1.  **TITLE**

2.  **INVESTIGATOR(S)**

3.  **INTRODUCTION**

4.  **THEORY OF ALGORITHM/MEASUREMENTS**

5.  **EQUIPMENT**

6.  **PROCEDURE**

7.  **OBSERVATIONS**

8.  **DATA DESCRIPTION**

9.  **DATA MANIPULATIONS**

10. **ERRORS**

11. **NOTES**

12. **REFERENCES**

13. **DATA ACCESS**

14. **GLOSSARY OF ACRONYMS**

**1. TITLE**

**1.1 Data Set Identification**

Subsets extracted from Global Inventory Modeling and Mapping Studies
(GIMMS) Satellite Drift Corrected and NOAA-16 Incorporated Normalized
Difference Vegetation Index (NDVI), Bi-monthly, 1981--2003.

**1.2 Database and Database Table Name(s)**

Not applicable to this data set.

**1.3 File Name(s)**

The files in this data set are named using the following naming
convention:

###### AF03dec15a.n16-VIg {#af03dec15a.n16-vig .unnumbered}

where

AF identifies the continent

> AF = Africa
>
> AZ = Australia and New Zealand
>
> EA = Eurasia
>
> NA = North America
>
> SA = South America and Central America

03 is the two digit year, 1981 to 2003.

dec is abbreviated month from January to December.

10a identifies the composite period, 10a denotes the days 1-10 of the
month, 10b denotes days 11-20 of the month, and and 10c denotes the days
from 21 to the end of the month (28, 30 or 31).

n16 identifies the satellite, NOAA-7, 9, 11, 14 or 16 from which the
data

originated.

VIg identifies the data type, here NDVI.

The VIg 10-day maximum value composites begin in July 1, 1981 and end in
May 31, 2004. The images are 751 lines by 801 pixels in dimension. The
VIrg files begin in June 2004 and

continue to the present. These files differ from the VIg files in their
degree of processing and replacement of cloudy and missing pixels.

**1.4 Revision Date of this Document**

September 20, 2004.

**2. INVESTIGATOR(S)**

**2.1 Investigator's Name and Title**

Jorge E. Pinzon and Molly E. Brown

> Science Systems and Applications
>
> NASA-Goddard Space Flight Center
>
> Code 923
>
> Greenbelt, MD 20771

Compton J. Tucker

> NASA-Goddard Space Flight Center
>
> Code 923
>
> Greenbelt, MD 20771

**2.2 Title of Investigation**

Monitoring Seasonal and interannual variations in Land-Surface
Vegetation from 1981-2003 using GIMMS NDVI.

2.3 Contacts (For Data Production Information)

+-------------+---------------------------+---------------------------+
|             | **Contact 1**             | **Contact 2**             |
+-------------+---------------------------+---------------------------+
| **2.3.1     | Dr. Jorge E. Pinzon       | Dr. Molly E. Brown        |
| Name**      |                           |                           |
+-------------+---------------------------+---------------------------+
| **2.3.2     | NASA-Goddard Space Flight | NASA-Goddard Space Flight |
| Address**   | Center                    | Center                    |
|             |                           |                           |
| > *         | Code 923                  | Code 923                  |
| *City/St.** |                           |                           |
| >           | Greenbelt, MD             | Greenbelt, MD             |
| > **Zip     |                           |                           |
| > Code**    | 20771                     | 20771                     |
| >           |                           |                           |
| >           | USA                       | USA                       |
| **Country** |                           |                           |
+-------------+---------------------------+---------------------------+
| **2.3.3     | 301-614-6685              | 301-614-6616              |
| Tel. No.**  |                           |                           |
|             | 301-614-6015              | 301-614-6015              |
| **Fax No.** |                           |                           |
+-------------+---------------------------+---------------------------+
| **2.3.4     | [jorge_pinzo              | <m                        |
| E-mail**    | n@ssaihq.com]{.underline} | olly.brown@gsfc.nasa.gov> |
+-------------+---------------------------+---------------------------+

**2.4 Requested Form of Acknowledgment**

Please cite the following publications whenever these data are used:

Pinzon, J., M. E. Brown and C. J. Tucker. 2004. Satellite time series
correction of orbital drift artifacts using empirical mode
decomposition. In Hilbert-Huang Transform: Introduction and
Applications, eds. N. Huang, pp. Chapter 10, Part II. Applications (to
appear).

Pinzon, J. 2002. Using HHT to successfully uncouple seasonal and
interannual components in remotely sensed data. SCI 2002. Conference
Proceedings Jul 14-18. Orlando, Florida.

Tucker, C. J., J. E. Pinzon, M. E. Brown, D. Slayback, E. W. Pak, R.
Mahoney, E. Vermote and N. El Saleous. 2005. An Extended AVHRR 8-km NDVI
Data Set Compatible with MODIS and SPOT Vegetation NDVI Data.
International Journal of Remote Sensing (submitted).

**3.0 INTRODUCTION**

**3.1 Objective/Purpose**

The Global Inventory Modeling and Mapping Studies (GIMMS) normalized
difference vegetation index (NDVI) data sets were generated to provide a
22-year satellite record of monthly changes in terrestrial vegetation.
New features of this dataset include reduced NDVI variations arising
from calibration, view geometry, volcanic aerosols, and other effects
not related to actual vegetation change. In particular, NOAA-9
descending node data from September 1994 to January 1995, volcanic
stratospheric aerosol correction for 1982-1984 and 1991-1994, and
improved NDVI using empirical mode decomposition/reconstruction (EMD) to
minimize effects of orbital drift. Global NDVI was generated to provide
inputs for computing the time series of biophysical parameters contained
in the International Satellite Land Surface Climatology Project (ISLSCP)
Initiative II collection. NDVI is used in climate models and
biogeochemical models to calculate photosynthesis, the exchange of CO~2~
between the atmosphere and the land surface, land-surface
evapotranspiration and the absorption and release of energy by the land
surface.

