# Presentation: 193133

## Global Vegetation Processing System (GVPS) Status Update

***Dan Tarpley ***
*(NOAA/NESDIS/ORA), *
***Le Jiang***
*(IMSG at NOAA/NESDIS)*

- 04/22/2005

## System Overview

**Input:**
- Weekly GVI (specifically, ch1, 2, 4, 5 in GVI array
- 2500 x 904 grids, 0.144-deg resolution)

**Output: **
-  - Primary products
- (for NCEP models):
- Weekly GVF (Green Vegetation Fraction) in GRIB and binary format along with SMN.

**Algorithm**
-  NDVI, T
-  Smoothing  SMN
-  VCI (Vegetation Condition Index)
-  **Classified Statistical Approach:**
**                                      ****(*)**
** **
**for each week, hemisphere & vegetation class**

**(*)**:                                                         for each week, vegetation class, and hemisphere; VCI is vegetation condition index;             is standard deviation of GVF for each pixel; a, b is pre-derived for each week, vegetation class, and hemisphere from multi-year GVF and VCI datasets.

- 04/22/2005

## Status    

-  Experimental products provided online, 20+ years products and climatology available for downloading
-  - URL  _[http://](http://www.orbit.nesids.noaa.gov/corp/scsb/landprod/gvps)__[www.orbit.nesids.noaa.gov/corp/scsb/landprod/gvps](http://www.orbit.nesids.noaa.gov/corp/scsb/landprod/gvps)_
-  Operational production
-   - Near real-time products sent to NCEP/EMC by OSDPD on weekly basis.
-   - Web access: N/A.
-  NCEP/EMC evaluation: in progress
-  Issue of AVHRR instrument-to-instrument stability under investigation
-    - Long term NDVI change

- 04/22/2005

## Issues:        Instrument-to-instrument stability

- Reason for inconsistency**:  **
-  Sensor filter functions among different AVHRR
-  Change in equator crossing time)
-  Ad hoc fix for N16:
- NDVI’ = 0.92*NDVI

**Issues:        **Instrument-to-instrument stability

- 04/22/2005

## Approaches

**• ****Quick Fix approaches**
-   - Linear approaches:** **
-      	a. Max / Min scaling:
-      	b. Mean / STD normalization:
-      	c. Linear regression:
-   - Non-linear approach:
-     	a. Adjust data with Cumulative Distribution Function
-   - Evaluation: “fixed” NDVI series using different methods will be evaluated by their ability to detect local NDVI anomaly for well-known drought cases.

- 04/22/2005

## Approaches

- NOAA-7

- Resulting Adjustments (Grey lines are the actual fits)

**Approaches**
-  Derive NOAA-14 equivalent NDVI for all sensors: correct NDVI to make it look like came from NOAA-14.  Use of Hyperion data and simulated sensor response

- Heidinger (2005)

- 04/22/2005

## STOP

- 04/22/2005

## Slide 8

| Region | Time & Place of Drought Cases  | References |
| --- | --- | --- |
| North America
(USA) | 1985,  week 26, 30~40N, 102~93W, Great Plains
1987, wk 26, 34~40N, 84~75W, Virginia & North Carolina
1988, end of June - early July, week 26, 30~40N, 102~93W, Great Plains
1989, wk 19, 35~42N, 104~95W, Great Plains
1996, wk 13, 30~40N, 102~93W, Great Plains | Kogan (1995, 2001)
 |
| South America
(Argentina) | 1987, end-Dec, wk 51, 32~52S, 75~55W
1988, end-Dec, wk 51, 32~52S, 75~55W
1989, end-Dec, wk 51, 32~52S, 75~55W
1995, mid-Dec, wk 50, 32~52S, 75~55W
1996, end-Jan, wk 4, 32~52S, 75~55W
1997, mid-Dec, wk 50, 32~52S, 75~55W | Seiler et al. (2000), Kogan (2001)
 |
| Africa | 1991, mid-Dec, wk 51, 15~23S, 25~31E, Zimbarbwe | Kogan (1997) |
| Euro-Asia
(FSU) | 1986, end-July, wk 31, 40~55N, 45~80E
1991, mid-June, wk 24, 40~55N, 45~80E
1995, late-June, wk 26, 40~55, 45~80E
1996, late-June, wk 26, 40~55, 45~80E
1997, late-June, wk 26, 40~55, 45~80E
1998, late-June, wk 26, 40~55, 45~80E | Kogan (1997, 2001) |
| Asia 
(China) | 1992, wk 27, 34~36N, 116~117E, Shangdong
1992, end-July, wk 31, 21~40N, 108E-122E, Southeast | Kogan (1997, 2001) |

- 04/22/2005

## Results (for example)

- Regional mean SMN without fix

- a. USA Great Plains [30N,40N], [102W,93W], late-June, week 26, 1985, 1988

- Fixed: MaxMin Scaling

- Fixed: MeanStd Normalization

- Fixed: Linear Regression

- Fixed: Adjusting CDF

- Fixed: Mod MaxMin Scaling

**Results ****(for example)**

- 04/22/2005

## Summary of preliminary results

-  Mathematical/statistical based approaches are generally incapable of achieving conflict goals of a) preserving data consistency and b) enhance “true” level of signals.
-  “Max/Min Range Scaling” approach appears to be able to fix the range, but not the global trend indicated by weekly global mean NDVI;
-  “Normalization” and “Linear Regression” approaches appear to work well on fixing the global mean, but has problems of incurring abnormal NDVI range (indicated by maximum NDVI);
-  “Adjusting CDF” approach can both fix the global mean and slightly improve the range is NDVI, however with strict assumption requirement;
-  The limited case studies on local/regional drought detection after different fixes are not conclusive though limited results suggested that non of the approaches will change the local weekly time series drastically.

- 04/22/2005

## Current Production

- Fred’s algorithm for NOAA16
- 8% down

- NVI
- Fred’s algorithm for NOAA 17
- 8% down

- SMN-16

- SMN-17

- SMN-17new

- Adjusted by
- linear regression coefficients

*NOAA 16*

*NOAA 17*

- Period of processed Data:
- 2002 week 47 to 2004 week 15

**Current Production**

- 04/22/2005

## Recommendations

-  More comprehensive studies on local drought detection capability by different approaches;
- More detailed study on using these approaches with land surface classification, which increases the complexity but may be more reasonable.
-  Investigate the physically based approach by explicitly compensating for differences in sensor response functions with modeled/simulated standard response curve signals, and coming up with methods to adjust non-standard curve signals to standard (under certain assumptions).
-  Fixing methods oriented towards addressing error sources.
-  Funding to cover effort: MetOP GVPS project (April 2005 ~ April 2006)

- 04/22/2005