# Presentation: 771667

## 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

**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**

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

| 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 |

- Solve *ND**0* (equivalent *ND* within the context of benchmark climo.

- a) Use pixel max ND as *ND**max*
- b) Use Avg of top 1% max ND as *ND**max*

## Schematic Illustration of the ACDF Approach (option e)

- Benchmark CDF

## Before Correction

## After CDF Correction

## Before and After

## Results & Evaluation 

- a

- b

- c

- d

- e

- f

- g

- h

**Results after simple adjustment**
- Yellow – maximum NDVI
- Green – average of top 1% NDVI
- Blue – average NDVI
- Red – 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): 

**E.g. Performance of different fixes on Class 7&12 (Short Ground Cover and Cropland): **
- Yellow – maximum NDVI
- Green – average of top 1% NDVI
- Blue – average NDVI
- Red – 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

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

- 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 

**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

- 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