**3.2 Summary of Parameters**

Composited, bi-monthly, normalized difference vegetation index over
global land area subsets. NDVI is the difference (in reflectance)
between the AVHRR near-infrared and visible bands divided by the sum of
these two bands (Tucker 1980; Sellers 1985; Sellers et al. 1994).

**3.3 Discussion**

Because NDVI is a ratio of differences between two adjacent bands, it is
largely *insensitive* to variations in illumination intensity. However,
NDVI ***is sensitive*** to effects that differ between bands. Band
calibrations, for example, have changed frequently between the five NOAA
AVHRR instruments that acquired the NDVI record for the 22-year
Initiative II period. In addition, natural variability in atmospheric
aerosols and column water vapor affect the NDVI record. Over the period
of record two major volcanic eruptions occurred, El Chichon in 1982 and
Mt. Pinatubo in 1991, injecting large quantities of aerosols into the
Earth's stratosphere. These aerosols, along with smoke from biomass
burning and dust from soil erosion and other factors, can introduce
significant variability in the AVHRR NDVI record. These constituents
have significantly different effects on AVHRR band's 1 and 2. The GIMMS
NDVI corrects for the known changes of the atmosphere from these two
volcanic eruptions, but reductions in the NDVI signal can still be seen
over densely vegetated tropical land covers for limited time periods.

NDVI is also sensitive to the periodic variations in solar illumination
angle and sensor view angles induced by the NOAA orbits. Its polar,
sun-synchronous orbits permitted daily coverage of each point on earth,
although at time-varying viewing and illumination geometry. The GIMMS
NDVI dataset has a satellite overpass time drift correction that largely
eliminates the variation of NDVI due to changes in solar zenith angle.

Frequent cloud cover can also create numerous gaps in the daily AVHRR
record eliminating roughly two-thirds of the data. In order to construct
cloud-free views of the Earth, *composite* images were constructed at
regular intervals by selecting pixels with the maximum NDVI. Choosing
pixels with maximum NDVI reduces cloud cover and water vapor effects
since both strongly reduce NDVI. Image compositing can be done over any
time interval, but 9-10 days is generally the minimum period selected
for maximum quality. The GIMMS data record constructed here is based on
15-day composites. There are two 15-day composites per month, the first
for days 1-15, and the second for day 16 to the end of the month.

**4. THEORY OF ALGORITHM/MEASUREMENTS**

Green leaves have a higher reflectance in the AVHRR near-infrared band
(band 2) than in the visible band (band 1), because of differences in
leaf chlorophyll absorption between the two bands. Chlorophyll absorbs
strongly in the red region, spanned by AVHRR band 1. Thus, the
difference in vegetation reflectance between the near-infrared and
visible bands increases with green leaf vegetation density, hence
chlorophyll concentration. The ratio of the difference between band 2
and band 1 and their sum, hence the NDVI, is an index that ranges
between -1 and +1, though the observed range is usually smaller.
Non-vegetated materials generally have a much lower NDVI (near 0) than
dense vegetation (\~0.7), since their reflectance in the near-infrared
and visible bands are more nearly equal.

**5. EQUIPMENT**

**5.1 Instrument Description**

The Advanced Very High Resolution Radiometer (AVHRR) acquired data in 5
spectral bands; one visible, one near infrared and three thermal bands,
all with 1024 quantizing levels. The thermal bands are not used in the
GIMMS NDVI data. The AVHRR produces imagery at 1.1 and 4 km spatial
resolution. The 4-km product or global area coverage (GAC) product is
derived from the 1-km product by onboard sampling. The 4-km product is
available globally from July 1981 until the present. The 1-km record is
not continuous. Its availability depends upon prior arrangements made by
NOAA, or on the proximity of a local receiving station that can capture
the data directly from the satellite. See NOAA--KLM users guide for
specifications of the newest NOAA AVHRR instruments.
<http://www2.ncdc.noaa.gov/docs/klm/>

> **5.1.1 Platform**
>
> The NOAA AVHRR satellite series 7, 9, 11, 14 and 16, used for this
> NDVI record, flew in sun-synchronous polar orbits with a nominal 1:30
> or 2:30 PM local daytime overpass time at launch. However, the
> overpass times drifted by 1-2 minutes per month to as much as 4 1/2
> hours later in the day, creating variable illumination and view angles
> over the period of record. The 55-degree sensor swath width permitted
> a daily view of each pixel on Earth, although at different
> illumination and view angles during the 9-day repeat cycle.
> Maximum-value NDVI data compositing tends to select pixels acquired in
> a near-nadir mode with minimum atmospheric effects. Even so, view,
> illumination and atmospheric effects remain. Reducing these effects
> was the aim of the GIMMS processing.
>
> The satellite platforms the data are derived from are noted below:
>
> Table 1. Instrument change times for the GIMMS dataset.

