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Prototyping GOES-R Albedo Algorithm Based
on MODIS Data Tao Hea, Shunlin Lianga, Dongdong Wanga
a. 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 [email protected] Jul 29, 2011
Contents
Introduction 1
Methodology 2
Data and Results 3
4 Summary and Conclusions
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, …
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.
ABI vs MODIS
GOES-R ABI MODIS
Channel Number
Central Wavelength (µm)
Spatial Resolution
Channel Number
Central Wavelength (µm)
Spatial 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
GOES-R
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)
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.
Methodology
§ 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.).
Cost Function:
Atmospheric Radiative Transfer Solution with Land Surface BRDF Modeling
§ Atmospheric Radiative Transfer Formulation for better modeling the interaction between atmosphere and non-Lambertian surfaces
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.
Spherical Albedo
(Qin, et al. 2001)
TOA Reflectance
Path Reflectance
Transmittance Matrix
Surface Reflectance Matrix
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
Flowchart
Albedo Climatology
TOA Reflectances
Prior BRDF Prior AOD Radiative Transfer Model
Optimization Optimal BRDF
Parameters and AOD
Spectral Reflectance
Angular Integration
Spectral Albedo
Narrow-2-Broadband Conversion
Broadband Albedo
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
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.
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
50 100 150 200 250 300 3500
0.2
0.4
0.6
0.8
1
Julian Day
Shor
twav
e Al
bedo
Bondville Lat:40.05 Lon:-88.37
Ground measured albedoEstimatesMODIS 16-day albedo
50 100 150 200 250 300 3500
0.2
0.4
0.6
0.8
1
Julian Day
Visib
le A
lbed
o
Mead(Rain fed) Lat:41.1797 Lon:-96.4396
Ground measured albedoEstimatesMODIS 16-day albedo
Example of time series total visible albedo from MODIS observations in 2005 over four AmeriFlux sites
Example of time series shortwave albedo from MODIS observations in 2005 over six SURFRAD sites
50 100 150 200 250 300 3500.2
0.4
0.6
0.8
1
Julian Day
Shor
twav
e Al
bedo
Saddle
Ground measured albedoEstimatesMODIS 16-day albedo
50 100 150 200 250 300 3500.2
0.4
0.6
0.8
1
Julian Day
Shor
twav
e Al
bedo
NASA-SE
Ground measured albedoEstimatesMODIS 16-day albedo
Validation Results: Snow Surface
• Greenland sites (GC-Net)
• 2003 • Comparison
with MODIS albedo products and ground measurements
0 50 100 150 200 250 3000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Julian Day
Ref
lect
ance
BrattsLake (50.28,-104.7) Cropland
Estimated Red Band IBRFMODASRVN Red Band IBRFEstimated NIR Band IBRFMODASRVN NIR Band IBRF
0 50 100 150 200 250 300 3500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Julian Day
Ref
lect
ance
Egbert (44.226,-79.75) Cropland
Estimated Red Band IBRFMODASRVN Red Band IBRFEstimated NIR Band IBRFMODASRVN NIR Band IBRF
Validation Results: Surface Reflectance
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
y=1.0177x+0.0065R-squared=0.698Bias=0.0084RMSE=0.0269
All Sites Band1
MODASRVN IBRF
Estim
ated
IBR
F
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
y=0.91535x+0.024R-squared=0.732Bias=0.0025RMSE=0.0471
All Sites Band2
MODASRVN IBRFEs
timat
ed IB
RF
.
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
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.
Summary of Validation Results
Albedo Our Retrievals F&PS Requirement
Accuracy (Bias) 0.0137 0.08 Precision (RMSE) 0.0618 10%
R2 0.8208 N/A
Reflectance Our Retrievals F&PS Requirement
Accuracy (Bias) 0.0084 (Red) 0.0025 (NIR)
0.08
Precision (RMSE) 0.0269 (Red) 0.0471 (NIR)
5%
R2 0.698 (Red) 0.732 (NIR)
N/A
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.
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).
Questions?