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GOES-R AWG Land Team: Green Vegetation Fraction

(GVF)

Yuhong Tian (IMSG)Peter Romanov (CREST)

Bob Yu (STAR)Dan Tarpley (Short and Assoc.)

Hui Xu (IMSG) Felix Kogan (STAR)

June 14-16, 2011

Outline

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

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Executive Summary· The GVF algorithm is being developed to generate Option 2 Green

Vegetation Fraction (GVF) from GOES-R ABI data.

· Version 5 was delivered June 13. ATBD (100%) will be delivered at the end of June as scheduled.

· The GVF Algorithm is based on GOES-R ABI NDVI and uses GOES-R ABI cloud mask.

· An empirical model to correct NDVI for angular anisotropy has been developed and implemented in the GVF algorithm

· The GVF algorithm has been developed and tested with MSG SEVIRI data.

· Validation studies indicate that the retrieved product is within specs.

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Algorithm Description

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What is GVF?Green Vegetation Fraction (GVF): fraction of the area within the instrument footprint occupied by green vegetation

GVF Definition

GVF is used to parameterize moisture and energy fluxes in Numerical Weather Prediction (NWP) and climate models

Why GVF is needed?

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GVF Algorithm

GVF = (NDVI – NDVImin) / (NDVImax – NDVImin)

• NDVImax and NDVImin : global parameters (location independent)

• All NDVIs are TOA values• NDVI-based approaches have been widely used with AVHRR data

(Gutman, 1998; Ignatov, 1999; Gallo, 2001)

ABI GVF Algorithm: - clear sky - NDVI-based - linear mixture approach

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• To develop a NDVI angular anisotropy model

• To establish NDVImax and NDVImin

GVF: Major Tasks

4 8 12 16 20L oca l Tim e , h

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Year: 2007, Day: 302Location: 8 .7 0N, 22 .7 0ESate llite view ang le: 28 0

BackscatterForward Scatter

MSG SEVIRI, Lat=8.7N Lon=22.7E

• SEVIRI NDVI changes up to 0.4 (or over 60% of the max value) due to the diurnal change in the illumination geometry

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NDVI Anisotropy

A kernel-driven model was developed to characterize NDVI anisotropy:

NDVI(ƟS, ƟV, φ) = NDVI(0,0,0) [1 + C1 f1 +C2 f2] f1 =( tanƟS + tan ƟV ) : to describe NDVI decrease at large ƟS and ƟV

f2=( cosφ + 1 )2 (tanƟS tanƟV)1/2 : to describe NDVI azimutal changes

C1 and C2 were established empirically from diurnal clear-sky SEVIRI NDVI time series

C1 = -0.0723

C2 = -0.0101

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NDVI frequency distribution from MSG data for 2007-2008

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Establishing NDVImax for GVF

NDVI frequency distribution is based on MSG SEVIRI weekly maximum NDVI composites for 2007-2008 NDVI was brought to the reference geometry of observations (Ɵs = 450, Ɵv = 450, φ = 900)

NDVImax was assumed to equal the value of 95th percentile of NDVI frequency distribution

NDVImax = 0.59

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NDVImin was assumed to equal the 95th percentile of NDVI frequency distribution in Sahara desert (NDVImin=0.13)

Establishing NDVImin for GVF

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GVF Processing Outline

Acquire satellite data (angle values, level 1b QC flags)

Acquire model data (NDVImin, NDVImax, NDVI angular model parameters)

Acquire ancillary data (ABI NDVI, NDVI QC flags)

Calculate GVF

GVF start

GVF end

Declare & Initialize variables

Read Data

Output: GVF, QC Flag

Bring NDVI to the reference geometry

Detailed Flow Chart of GVF

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Algorithm Summary

● GVF is derived from GOES-R NDVI product

● Only cloud clear cases are considered in GVF retrievals

● Linear mixture model is used to estimate GVF from NDVI

● NDVI is corrected for angular anisotropy using a kernel-driven model

● NDVI anisotropy model kernel weights as well as endmember values for the GVF model (NDVImax and NDVImin) were determined empirically from SEVIRI clear sky data

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Light gray: clouds

black: solar zenith angle above 67 deg

GVF ProductGVF from instantaneous images (date: 2007275)

14Light gray: clouds and area with satellite view zenith angle above 70 deg

GVF Product

Weekly composite image

Daily composite data vs. weekly composite data

Daily composite image

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Algorithm Changes from 80% to 100%

· The algorithm parameters NDVImax and NDVImin values are updated based on more available SEVIRI data.

· More validation of the GVF product with the common dataset is made.

· Metadata outputs are added.· Quality flags are standardized.

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ADEB and IV&V Response Summary

· Recommendation 1: Insufficient validation Response: Direct validation is impossible as no in situ

measurements are available. We have made more comparisons with other products based on geostationary (SEVIRI) and polar orbiting (AVHRR) satellites as part of the product verification activities.

· Recommendation 2: Continue developing and improving the algorithms

Response: We will continue to improve the NDVI angle correction model using TOA NDVI modeled with radiative transfer model (6S) and SEVIRI-observed values, and to update global NDVImax and NDVImin values based on more satellite measurements.

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ADEB and IV&V Response Summary

· Recommendation 3: Requirements are conservative

Response: The retrieval accuracy depends on the quality of other input data. For example, the atmospheric composition (aerosol, absorbing gases content) are often unavailable, which affects the accuracy of the anisotropic correction of NDVI.

· Recommendation 4: Advantages of geostationary satellites are not used

Response: The requirement is to derive GVF on a hourly basis. Using observations at 15 min interval within 1 hour time period will hardly add much to the product in terms of extended coverage.