+---------------------+-----------------+-----------------------------+
| ### AVHRR inst      | #### Start      | ### End d                   |
| rument {#avhrr-inst | date {#start-da | ate {#end-date .unnumbered} |
| rument .unnumbered} | te .unnumbered} |                             |
+---------------------+-----------------+-----------------------------+
| NOAA-7              | > July 1, 1981  | February 8, 1985            |
+---------------------+-----------------+-----------------------------+
| NOAA-9              | > February 11,  | November 7, 1988            |
|                     | > 1985          |                             |
+---------------------+-----------------+-----------------------------+
| NOAA-11             | > November 11,  | September 19, 1994          |
|                     | > 1988          |                             |
+---------------------+-----------------+-----------------------------+
| NOAA-9 (descending) | > September 20, | January 18, 1995            |
|                     | > 1994          |                             |
+---------------------+-----------------+-----------------------------+
| NOAA-14             | > January 19,   | October 31, 2000            |
|                     | > 1995          |                             |
+---------------------+-----------------+-----------------------------+
| NOAA-16             | > November 1,   | December 31, 2003           |
|                     | > 2000          |                             |
+---------------------+-----------------+-----------------------------+
| NOAA-17             | > January 1,    | continuing                  |
|                     | > 2004          |                             |
+---------------------+-----------------+-----------------------------+

> **5.1.2 Mission Objectives**
>
> The NOAA AVHRR satellite sensor series was originally designed as a
> weather satellite. However from the early 1980s, AVHRR data has found
> increasing use to monitor the type and condition of land vegetation.
> AVHRR vegetation data archives extend back to August 1981.
>
> **5.1.3 Key Variables**
>
> The AVHRR measured top of the atmosphere radiance in 5 bands. Band 1
> covered the 0.5 to 0.7 um region, band 2 the 0.7 to 1.1 um region,
> with three thermal bands one covering the middle infrared region
> around 3 microns and two thermal infrared bands in the 10 to 11 micron
> region. The GIMMS NDVI product uses radiance in bands 1 and 2 and were
> mapped at the NASA Goddard Space Flight Center under the guidance of
> Dr. Compton Tucker. The current adjusted NDVI dataset was derived
> entirely from the NDVI produced during the 1998-2000 remapping effort
> of the GIMMS group.
>
> **5.1.4 Principles of Operation**
>
> The NOAA satellite series, NOAA 6, 7, 9, 11 and 14 were in polar,
> sun-synchronous orbits with nadir afternoon overpass times. NOAA 7
> data span the years 1981 -1985, NOAA 9, 1986-1989, NOAA 11, 1989-1995,
> NOAA 14, 1995-2000, and NOAA-16 from 2000 to 2004 (and continues
> through the present). Data from NOAA-9 was used from September 1994
> until January 1995 when NOAA 11 started to malfunction and its
> replacement, NOAA 13, failed shortly after launch. Each AVHRR sensor
> has different and variable calibration and overpass time.
>
> **5.1.5 Instrument Measurement Geometry**
>
> The AVHRR is a scanning, imaging radiometer, scanning ± 55 degrees,
> providing a 2800 km swath width. The orbital configuration permits
> daily coverage at a maximum spatial resolution of 1 km of each point
> on earth, although at different viewing and illumination geometries on
> subsequent days. The orbit repeats its ground track each 9 days.
>
> **5.1.6 Manufacturer of Instrument**
>
> See NOAA--KLM users guide for specifications of the newest NOAA AVHRR
> instrument. <http://www2.ncdc.noaa.gov/docs/klm/>

**5.2 Calibration**

NOAA provides preflight calibration coefficients that relate the
satellite-measured digital counts to reflectance values. The preflight
calibration coefficients for the visible and near infrared channel are
of the form

reflectance = gain\*(digital counts - offset)

The gain and the offset are determined on the ground prior to launch of
the satellite; The gain and offset are referred to as pre-flight
calibration coefficients. The preflight calibration coefficients change
for each satellite. In some cases preflight coefficients were updated
during the time of operation of a satellite.

Pre-flight calibration coefficients do not take into account the
degradation of the AVHRR during its time of operation. Several
techniques exist to correct for the change in sensitivity of the AVHRR.
For the GIMMS data, the coefficients by Vermote and Kaufman (1995) are
used to correct the visible and near infrared reflectance for in-flight
sensor degradation. In the GIMMS NDVI data the relative degradation
error in the gain is further reduced to below 1 % by using a desert
calibration of the ratio of channels 1 and 2 reflectance to invariant
Sahara Desert targets.

**5.2.1 Specifications**

> **5.2.1.1 Tolerance**
>
> See Vermote and Kaufman (1995) and Section 9.2.1 for more details.

**5.2.2 Frequency of Calibration**

> See Vermote and Kaufman (1995) and Section 9.2.1 for more details.
> (Vermote and Kaufman 1995)

**5.2.3 Other Calibration Information**

> See Rao and Chen (1995). (Rao and Chen 1995)

**6. PROCEDURE**

**6.1 Data Acquisition Methods**

The input data for the GIMMS Land data processing is the NOAA AVHRR GAC
1B data. Our GAC 1B data were obtained from NOAA and from NCAR sources
at the time of acquisition from 1985 to the present. We have augmented
this dataset with GAC 1B data available from NOAA's Satellite Active
Archive. The GIMMS NDVI datasets were corrected using solar zenith angle
values from the AVHRR sensor for the period of 1981-2003 (Pinzon 2002;
Pinzon et al. 2004; Tucker et al. 2005).

**6.2 Spatial Characteristics**

> **6.2.1 Spatial Coverage**

-   Before subset extraction, the coverage is global for all land areas
    except Greenland and Antarctica. Data in the files are ordered from
    North to south and from West to East beginning at 180 degrees West
    longitude and 90 degrees North latitude.

