NDVI.DOC
1. TITLE
1.1 Data Set Identification.
Normalized difference vegetation index (NDVI)
NDVI (Monthly ; NASA/GSFC)
1.2 Data Base Table Name.
Not applicable.
1.3 CD-ROM File Name.
\DATA\VEGETATN\NDVI\YyyMmm.sfx
Note: capital letters indicate fixed values that appear on the CD-ROM
exactly as shown here, lower case indicates characters (values) that
change for each path and file.
The format used for the filenames is: YyyMmm.sfx, where yy is the last
two digits of the year (e.g., Y87=1987), and mm is the month of the year
(e.g., M12=December). The filename extension (.sfx), identifies the data
set content for the file (see Section 8.2) and is equal to .NDV for this
data set.
1.4 Revision Date Of This Document.
April 5, 1995.
2. INVESTIGATOR(S)
2.1 Investigator(s) Name And Title.
Sietse O. Los, Compton J. Tucker, Chris O. Justice
Biospheric Sciences Branch
NASA/Goddard Space Flight Center
Greenbelt MD
2.2 Title Of Investigation.
Earth Observing System - Inter-Disciplinary Science
project (Sellers - Mooney).
2.3 Contacts (For Data Production Information).
________________________________________________
| Contact 1 |
______________|_________________________________|
2.3.1 Name |Sietse O. Los |
2.3.2 Address |Biospheric Sciences Branch |
|code 923 |
|NASA Goddard Space Flight Center |
City/St.|Greenbelt MD |
Zip Code|20771 |
2.3.3 Fax |(301) 286-1757 |
2.3.4 Email |Sietse@Jello.gsfc.nasa.gov |
______________|_________________________________|
NOTE: Providing information on these data is not part of my daily routine;
Please read literature and descriptions first. Allow for some delay in
my response.
**********************************************************
* FOR GENERAL QUESTIONS REGARDING THE DATA CONTACT THE *
* GODDARD DAAC (SEE SECTION 13). *
**********************************************************
2.4 Requested Form of Acknowledgment
Please cite the following publication when ever these data are used:
Los, S.O., C.O. Justice, C.J. Tucker, 1994. A global 1 by 1 degree NDVI
data set for climate studies derived from the GIMMS continental NDVI
data. International Journal of Remote Sensing, 15(17):3493-3518.
ACKNOWLEDGMENTS
This research was funded by the NASA Earth Observing System Inter
disciplinary science (EOS-IDS) program, Sellers-Mooney team (contract
NAS-531732). Computer code that aided in processing of the global data
set was developed by Hong Zhang. The NDVI continental data sets could not
have been compiled without the contributions of the members of the GIMMS
group, in particular of Wayne Newcomb, Dave and John Rosenfelder and Bob
Rank. Their efforts are gratefully acknowledged.
3. INTRODUCTION
3.1 Objective/Purpose
The 1 X 1 degree monthly normalized difference vegetation index (NDVI)
data sets for 1987 - 1988 were compiled from the 8 km global area
coverage (GAC) continental NDVI data sets that have been processed by the
global inventory monitoring and modeling studies (GIMMS) group at NASA-
Goddard Space Flight Center. These data sets were created for the study
of vegetation dynamics around the globe. The data set described here is
the basis for a corrected version of the NDVI data (see FASIR-NDVI data
sets) that is used to calculate the global distribution of biophysical
parameters: fraction of photosynthetic active radiation absorbed by the
green part of vegetation (FPAR), leaf area index (LAI), green fraction of
vegetation (Greenness), roughness length and albedo. These data sets are
also held on this CD-ROM.
3.2 Summary of Parameters
NDVI: Normalized Difference Vegetation Index calculated from AVHRR
channel 1 and 2 radiance data, normalized for incoming solar radiation
in the respective bands: NDVI = (L2 - L1)/(L2 + L1).
(Note: this is equivalent to calculating the NDVI from apparent
reflectance values, which are uncorrected for Rayleigh scattering, ozone,
aerosols and water vapor in the atmosphere.)
