in 2005 over six SURFRAD sites Visible Albedo Mead(Rain fed) Lat: Lon: Ground measured albedo Estimates MODIS 16-day albedo Julian Day Example of time series total visible albedo from MODIS 15 observations in 2005 over four AmeriFlux sites

16 Validation Results: Snow Surface Shortwave Albedo Shortwave Albedo Saddle Julian Day NASA-SE Ground measured albedo Estimates MODIS 16-day albedo Julian Day Ground measured albedo Estimates MODIS 16-day albedo Greenland sites (GC-Net) 2003 Comparison with MODIS albedo products and ground measurements

17 . Validation Results: Surface Reflectance Reflectance BrattsLake (50.28,-104.7) Cropland Julian Day Estimated Red Band IBRF MODASRVN Red Band IBRF Estimated NIR Band IBRF MODASRVN NIR Band IBRF Reflectance Egbert (44.226,-79.75) Cropland Estimated Red Band IBRF MODASRVN Red Band IBRF Estimated NIR Band IBRF MODASRVN NIR Band IBRF Julian Day Example of time series instantaneous reflectance from MODIS observations in 2005 over AERONET sites Comparison of estimated and MODASRVN instantaneous bidirectional reflectance for MODIS band1&2 over 16 AERONET sites during 2005 Estimated IBRF y=1.0177x R-squared=0.698 Bias= RMSE= All Sites Band MODASRVN IBRF Estimated IBRF y= x R-squared=0.732 Bias= RMSE= All Sites Band MODASRVN IBRF

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 Summary of Validation Results Albedo Our Retrievals F&PS Requirement Accuracy (Bias) Precision (RMSE) % R N/A Reflectance Our Retrievals F&PS Requirement Accuracy (Bias) Precision (RMSE) R (Red) (NIR) (Red) (NIR) (Red) (NIR) % N/A

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 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 Questions?