> **6.2.2 Spatial Resolution**
>
> The data are provided at an 8 km pixel spatial scale.

**6.3 Temporal Characteristics**

> **6.3.1 Temporal Coverage**
>
> The data are available from July 1981 through May 2004.
>
> **6.3.2 Temporal Resolution**
>
> Bi-monthly; the GIMMS dataset is composited at a nominal 15-day time
> step.

**7. OBSERVATIONS**

**7.1 Field Notes**

Not applicable to this dataset.

**8. DATA DESCRIPTION**

**8.1 Table Definition with Comments**

Not applicable to this dataset.

**8.2 Type of Data**

**8.2.1 Parameter/ Variable Name**

GIMMS Normalized Difference Vegetation Index, corrected for (see section
9.2.2 for details):

-   residual sensor degradation and sensor intercalibration differences;

-   distortions caused by persistent cloud cover globally;

-   solar zenith angle and viewing angle effects due to satellite drift;

-   volcanic aerosols;

-   missing data in the Northern Hemisphere during winter using
    interpolation due to high solar zenith angles;

-   low signal to noise ratios due to sub-pixel cloud contamination and
    water vapor.

**8.2.2 Parameter/ Variable Description**

NDVI is archived in signed 16-bit band-sequential integer files of the
entire period of record for each site. The image flags have been
imbedded into the one's place in the integer values. Good NDVI values
are four digit integers ending in zero, while flagged integers end in
non-zero values. In the formulas below, the data, once imported, is
referred to as \'raw\' data. To recover the -1 to 1 range of NDVI, use
the following formula:

NDVI = raw/10000 (where the insignificant 4^th^ decimal place can be
ignored)

or, to strip off the flag value, use:

NDVI = (raw/10000)/10

In the NDVI data,

Water pixels have raw values of -10000

Masked pixels have raw values of -5000

Missing pixels have raw values of --1994

The AVHRR post-processing has improved the quality of the AVHRR NDVI raw
data by removing clouds, reducing noise and removing artifacts due to
satellite drift. Corrected pixels have been flagged for the user to
provide information about what has been done to each pixel. The fourth
decimal place in NDVI can be ignored, as it was used for the flags only.
Good NDVI values are 'raw' four digit integers ending in zero.

The flag files can be retrieved from the NDVI data by the following
formula:

FLAG = raw - floor(raw/10) \* 10;

(where FLOOR(X) rounds the elements of X to the nearest integer).

Thus, users can use the formula to create a mask image of flagged pixels
to determine their spatial and frequency distribution as an aid to
analysis.

The meaning of the Page 8 of 22FLAG values:

> FLAG = 5 (NDVI retrieved from average seasonal profile, possibly snow)
>
> FLAG = 4 (NDVI retrieved from average seasonal profile)
>
> FLAG = 3 (NDVI retrieved from spline interpolation, possibly snow)
>
> FLAG = 2 (NDVI retrieved from spline interpolation)
>
> FLAG = 1 (Good value, possibly snow)

**8.2.3 Data Range**

NDVI has a theoretical range between -1 and 1; with values around 0 for
bare soil (little or no vegetation), and values of 0.7+ for dense
vegetation. Raw data range can be from --10000 to 10000.

**8.2.4 Units of Measurement**

Unitless

**8.2.5 Data Source**

AVHRR

**8.3 Sample Data Record**

Not applicable to this dataset.

**8.4 Data Format**

All of the files in this AVHRR NDVI dataset are in integer format (IEEE
standard - big endian). NDVI values are multiplied by 10000, water
pixels have a value of -10000, -5000 are masked pixels, and missing are
-2000 plus the flag 6.

The files are in the Albers Equal Area Projection. The README file
provided has the projection information, as well as a header file
appropriate for use with the ENVI software.

**8.5 Related Data Sets**

The 8 km GIMMS Land Surface AVHRR data set (Tucker et al. 2005) is
available at its original spatial resolution directly from the GIMMS
group. Please contact Dr. Tucker for this dataset. LAI and FPAR fields
at quarter degree resolution based on the GIMMS data are also available.

**9. DATA MANIPULATIONS**

**9.1 Formulas**

> **9.1.1 Derivation Techniques/Algorithms**
>
> See Section 9.2.1.

**9.2 Data Processing Sequence**

> **9.2.1 Processing Steps and Data Sets**

The original 4.62 km native resolution GAC data has been mapped to 8km
in the. GIMMS Land Surface AVHRR data set (Tucker et al. 2005). The
contents of this dataset are:

-   NDVI

-   solar zenith angle

-   Temperature Channel 4

-   Temperature Channel 5

The data set can be obtained from University of Maryland's Global Land
Cover Facility (GLCF) and the ISCLCP II initiative web sites.

