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 the@umd.edu 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?