3.3 Discussion.
Developments in the study of the global climate have led to the
incorporation of biosphere models into General Circulation Models of the
atmosphere (GCMs). These biosphere models differed from prior efforts in
providing a more realistic treatment of the water, energy, and carbon
exchanges between the land surface and the lower boundary layer of the
atmosphere (Sato et al., 1989). With the development of these models, a
need emerged for data sets from which the global distribution of
biophysical parameters could be derived. To obtain these data sets,
Dorman and Sellers (1989) used lookup tables to assign biophysical
parameters to global land cover classifications.
A better way to obtain global distributions of biophysical parameters is
by means of satellite data. Solar radiation in the visible and near-
infrared wavebands, reflected by the Earth's surface and collected by a
remote sensing device, can be combined into a spectral vegetation index
such as the normalized difference vegetation index (NDVI) and related to
physical properties of vegetation. In an updated version of SiB (Sellers
et al., 1995a), named SiB2, these relationships are employed to
calculate, with monthly NDVI data as the principal input, monthly values
of FPAR, leaf area index, land surface albedo and roughness
length. (Sellers et al. 1992, Sellers et al., 1995b).
To obtain 1 X 1 degree global fields of NDVI, monthly NDVI data sets
with a spatial resolution of around 5 to 8 km were used that had been
compiled by continent for the period 1982 to 1990 by the Global Inventory
Monitoring and Modeling Studies (GIMMS) group, at NASA Goddard Space
Flight Center. The continental GIMMS NDVI data sets were calculated from
Global Area Coverage (GAC) data collected at daily intervals by the
Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA 9 and
11 satellites.
The global 1 X 1 degree NDVI data set was produced as the first step in
the calculation of land surface parameters for use within a General
Circulation Model of the atmosphere (GCM). An evaluation of the NDVI
data is given in Los et al (1994). Errors or inconsistencies in the data
are defined as deviations from measurements obtained under "standard
conditions" (i.e., conditions at the top-of-the-atmosphere with no
clouds, clear atmosphere, near-nadir viewing angles, overhead sun, and
invariant soil background). For example, a cloud-contaminated pixel is
referred to as erroneous, since it does not directly relate to the
condition of the vegetation at the land surface. We further assume that
a value represents conditions for the middle of the month, whereas in
reality a bias may be introduced by compositing towards a "greener" part
of a month. Given the enormous range of conditions under which data are
collected and the diverse ways the data can be affected, the error
estimates provided in section 10 of this document serve only as an
indication of their magnitude.
4. THEORY OF MEASUREMENTS
The Advanced Very High Resolution Radiometer (AVHRR) is a four- or five-
channel scanning radiometer capable of providing global daytime and nighttime
information about ice, snow, vegetation, clouds and the sea surface. These
data are obtained on a daily basis primarily for use in weather analysis and
forecasting, however, a variety of other applications are possible, amongst
which monitoring of vegetation dynamics is one of the most important. The
GIMMS group has processed and archived AVHRR data that were collected onboard
the NOAA-7, NOAA-9, and NOAA-11 polar orbiting platforms. The radiometers
measured emitted and reflected radiation in one visible, one near and one
middle infrared, and two thermal channels. For vegetation monitoring a
normalized vegetation index is calculated from the visible and near-infrared
bands. The vegetation index exploits the property of green, vigorous
vegetation to reflect strongly in the infrared band, whereas at the same time
strongly absorbing in the red band. The spectral regions, band widths and
primary use of each channel are given in the following table:
Channel Wavelength [micro- Primary Use
meters]
------- ------------------- ---------------------------------
1 0.58 - 0.68 Daytime Cloud and Surface Mapping
2 0.725 - 1.10 Surface Water Delineation, Vegetation
Cover
3 3.55 - 3.93 Sea Surface Temperature (SST),
Nighttime Cloud Mapping
4* 10.5 - 11.5 Surface Temperature, Day/Night Cloud
Mapping
5 11.5 - 12.5 Surface Temperature
* For NOAA-7 and 9 Channel 4 was 10.3 - 11.3 micrometers.