### 9.2.2 8-km NDVI Data Processing {#km-ndvi-data-processing .unnumbered}

> Satellite data in NOAA level-1b format (Kidwell 1998) were ingested
> from magnetic media, were forward mapped to the output bin closest to
> the center location of each 8 km equal area grid cell, respective
> calibration values were applied to each channel (Vermote and Kaufman
> 1995), and the other values associated with the maximum NDVI were
> retained for each compositing period. Data from seven AVHRR
> instruments on different satellite platforms were used in this dataset
> (see Table 1, 5.1.1).
>
> We used a similar navigation procedure to that of El Saleous *et al.*
> (2000), which in turn is based upon the work of Baldwin and Emery
> (1995). The orbital model predicts the position of the satellite at
> any time, using the satellite onboard clock and orbital elements to
> determine the predicted position. From this perspective, the
> sun-target-sensor geometry is determined. The NOAA satellites'
> velocity with respect to the Earth's surface is \~7 km/second and the
> output bin size we employed was 8 km. This translates into a timing
> error of \~1.0 second to achieve a navigation accuracy of less than
> or equal to 1 pixel. The majority of our navigation errors were due to
> errors of the onboard spacecraft clock. (El Saleous et al. 2000,
> Baldwin and Emory 1995)
>
> Every composite image was manually checked for navigation accuracy by
> comparing the mapped data to a reference coastline for the continent
> in question. Images with \> 1 pixel navigation errors were
> investigated and the day(s) of the navigation error identified. These
> images were subsequently reprocessed separately and manually
> registered to the reference data, to bring the badly navigated day(s)
> into agreement with the reference coastline. The the composite image
> was reconstructed by maximum value NDVI compositing. In a few cases it
> was necessary to discard data from specific days, as the navigation
> errors were impossible to adjust.
>
> With the failure of NOAA-13 to achieve orbit in 1992, NOAA-11
> continued to provide global afternoon/early morning AVHRR data. By
> 1994, the afternoon equatorial overpass time for NOAA-11 was \~17:00
> hours. We began using NOAA-9 descending node AVHRR data for our global
> NDVI data set in September 1994, and continued using these data until
> NOAA-14 became operational in late January 1995.[^1] There is thus a
> fundamental difference between the Pathfinder AVHRR Land (PAL) and the
> Global Vegetation Index (GVI) data sets and the data set we describe
> herein: We use NOAA-9 AVHRR data from October 1994 into January 1995
> when there were no NOAA-11 AVHRR data (it had failed), and use NOAA-9
> AVHRR data in September 1994 when the PAL and GVI data sets use
> NOAA-11 data.

### 9.2.2.1 Radiometric calibration {#radiometric-calibration .unnumbered}

> Satellite determination of long-term surface trends requires precision
> within and among various space-borne instruments. It is also crucial
> to document the within- and among-sensor calibration uncertainty to
> determine the accuracy with which surface trends over time can be
> ascertained. The NDVI data we describe were processed in two ways: (1)
> NOAA-7 through NOAA-14 channel 1 and 2 data were processed using the
> Vermote and Kaufman (1995) channel 1 and channel 2 calibration. The
> resulting NDVI fields were further adjusted using the desert
> calibration technique from Los (1998), then decomposed and
> reconstructed using empirical mode decomposition to correct for solar
> zenith angle effects (Pinzon 2002); and (2) Data from NOAA-16 were
> processed using the preflight channel 1 and channel 2 calibration
> values and formed into maximum values composites. An empirical mode
> decomposition and reconstruction was performed to insure a slope with
> respect to time of 0.00 in desert areas, this was also used to correct
> solar zenith angle artifacts. This NOAA-16 NDVI time series was
> adjusted by a constant offset to match up with a temporally-coincident
> and spatially-aggregated 8-km SPOT Vegetation NDVI time series, which
> had previously been adjusted to match up with the corresponding
> temporally-coincident NOAA-14 NDVI time series. We thus used
> overlapping SPOT Vegetation NDVI time series as the means to
> intercalibrate or tie together the NOAA-14 and NOAA-16 NDVI time
> series (Pinzon et al. 2004). This was necessary because the bilinear
> gain for channel 1 and channel 2 of NOAA-16's AVHRR instrument
> complicates *ex post* *facto* calibration. Thermal calibration
> coefficients after Weinrab *et* *al.* (1990) were used for the
> calibration of all thermal channels. (Weinreb et al. 1990)

