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GOES-R AWG Land Team: Green Vegetation Fraction (GVF) Yuhong Tian (IMSG) Peter Romanov (CREST)

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

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GOES-R AWG Land Team: Green Vegetation Fraction (GVF) Yuhong Tian (IMSG) Peter Romanov (CREST)

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  1. GOES-R AWG Land Team: Green Vegetation Fraction (GVF) YuhongTian (IMSG) Peter Romanov (CREST) Bob Yu (STAR) Dan Tarpley (Short and Assoc.) HuiXu (IMSG) Felix Kogan (STAR) June 14-16, 2011

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

  3. 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.

  4. Algorithm Description

  5. What is GVF? GVF Definition Green Vegetation Fraction (GVF): fraction of the area within the instrument footprint occupied by green vegetation Why GVF is needed? GVF is used to parameterize moisture and energy fluxes in Numerical Weather Prediction (NWP) and climate models 5

  6. GVF Algorithm ABI GVF Algorithm: - clear sky - NDVI-based - linear mixture approach GVF = (NDVI – NDVImin) / (NDVImax – NDVImin) • NDVImaxandNDVImin : 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)

  7. MSG SEVIRI, Lat=8.7N Lon=22.7E Forward Scatter Backscatter GVF: Major Tasks • To develop a NDVI angular anisotropy model • To establish NDVImaxandNDVImin • SEVIRI NDVI changes up to 0.4 (or over 60% of the max value) due to the diurnal change in the illumination geometry

  8. 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 largeƟSand ƟV f2=( cosφ + 1 )2 (tanƟStanƟV)1/2: to describe NDVI azimutalchanges C1 and C2 were established empirically from diurnal clear-sky SEVIRI NDVI time series C1= -0.0723 C2 = -0.0101

  9. 95th percentile 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 NDVI frequency distribution from MSG data for 2007-2008

  10. 95th percentile Establishing NDVImin for GVF NDVIminwas assumed to equal the 95thpercentile of NDVI frequency distribution in Sahara desert (NDVImin=0.13)

  11. GVF Processing Outline GVF start Acquire satellite data (angle values, level 1b QC flags) Declare & Initialize variables Acquire model data (NDVImin, NDVImax, NDVI angular model parameters) Read Data Acquire ancillary data (ABI NDVI, NDVI QC flags) Bring NDVI to the reference geometry Calculate GVF Output: GVF, QC Flag Detailed Flow Chart of GVF GVF end

  12. 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

  13. GVF Product GVF from instantaneous images (date: 2007275) Light gray: clouds black: solar zenith angle above 67 deg

  14. GVF Product Daily composite data vs. weekly composite data Daily composite image Weekly composite image Light gray: clouds and area with satellite view zenith angle above 70 deg

  15. Algorithm Changes from 80% to 100% • The algorithm parameters NDVImax and NDVImin values are updated based on more available SEVIRI data. • Morevalidation of the GVF product with the common dataset is made. • Metadata outputs are added. • Quality flags are standardized.

  16. 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.

  17. 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.

  18. 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).

  19. Current Requirements Accuracy and Precision: 0.1 for θsat<550 0.2 for 550 < θsat<700

  20. Validation Approach

  21. 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 21

  22. 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

  23. Validation Results

  24. 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

  25. GVF Algorithm Verification Results Summary • 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):

  26. GVF Algorithm Verification Comparison 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

  27. GVF Algorithm Verification Results Summary (Bias) • Land-SAF (y) versus GOES-R (x) Proxy • AVHRR (y) versus GOES-R (x) proxy

  28. GVF Algorithm Verification Results Summary (STD Dev) • Land-SAF (y) versus GOES-R (x) Proxy • AVHRR (y) versus GOES-R (x) proxy

  29. 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

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