1

GOES-R AWG Land Team: ABI Land Surface Albedo (LSA)

and Surface Reflectance Algorithm

June 14, 2011Presented by:Shunlin Liang

Dongdong Wang, Tao HeUniversity of Maryland, College Park

Contributors: Bob Yu1, Hui Xu2

1NOAA/NESDIS/STAR2IMSG

2

Outline

Executive Summary Algorithm Description ADEB and IV&V Response Summary Requirements Specification Evolution Validation Strategy Validation Results Summary

3

Executive Summary

This ABI Land Surface Albedo Algorithm (LSA) generates the Option 2 products of surface albedo and surface reflectance.

The Version 4 code and 80% ATBD have been delivered. The delivery of Version 5 code and 100% ATBD are on track. An improved optimization algorithm was developed to estimate the

surface properties and atmospheric condition simultaneously. The algorithm of direct estimation of albedo was added as back-up to

handle cases where the routine algorithm fails. Background information of albedo climatology and gap-filling

technology are used to reduce the frequency of missing and filling values.

Both albedo and reflectance products have been validated. Validation shows the LSA algorithm can satisfy the requirements.

4

Algorithm Description

5

Algorithm Summary

This ABI LSA Algorithm generates the Option 2 products of Surface Albedo and Surface Reflectance.

Combination of atmospheric correction and surface BRDF modeling, producing both surface directional reflectance and shortwave albedo simultaneously .

The unique advantages of GOES-R ABI(multi-channel and frequent refreshing rate) are fully utilized in the cooperative retrieval of surface BRDF and atmospheric parameters.

The operational codes are run in two modes. The time-consuming process of retrieving BRDF is handled in offline mode.

Back-up algorithm is designed to handle rapidly-changing surfaces. The albedo climatology is incorporated as background information

effectively in the inversion algorithms. Use of background information and gap-filling technique greatly

reduces the frequency of data gaps.

6

Introduction to the Offline Mode

The calculation of albedo needs a long time series. To improve efficiency, an “offline” computation is carried out at the end of each day to derive BRDF parameters. These parameters will be stored to calculate albedo and surface reflectance for the next day.

The online mode is run in real-time to generate instantaneous full-disk albedo and surface reflectance products.

The offline and online modes share similar mathematical and physical theories and have slightly different input/output

Flowchart of offline Mode

BRDF Retrievals

from Previous Day

Output Clear-sky Reflectance with Time

Stamp

Aerosol Product at Ti

Cloud Screening

“First Guess”Aerosol Optical

Depth

Optimization

StartOld

Reflectance Database

Ti is the last time step?

Cloud Mask at Time Step Ti

No

Zenith Angle Screening

TOA Reflectances

at Time Step Ti

Illumination Geometries

at Ti

New Reflectance

Database

Radiative Transfer & BRDF Models

Next Ti

Yes

Water Screening Land/water Mask

Time Stamp Check

BRDF Retrievals

and Quality Flags

Albedo Climatology

Flowchart of online mode

TOA Reflectances

BRDF Retrievals

from Previous Day

Good Quality? Spectral Surface Albedo

Cloud Screening

Number of Cloud Free Observation(s)?

Lambertian Correction

BRDF Model

Spectral Surface Reflectance

Water ScreeningLand/water Mask

Zenith Angle Screening

Broadband Surface Albedo

N=0

Lambertian Correction

N=1Optimization

N≥2

AOD Product

Cloud Screening

Number of Cloud Free Observation(s)?

Spectral Surface Reflectance

N=0

Direct Estimation Algorithm

N≥1

Broadband Surface Albedo

Albedo Climatology

No

BRDF ModelYes

Cloud Correction

Back-up Algorithm A back-up algorithm, which directly estimates albedo from TOA radiances

through extensive radiative transfer simulation, has been developed to handle the failure of the LSA routine algorithm.

The two figures show the error budgets (Left: R2, Right: RMSE) of the LSA back-up algorithm under various view and relative azimuth angles. The slightly reduced accuracy at hot-spot is evident due to the larger uncertainties in that geometry.

Examples of Albedo Algorithm Output

10

Retrieved broadband blacksky albedo map using MODIS as the proxy data

May 1st, 2005 Around 48˚N, 102˚W

11

Algorithm Changes from 80% to 100%

Back-up algorithm was added.

Gap-filling technique was added to reduce missing values

Metadata output added.

Quality flags standardized.

12

ADEB and IV&V Response Summary

All ADEB comments and recommendations have been addressed.

We have paid special attention to the scaling problems of validating albedo and submitted a GOES-R val/cal proposal to further address this issue.

With regard to the problem that “graceful degradation was rarely addressed”, we proposed the combination of back-up algorithm and gap-filling technique to handle the failure of the LSA routine algorithm.