### 9.2.2.2 Atmospheric Correction and Cloud Screening {#atmospheric-correction-and-cloud-screening .unnumbered}

> We choose to produce a maximum value NDVI composite data set, and
> associated layers, without any atmospheric correction, except during
> the El Chichon and Mt. Pinatubo volcanic stratospheric aerosol
> periods. A stratospheric aerosol correction was applied as proposed by
> Vermote *et al.* (1997) from April 1982 through December 1984 and from
> June 1991 through December 1994. We formed composite stratospheric
> aerosol optical depth fields by combining the work of Sato *et al.*
> (1993), and Vermote *et al.* (1997). Rosen *et al.* (1994), Russel et
> al. (1993) and Dutton (1994) were used to compare specific optical
> depth measurements to our blended global fields. We produced optical
> depth fields that varied by month and degree of latitude.
>
> Cloud screening was provided by a channel 5 thermal mask of 0^o^ C for
> all continents except Africa, where a cloud mask of 10 ^o^ C was used.
> In addition, the bimonthly composites significantly reduced cloud
> contamination and the channel 4 and channel 5 thermal values are
> available for *ex post facto* cloud screening if desired.
>
> **9.2.2.3 Satellite drift correction using Pinzon et al (2002)
> method**
>
> The GIMMS group uses Empirical Mode Decomposition (EMD) to identify
> and remove parts of the NDVI signal that are most related to the
> satellite drift (Pinzon 2002; Pinzon et al. 2004). The EMD technique
> was introduced by Nordon Huang in 1998 as an alternative to standard
> decomposition techniques for representation of nonlinear and
> non-stationary data that show clear physical scales or frequency
> content. Unlike Fourier decomposition (Wilks 1995; Trefethen and Bau
> 1997), the EMD a basis for the signal from the data itself. The EMD is
> empirical, intuitive, direct, *a posteriori*, and adaptive, with the
> decomposition functions based on and derived from the data (Huang et
> al. 1998; Huang et al. 1999). Pinzon et al. (2001) showed that EMD was
> applicable to NDVI time series from the AVHRR sensor to isolate
> orbital drift effects from the NDVI signal.(Pinzon et al. 2001)
>
> Orbital drift results in later equatorial crossing times for the NOAA
> satellites and results in changes of illumination that affect the
> NDVI. In this dataset, we identify the trends in the NDVI that
> associated with changes in sun-target-sensor geometry due to satellite
> drift. Figure 3 shows the effects of satellite drift on the Solar
> Zenith Angle (SZA) at three latitude bands, 75-35N, 35N-35S and
> 35S-55S. An increasing trend is observed in each satellite due to its
> delay in the equatorial crossing time. This trend, superimposed on
> each plot, is more pronounced at lower latitude, whereas seasonal
> variations dominate at higher latitudes. Note that equatorial
> latitudes have an extra oscillation due to solar nadir moving past the
> target latitude, causing and increase in SZA at six month intervals
> rather than at yearly intervals (Privette et al. 1995).
>
> The EMD is used to extract NDVI trends that may be caused by the
> satellite orbital drift, reducing the interference of other components
> in the NDVI signal. The EMD is used to isolate the components of the
> NDVI signal that are related to SZA trends and remove them from the
> corrected NDVI (see Figures 2 and 3). The technique removes NDVI
> trends that are more than 80% correlated to the SZA trends shown in
> Figure 2. Areas with trends that have a lower correlation are not
> corrected. Figure 3 shows both cases: trend removal from NDVI signal
> due to high correlation with the satellite drift, and trends that have
> not been altered because of low correlation to satellite drift.

Figure 4 shows areas where the correction is the strongest, notably 1)
the tropics are most affected by satellite drift due to the SZA trend
magnitudes, and 2) the high northern latitude and regions with low
vegetation biomes are less contaminated since the SZA component
represents a small part of the NDVI signal (see Figure 2). The
correction is performed by-pixel. After the satellite drift correction,
a kriging interpolation removes noise and attenuates the effect of
cloudy and missing pixels. The original 8-km AVHRR data is then averaged
into 1 degree, 0.5 and 0.25 degree datasets provided here.

> **9.2.3.4 Intercalibration of NDVI from all instruments using
> narrow-band instruments such as MODIS and SPOT-Vegetation**

In order to include the data from NOAA-16, an intercalibration of the
single-gain satellites NOAA-7-14 (historical data) and the dual-gain
NOAA-16 with data from SPOT-Vegetation global data at 1 km (Achard et
al., 1992). SPOT data was averaged from 1 km to 8 km globally and then
decomposed using the EMD method. The interannual trend from three years
of data, 20 months overlapping with NOAA-14 and the rest with NOAA-16,
was extracted. This trend was determined to be invariant through time
over the period examined. A similar trend was extracted from the same
period of NOAA-14. A non-linear regression was performed to establish
coefficients that transform the historical data into the same range as
that of the current and upcoming suite of visible and NIR sensors, such
as MODIS, SPOT-Vegetation and others (Morisette et al., 2004). A similar
regression was performed for the NOAA-16 data. Once the trends from the
historical and NOAA-16 data were transformed into the common range, the
data was reconstructed and a consistent time series was established.

> ![](media/image1.wmf){width="5.934027777777778in"
> height="7.904861111111111in"}
>
> **Figure 2.** Solar Zenith Angles and trends derived from the
> NOAA-AVHRR sensor, averaged by latitude bands 75N-35N, 35N-5S, 35S-55S
>
> ![](media/image2.png){width="3.0125in" height="3.375in"}A. B.
>
> **Figure 3.** Example of the use of EMD technique and the removal of
> SZA-correlated trends. A. Before satellite drift correction (series at
> top), trend removed (middle) and resulting series (bottom). B. A pixel
> that is not corrected due to lack of correlation with the SZA trends.
>
> ![](media/image4.png){width="4.63125in" height="3.3618055555555557in"}
>
> **Figure 4.** Map displaying continental regions where satellite drift
> has an important contribution to the signal.

### 8-km NDVI Near-Real-Time Data Processing

Real time data processing differs from the historical processing in the
replacement of missing and cloudy pixels with valid NDVI values. To
create the real-time data images, we use a ratio and an offset by pixel
that is static. These two files were created from the entire time series
and are very stable. Changes in the solar zenith angles through time
will affect these static files, however, necessitating a solar zenith
angle correction for the real-time data to remove these effects. Real
time data can be created for NOAA-16 and 17 due to the dramatic
reduction in satellite drift through time achieved by NOAA for these
satellites (Kaufmann et al., 2000).

**9.3 Calculations**

> **9.3.1 Special Corrections/Adjustments**
>
> None other than those described in 9.1 and 9.2 above.

**9.4 Graphs and Plots**

See text above, Tucker et al. (2003), Pinzon et al. (2003) for some
graphs and plots of the data.

**10. ERRORS**

**10.1 Sources of Error**

Some sources of error in the NDVI data set are not accounted for with
the GIMMS corrections. These errors are caused by soil background
reflectance; this affects low NDVI values, but does not affect high NDVI
values. Thus similar low NDVI values may indicate different amounts of
vegetation. However, the EMD noise removal diminishes it.

