toward a stable real-time green vegetation fraction le jiang, dan tarpley, felix kogan, wei guo and...

Toward a Stable Real-Time Green Vegetation Fraction

Le Jiang, Dan Tarpley, Felix Kogan, Wei Guo and Kenneth Mitchell

JCSDA Science Workshop May 31 – June 1, 2006

The Problem: AVHRR NDVI and green vegetation fraction are not stable over time

Averaged NDVI (40°S to 40°N)

0

0.05

0.1

0.15

0.2

0.25

0.3

1980 1985 1990 1995 2000 2005 2010

Year

ND

VI

NOAA-7 NOAA-9 NOAA-11 NOAA-14 NOAA-16

Approach: Develop an algorithm for operational adjustment to real-time global NDVI to ensure the time series consistency

• Assume the “climatology” of the Earth’s distribution of NDVI is stable over time for each week

• Select a climatology of “standard” years without problems with: – Instrument calibration

– Equator crossing time

– Natural perturbations of the atmosphere (volcanic eruptions)

• Evaluate mathematical or statistical procedures that adjust real time NDVI distributions to climatological “standard”– Method has to retain regional information about vegetation condition

– Method should be simple and require minimum assumptions

– Method should not rely on outside data sources

• Select procedure for operational use

NDVI Time Series

“Benchmark NDVI Climatology” selected from years with the best known data quality: 1989, 1990, 1995 (after wk 14), 1996, 1997, 1998 from NOAA-11 and -14

Averaged NDVI (40 °S to 40 °N)

0

0.05

0.1

0.15

0.2

0.25

0.3

1980 1985 1990 1995 2000 2005 2010

Year

ND

VI

Approaches Evaluated Description

a), b) Range Re-Scaling

(RRS)

c. Normalization (NML)

d. Linear Regression (LR)

e. Adjusted Cumulative Distribution Function (ACDF)

Adjusting the CDF of real-time weekly global NDVI to match that of the benchmark climatology for week i

f. NML+RRS NML followed by RRS

g. Adjusting Satellite-By-Satellite (ASBS)+NML

ASBS followed by NML

h. GIMMS Data (for comparison)

Independent datasets from Global Inventory Monitoring & Modeling Studies (GIMMS) group: global 8-km, semi-monthly, time series decomposed / reconstructed, RT corrected

min0max0

min00

minmax

min

NDND

NDND

NDND

NDND

0

00

NDND

NDNDNDND

NDbaND 0

Solve ND0 (equivalent ND within the context of benchmark climo.

a) Use pixel max ND as NDmax

b) Use Avg of top 1% max ND as NDmax

Schematic Illustration of the ACDF Approach (option e)

Benchmark CDF

Before Correction Average NDVI (40°S to 40°N)

0.1

0.15

0.2

0.25

0.3

1980 1985 1990 1995 2000 2005 2010

ND

VI

NVI

After CDF Correction Time Series of CDF corrected NDVI

0.1

0.15

0.2

0.25

0.3

1980 1985 1990 1995 2000 2005 2010

ND

VI

NVI_CDF

Before and AfterAveraged NDVI (40°S to 40°N)

Original and CDF Corrected

0.1

0.15

0.2

0.25

0.3

1980 1985 1990 1995 2000 2005 2010

ND

VI

NVI NVI_CDF

Results & Evaluation

Results after simple adjustment Yellow – maximum NDVI Green – average of top 1% NDVIBlue – average NDVIRed – standard deviation of NDVI

a. RRS b. RRS_Top 1%

c. NML d. LR

e. ACDF f. NML+RRS

g. ASBS+NML h. GIMMS Data

E.g. Performance of different fixes on Class 7&12 (Short Ground Cover and Cropland): Yellow – maximum NDVI Green – average of top 1% NDVIBlue – average NDVIRed – standard deviation of NDVI

a. RRS

b. RRS_Top 1% c. NML

d. LR e. ACDF

f. NML+RRS g. ASBS+NML

h. GIMMS Data

Un-adjusted

Satellite ECTs for the period 1982 to 2003

Effects after different adjustment for week 27 from 1982 to 2003.

a. RRS

b. RRS_Top 1% c. NML

d. LR e. ACDF

f. NML+RRS g. ASBS+NML

h. GIMMS Data

Un-adjusted

Comparison of drought detection signatures over CONUS (Weeks 16, 20, 24, 29, 33 and 37 in 2005)

Left column: ND* from un-adjusted NDVI;

Middle column: ND* from ACDF fixed NDVI;

Right column: Vegetation Condition Index (VCI) based on manual adjustment of unfixed NDVI

ND

NDNDND

*

Indicator (or quantity examined):

Summary

• ACDF correction looks best

• Successfully compensates for sensor change and orbit drift

• Local vegetation anomalies retained in corrected data

• More validation needed

• Applicable to VIIRS NDVI on day 1

• Possible long-term NDVI trends removed

Stop

Before Correction

Time Series of NVI

0.1

0.120.14

0.160.18

0.20.22

0.240.26

0.280.3

1980 1985 1990 1995 2000 2005 2010

ND

VI

NVI

Average NDVI (40°S to 40°N)

0

0.05

0.1

0.15

0.2

0.25

0.3

1980 1985 1990 1995 2000 2005 2010

ND

VI

NVI

Time Series of CDF corrected NDVI

0

0.05

0.1

0.15

0.2

0.25

0.3

1980 1985 1990 1995 2000 2005 2010

ND

VI

NVI_CDF

13-class global land surface type map1) Broadleaf-evergreen trees (tropical forest); 2) Broadleaf-deciduous trees; 3) Broadleaf and needle leaf trees; 4) Needle leaf evergreen trees; 5) Needle leaf deciduous trees (larch); 6) Broadleaf trees with ground cover (savanna); 7) Short groundcover (in perennial); 8) Broadleaf shrubs with perennial ground cover; 9) Broadleaf shrubs with bare soil; 10) Tundra (dwarf trees and shrubs with ground cover); 11) Bare soil; 12) Cropland (cultivated); 13) Glacial.

Evolution of percentages for different NDVI intervals from 1982 to 2003 (Black – 0.0% ~ 20%, Blue – 20% ~ 40%, Green – 40% ~ 60%, Yellow – 60% ~ 80%, Red – 80% ~ 100% of maximum NDVI)

a. RRS

b. RRS_Top 1% c. NML

d. LR e. ACDF

f. NML+RRS g. ASBS+NML

h. GIMMS Data

Un-adjusted

ND* of the unfixed, adjusted, and GIMMS datasets for week 26 over US Great Plains from 1982 to 2003

a. RRS

b. RRS_Top 1% c. NML

d. LR e. ACDF

f. NML+RRS g. ASBS+NML

h. GIMMS Data

Un-adjusted

ND

NDNDND

*