13

Requirements and Product Qualifiers

Surface Albedo and Surface Reflectance

Nam

e

User &

Priority

Geograp

hic

Coverage

(G, H

, C, M

)

Vertical

Resolu

tion

Horizon

tal

Resolu

tion

Map

pin

g

Accu

racy

Measu

remen

ts

Ran

ge

Measu

remen

ts

Accu

racy

Refresh

Rate/C

overage Tim

e Op

tion (M

ode

3) Refresh

Rate O

ption

(Mod

e 4)

Data

Laten

cy

Prod

uct M

easurem

ent P

recision

Tem

poral C

overage Qu

alifiers

Prod

uct E

xtent Q

ualifier

Clou

d C

over Con

dition

s Qu

alifier

Prod

uct S

tatistics Qu

alifier

Surface Albedo

GOES-R

FD N/A 2 km 2 km 0 to 1 Albedo Units

0.08 Albedo Units

60 min 60 min 3236 sec

10 % Sun at 67 degree daytime solar zenith angle

Quantitative out to at least 70 degrees LZA and qualitative beyond

Clear conditions associated with threshold accuracy

Over specified geographic area

Nam

e

User &

Priority

Geograp

hic

Coverage

(G, H

, C, M

)

Vertical

Resolu

tion

Horizon

tal

Resolu

tion

Map

pin

g

Accu

racy

Measu

remen

ts

Ran

ge

Measu

remen

ts

Accu

racy

Refresh

Rate/C

overage Tim

e Op

tion (M

ode

3) Refresh

Rate O

ption

(Mod

e 4)

Data

Laten

cy

Prod

uct M

easurem

ent P

recision

Tem

poral C

overage Qu

alifiers

Prod

uct E

xtent Q

ualifier

Clou

d C

over Con

dition

s Qu

alifier

Prod

uct S

tatistics Qu

alifier

Surface Reflectance

GOES-R

FD N/A 2 km 2 km 0 to 2 0.08 60 min 60 min 3236 sec

5 % Sun at 67 degree daytime solar zenith angle

Quantitative out to at least 70 degrees LZA and qualitative beyond

Clear and cloudy conditions

Over specified geographic area

14

Proposal to change some requirement parameters

Parameters Original Description Proposed Changes

LSA Product Measurement Precision

10% 0.08 albedo units

Surface Reflectance Measurement Accuracy

0.08 0.08 reflectance units

Surface Reflectance Product Measurement Precision

5% 0.08 reflectance units

15

Validation Approach and Results

Validation Approach

Albedo Validation» Direct comparison of retrieved albedo with field

measurement.– Proxy data: MODIS, SEVIRI– Surface types: “bright”, ”dark” surfaces

» Intercomparison with existing albedo products– MODIS albedo product: 16 days composite

Surface Reflectance Validation» Direct validation

– No routine field measurements of surface reflectance– Calculate “true” surface reflectance from TOA reflectance

using in situ measurements of atmospheric parameters» Intercomparison with other products

16

Validation Approach: datasets

Satellite proxy data » MODIS: multi-channels, but polar orbiting» SEVIRI: geostationary, but limited channels (three)

In situ measurement of surface albedo» AmeriFlux» SURFRAD» GC-Net» IMECC

Surrogate of surface reflectance data» ASRVN: AERONET-based Surface Reflectance

Validation Network

17

18

Validation Results: Vegetated Surface

50 100 150 200 250 300 3500

0.2

0.4

0.6

0.8

1

Julian Day

Sho

rtw

ave

Alb

edo

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

Vis

ible

Alb

edo

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

19

50 100 150 200 250 300 3500.2

0.4

0.6

0.8

1

Julian Day

Sho

rtw

ave

Alb

edo

Saddle

Ground measured albedoEstimatesMODIS 16-day albedo

50 100 150 200 250 300 3500.2

0.4

0.6

0.8

1

Julian Day

Sho

rtw

ave

Alb

edo

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

20

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 IBRF

MODASRVN Red Band IBRFEstimated NIR Band IBRF

MODASRVN 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 IBRF

MODASRVN Red Band IBRFEstimated NIR Band IBRF

MODASRVN 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

Est

imat

ed IB

RF

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 IBRF

Est

imat

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

21

Summary of Validation Results

Albedo Our Retrievals F&PSRequirement

Accuracy (Bias) 0.0137 0.08

Precision (RMSE) 0.0618 10%

R2 0.8208 N/A

Reflectance Our Retrievals F&PSRequirement

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

Next Steps to Reach 100%

Refine the subroutine (back-up algorithm) to handle snow/non-snow transitional periods and other cases of main algorithm failure;

Continue to improve the algorithms in terms of accuracy and computational efficiency;

Carefully design strategy of graceful degradation; Conduct more validation activities to further

evaluate the algorithm and quantify the accuracy and precision of the data;

Revise the code by following the coding regulation.

22

23

Summary

The ABI LSA algorithm takes full advantage of the ABI unique new capabilities of frequent multichannel observations.

This algorithm works on both bright and dark surfaces. The algorithm of direct estimation of albedo was

incoporated as back-up to handle cases where the routine algorithm fails.

Background information of albedo climatology and gap-filling technology are used to reduce the frequency of missing and filling values.

Validation shows the LSA algorithm can satisfy the requirements.

The delivery of Version 5 code and 100% ATBD are on track.