**10.2 Quality Assessment**

> **10.2.1 Data Validation by Source**
>
> Earlier versions of the GIMMS-NDVI data have been used in various
> models and seem to capture general patterns of vegetation well. Tests
> on sites showed reasonable agreement of interannual variation in GIMMS
> NDVI and other measures of vegetation (Davenport and Nicholson 1993;
> Malmstrom et al. 1997; D\'Arrigo et al. 2000). These tests show that
> the variation in GIMMS NDVI is realistic on specific sites. Comparison
> between climate signals and GIMMS NDVI show realistic patterns in many
> semi-arid regions and temperate regions.
>
> Recent independent research has verified the quality of the NDVI data:

-   Lotsch et al (2003a) using the new GIMMS NDVI presented here and
    global standardized precipitation index (SPI) data revealed
    geographically extensive patterns of joint NDVI-SPI variability
    (Lotsch et al. 2003).

-   Jia et al (2003) analyzed the GIMMS NDVI data for three bioclimate
    subzones in northern Alaska and confirmed a long-term trend of
    increase in vegetation greenness for the Alaskan tundra that has
    been detected globally for the northern latitudes. There was a 16.9%
    (±5.6%) increase in peak vegetation greenness across the region that
    corresponded to simultaneous increases in temperatures (Jiaet al.
    2003).

-   In the Amazon basin, a recent publication by Poveda and
    Salazar (2004) showed that at interannual timescales, the NDVI shows
    a reaction to both phases of ENSO. Dryness was seen due to El Nino,
    whereas NDVI spatial variability is enhanced during La Nina (Poveda
    and Salazar 2004).

-   Lotsch et al (2003b) demonstrated that seasonal GIMMS-NDVI anomalies
    for Africa, spatial independent component analysis (ICA) reveals
    coherent regions that capture high interannual variability in arid
    and semiarid areas of eastern and southern Africa that were
    associated with fluctuations in sea surface temperature anomalies
    (SSTA). When using other AVHRR datasets (here, PAL) known to be
    contaminated with volcanic aerosols and sensor transition
    discontinuities, Lotsch et al (2003b) needed to apply ICA correction
    to account for these artifacts. When using these other datasets,
    results must be treated with caution (Lotsch et al. 2003).

-   Nemani et al (2002) found a 6% increase in NPP using the GIMMS NDVI
    in their primary production model corresponding with known
    corresponding with changes in CO~2~ concentrations 42% of the
    increase was from tropical forests due to decreases in cloud cover.
    Their satellite-based estimates of NPP show significant growth
    stimulation in both the tropics and the northern high-latitude
    ecosystems. Assuming that carbon emissions, including those from
    biomass burning and land-use changes, are properly accounted for in
    the atmospheric inversions, this spatial discrepancy means that
    respiration as well as NPP is a major driver of terrestrial
    carbon-sink dynamics (Nemani et al. 2002).

#####  {#section .unnumbered}

> **10.2.2 Confidence Level/Accuracy Judgment**
>
> The GIMMS-NDVI data set is believed to give large improvements over
> the Pathfinder NDVI data set, especially for areas with persistent
> cloud cover and for needle
>
> bearing evergreen vegetation during winter. GIMMS-NDVI has reduced the
> effects of orbital drift, which are especially large near the end of
> the time of operation a satellite. Corrections for atmospheric
> aerosols are likely to be too small from about one to three months
> after an eruption (May-Jul 1982 for El Chichon and Jul-Sep 1991 for
> Mt. Pinatubo), where the aerosols have not mixed evenly into the
> atmosphere (Brown et al. 2003).
>
> **10.2.3 Measurement Error for Parameters and Variables**
>
> None given.
>
> **10.2.4 Additional Quality Assessment Applied**
>
> NDVI data are increasingly being used to assess interannual variations
> in the biosphere; we would like to distinguish between the following
> types of temporal analysis (Gutman et al. 1995):

-   seasonal analysis: NDVI data should have more than sufficient
    accuracy to assess seasonality of vegetation with great confidence.
    The one exception may be the small seasonal cycles observed in the
    Sahara, this seasonality is most likely caused by variations in
    atmospheric water vapor, not by variations in vegetation.

-   interannual analysis: NDVI data have been used to look at
    year-to-year variations in desert margins (Tucker et al. 1991) and
    to variations associated with climate oscillations (Myneni et al.
    1995). The interannual signal in semi-arid regions and temperate
    regions is in general larger than the residual errors in the data.

-   trend analysis: This is in general the smallest interannual signal
    in the data; errors in the NDVI data are often as large or larger
    than the magnitude of trends, at this stage the magnitude of trends
    in NDVI data is uncertain within an order of magnitude.

**11. NOTES**

**11.1 Known Problems with the Data**

The decline in NDVI in mid-1991 was due to the Pinatubo eruption and
subsequent cooling and may not be related to actual declines in
vegetation. Trends in tropical regions are affected by this decline and
should therefore be treated with caution. The high northern latitude
(north of 65 degrees) corrections have been made for extremely high
solar zenith angles during the winter months, and these values should
thus be treated with caution.

**11.2 Usage Guidance**

GIMMS NDVI presents generalized patterns that may result in poor
representations of a specific locale; quantitative conclusions should be
drawn with caution. Nevertheless, NDVI should provide a large
improvement over previously used NDVI data sets, because the data are
collected by one series of instruments, and they give a more realistic
representation of the spatial and temporal variability of vegetation
patterns over the globe. Users of the data are strongly encouraged to
validate their results using independent data.