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ADEB and IV&V Response Summary

· Recommendation 5: The current validation methodology will not uncover systematic errors

Response: We will include comparison of GVF with estimates of polar orbiting satellites (NOAA AVHRR GVF).

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Current Requirements

Name User &Priority

GeographicCoverage(G, H, C, M)

VerticalResolution

HorizontalResolution

MappingAccuracy

MeasurementsRange

MeasurementsAccuracy

RefreshRate/Coverage Time Option (Mode 3)

Refresh Rate Option (Mode 4)

DataLatency

Product Measurement Precision

Temporal Coverage Qualifiers

Product Extent Qualifier

Cloud Cover Conditions Qualifier

Product Statistics Qualifier

Vegetation Fraction: Green

GOES-R

CONUS N/A 2 km 1 km 0.0 to 1 (unitless)

0.10 for satellite zenith angles below 55; 0.20 for satellite zenith angles 55~70 degrees

60 min 60 min 3236 sec

0.10 for satellite zenith angles below 55; 0.20 for satellite zenith angles 55~70 degrees

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

Accuracy and Precision:

0.1 for θsat<550

0.2 for 550 < θsat<700

Validation Approach

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GVF Algorithm Validation/Verification

• No ground truth is available and thus no “real” validation can be performed

Features characterizing the validity of GVF product

• Adequate reproduction of GVF seasonal change (qualitative)• Adequate reproduction of GVF geographical distribution• Small spurious diurnal change of derived GVF

• Diurnal change should be consistent with precision specification

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GVF ValidationDetails

Diurnal change of derived GVF

• Get hourly clear-sky GVF data for every location • Calculate daily GVF RMSD and statistics of RMSD for all pixels• Compare RMSD with precision specifications

Comparison of the GVF product with the common dataset

• NOAA AVHRR based GVF and EUMETSAT LAND-SAF SEVIRI based fraction vegetation cover (FVC) are used• GOES-R GVF are composited to similar time scale and map projection before the comparison• Comparison is pixel by pixel for the full disk

Validation Results

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GVF Algorithm Verification

Examining the diurnal GVF variation• Statistics of diurnal GVF variations (RMSD): SEVIRI full disk

clear sky data with solar zenith angle < 67 degree (10 weeks run results: Oct. 1~13, 2007):

For θsat<550

RMSD = Precision = 0.052

For 550 < θsat<700

RMSD = Precision = 0.046

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GVF Algorithm VerificationResults Summary

Precision F&PSGVF Algorithm

Aug. 1~ 31 Feb. 1~14 Apr. 1~ 13 Oct. 1~13

θsat<550 0.1 0.054 0.050 0.051 0.052

550 < θsat<700 0.2 0.081 0.079 0.061 0.046

• Statistics of diurnal GVF variations (RMSD): SEVIRI full disk clear sky data with solar zenith angle < 67 degree (10 weeks run results: Aug. 1~31, 2006; Feb. 1~14, 2007; Apr. 1~13, 2007; Oct. 1~13, 2007):

GVF Algorithm VerificationComparison with Common dataset

AVHRR GVF provides weekly GVF at 0.144 degree resolution.

The SEVIRI GVF was composited to weekly from hourly data and converted to 0.144 degree resolution

Right image: comparing SEVIRI GVF to AVHRR GVF

For θsat<550

Bias = 0.014; STD dev = 0.104

For 550 < θsat<700

Bias = 0.008; STD dev = 0.165

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GVF Algorithm Verification

Results Summary (Bias)

Bias(y-x) F&PS(Accuracy)

Results in 2007 Results in 2009

Apr. 1~7

Apr. 8~14

Oct.1~7 Oct. 8~14

Jul. 1~7 Feb. 1~7

θsat<550 0.1 -0.017 -0.021 -0.001 -0.010 0.017 -0.002

550 < θsat<700 0.2 -0.058 -0.082 0.013 0.015 -0.04 -0.007

• Land-SAF (y) versus GOES-R (x) Proxy

Bias(y-x) F&PS(Accuracy)

Results in 2007Apr. 2~8 Oct. 1 ~ 7

θsat<550 0.1 0.014 0.026

550 < θsat<700 0.2 0.008 0.043

• AVHRR (y) versus GOES-R (x) proxy

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GVF Algorithm Verification

Results Summary (STD Dev)

STD Dev(y-x) F&PS(Precision)

Results in 2007 Results in 2009

Apr. 1~7 Apr. 8~14 Oct. 1~7 Oct. 8~14

Jul. 1~7 Feb. 1~7

θsat<550 0.1 0.114 0.110 0.107 0.116 0.109 0.113

550 < θsat<700 0.2 0.169 0.201 0.184 0.195 0.133 0.134• AVHRR (y) versus GOES-R (x) proxy

STD Dev(y-x) F&PS(Precision)

Results in 2007Apr. 2~8 Oct. 1 ~ 7

θsat<550 0.1 0.104 0.114

550 < θsat<700 0.2 0.165 0.173

• Land-SAF (y) versus GOES-R (x) Proxy

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Summary

● GOES-R GVF algorithm development is on schedule.

● MSG SEVIRI is used as GOES-R ABI prototype

● The algorithms uses NDVI and a linear-mixture approach to convert NDVI into GVF.

● Preliminary estimates show that the algorithm meets performance requirements

● Further validation studies will be conducted with GOES-R simulated data/products generated by AIT.

● Tailored products such as daily and weekly products and mostly cloud-free GVF products are needed and have to be developed