**11.3 Other Relevant Information**

Not available at this revision.

**12. REFERENCES**

**12.1 Satellite/Instrument/Data Processing Documentation**

Kidwell, K. B. (1998). Polar Orbiter Data Users\' Guide (TIROS-N,
NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, NOAA-14),
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Kidwell, K. B. (2000). NOAA KLM User\'s Guide, National Oceanic and
Atmospheric Administration .<http://www2.ncdc.noaa.gov/docs/klm/>.

Pinzon, J., M. E. Brown, et al. (2004). Satellite time series correction
of orbital drift artifacts using empirical mode decomposition.
[Hilbert-Huang Transform: Introduction and Applications]{.underline}. N.
Huang**:** Chapter 10, Part II. Applications.

Tucker, C. J., J. E. Pinzon, et al. (2005). \"An Extended AVHRR 8-km
NDVI Data Set Compatible with MODIS and SPOT Vegetation NDVI Data.\"
[International Journal of Remote Sensing]{.underline} **submitted**.

**12.2 Journal Articles and Study Reports.**

Achard, F., J.-P. Malingreau, et al. (1992). "A mission for global
monitoring of the continental biosphere: the 'Vegetation' Instrument on
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Baldwin, D. and W. J. Emory (1995). \"Spacecraft altitude variations in
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Brown, M. E., J. E. Pinzon, et al. (2003). Quantitative Comparison of
Four AVHRR Global Data Sets for Land Applications. Greenbelt, MD, Global
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D\'Arrigo, R. D., C. M. Malmstrom, et al. (2000). \"Correlation between
maximum latewood density of annual tree rings and NDVI based estimates
of forest productivity.\" [International Journal of Remote
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Davenport, M. L. and S. E. Nicholson (1993). \"On the Relation between
Rainfall and the Normalized Difference Vegetation Index for Diverse
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Dutton, E. (1994). 'Aerosol optical depth measurements from four
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global biospheric record from the Advanced Very High Resolution
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Emery, W. J., J. Brown, et al. (1989). \"AVHRR Image Navigation: Summary
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Gutman, G., D. Tarpley, et al. (1995). \"The enhanced NOAA Global land
dataset from the Advanced Very High Resolution Radiometer.\" [Bulletin
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Huang, N. E., Z. Shen, et al. (1999). \"A new view of nonlinear water
waves: the Hilbert spectrum.\" [Annual Review of Fluid
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Huang, N. E., Z. Shen, et al. (1998). \"The empirical mode decomposition
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Alaska, 1981-2001.\" [Geophysical Research Letters]{.underline}
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Kaufmann, R.K. et al., 2000. Effect of orbital drift and sensor changes
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Los, S. O. (1998). \"Estimation of the Ratio of Sensor Degradation
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Lotsch, A., M. A. Friedl, et al. (2003). \"Coupled
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Lotsch, A., M. A. Friedl, et al. (2003). \"Spatio-Temporal Deconvolution
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Malmstrom, C. M., M. V. Thompson, et al. (1997). \"Interannual variation
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[Global Biogeochemical Cycles]{.underline} **11**(3): 367-392.

Morisette, J.T., Pinzon, J.E., Brown, M.E., Tucker, C.J. and Justice,
C.O., 2004. "Initial validation of NDVI time series from AVHRR,
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Nemani, R., M. White, et al. (2002). \"Recent trends in hydrologic
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Pinzon, J. (2002). "[Using HHT to successfully uncouple seasonal and
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NDVI Data Set Compatible with MODIS and SPOT Vegetation NDVI Data.\"
[International Journal of Remote Sensing]{.underline} **submitted**.

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Stratospheric Aerosol Perturbing Effect on the Remote Sensing of
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an Introduction]{.underline}." San Diego, Academic Press.

**13. DATA ACCESS**

**13.1 Contacts for Archive/Data Access Information**

These subsets are available via the EOS Land Validation web site.

<http://landval.gsfc.nasa.gov/MODIS/>, under the 'Core Sites' link.

The continental NDVI data set from which these data were extracted are
available from the University of Maryland's, GLCF
(<http://glcf.umiacs.umd.edu/index.shtml>).

The GIMMS Land Surface AVHRR data set (Tucker et al. 2005) can be
obtained from the GLCF and the ISCLCP II Initiative web sites.

**14. GLOSSARY OF ACRONYMS**

AVHRR Advanced Very High Resolution Radiometer

BRDF Bidirectional Reflectance Distribution Function

DAAC Distributed Active Archive Center

DVD Digital Video Disk

EMD Empirical Mode Decomposition

GAC Global Area Coverage

LAC Local Area Coverage

GIMMS Global Inventory Monitoring and Modeling Studies

GLCF Global Land Cover Facility

GSFC Goddard Space Flight Center

GVI Global Vegetation Index

ICA Independent Component Analysis

ISLSCP International Satellite Land Surface Climatology Project

NASA National Aeronautics and Space Administration

NDVI Normalized Difference Vegetation Index

NOAA National Oceanographic and Atmospheric Administration

PAL Pathfinder AVHRR Land

RMS Root Mean Square Error

SR Simple Ratio

SZA Solar Zenith Angle

[^1]: NOAA-9 had a daytime descending node equatorial crossing time of
    \~09:00 hours in mid-1994 to early 1995. It had \"rocked around the
    clock\", with a \~2-3 minute/month later time procession from its
    original day time descending node equatorial crossing time of 02:30
    hours.