The wavelength range at 50% Relative Spectral Response (in micrometers)
of the bands for each platform are :
Band NOAA-9 NOAA-11
------ --------------- ---------------
1 0.570 - 0.699 0.572 - 0.698
2 0.714 - 0.983 0.716 - 0.985
3 3.525 - 3.931 3.536 - 3.935
4 10.334 - 11.252 10.338 - 11.287
5 11.395 - 12.342 11.408 - 12.386
5. EQUIPMENT
5.1 Instrument Description.
The Advanced Very High Resolution Radiometer (AVHRR) is a cross-track
scanning system featuring one visible, one near infrared, one middle
infrared, and two thermal infrared channels. For the GIMMS NDVI product,
channels 1 and 2 are used for the calculation of the vegetation index and
channel 5 is used to identify and screen for clouds.
5.1.1 Platform.
AVHRR data used for this data set were collected onboard the
NOAA-9 and NOAA-11 polar orbiting platforms. The NOAA-9 and NOAA-
11 are afternoon pass satellites with northbound Equatorial
crossings directly after launch of 1420 and 1340 LST,
respectively. During the time of operation of the satellite the
equatorial crossing time gradually shifted to a later time in the
afternoon
5.1.2 Mission Objectives.
The AVHRR is designed for multi-spectral analysis of meteorol-
ogic, oceanographic, and hydrologic parameters. The objective of
the instrument is to provide radiance data for investigation of
clouds, land-water boundaries, snow and ice extent, ice or snow
melt inception, day and night cloud distribution, temperatures
of radiating surfaces, and sea surface temperature. It is an
integral member of the payload on the advanced TIROS-N space-
craft and its successors in the NOAA series, and as such contri-
butes data required to meet a number of operational and research-
oriented meteorological objectives. Although not its primary
purpose, the AVHRR was found to be suitable for vegetation
monitoring studies, in part because of its high temporal
resolution and global coverage.
5.1.3 Key Variables.
Emitted radiation.
Reflected radiation (Only reflected radiation used for NDVI)
5.1.4 Principles of Operation.
The AVHRR is a four-channel or five-channel scanning radio-
meter which detects emitted and reflected radiation from the
Earth in the visible, near-infrared and far-infrared regions of
the spectrum. Scanning is provided by an elliptical beryllium
mirror rotating at 360 rpm about an axis parallel to the Earth. A
two-stage radiant cooler is used to maintain a constant
temperature for the IR detectors of 95 K. The operating
temperature is selectable at either 105 or 110 K. The telescope is
an 8-inch afocal, all-reflective Cassegrain system. Polarization
is less than 10 percent. Instrument operation is controlled by 26
commands and monitored by 20 analog housekeeping parameters.
5.1.5 Instrument Measurement Geometry.
The AVHRR is a cross-track scanning system. The instantaneous
field-of-view (IFOV) of each sensor is approximately 1.4
milliradians giving a resolution of 1.1 km at the satellite
subpoint. There is about a 36 percent overlap between IFOVs
(1.362 samples per IFOV). The scanning rate of the AVHRR is six
scans per second, and each scan spans an angle of +/- 55.4
degrees from the nadir. The global area coverage data are
resampled from the GAC by taking the average of 4 by 1 pixels out
of the first row of a 5 by 3 window.
5.1.6 Manufacturer of Instrument.
International Telegraph and Telephone (ITT).
5.2 Calibration.
NOAA provides calibration parameters on tape that relate the data in the
visible and near infrared channels to a preflight standard (preflight
calibration). During the time of operation of the satellite, the
sensitivity of the red and infrared has gradually decreased. This
decrease is not accounted for by the preflight calibration. The Thermal
infrared channels are calibrated in-flight using a view of a stable
blackbody and space as a reference.
5.2.1 Specifications.
LAC resolution
IFOV 1.4 mRad
RESOLUTION 1.1 km
ALTITUDE 833 km
SCAN RATE 360 scans/min
1.362 samples per IFOV
SCAN RANGE -55.4 to 55.4 degrees
SAMPLES/SCAN 2048 samples per channel per earth scan
NOTE: data at GAC resolution are resampled onboard by
taking the average of 4 by 1 pixels out of the first row
of a 5 by 3 LAC window.
5.2.1.1 Tolerance.
The AVHRR IR channels were designed for an NEDT
(Noise Equivalent Differential Temperature) 0.12
K (at 300 K), and a Signal-to-noise
ratio of 3:1 at 0.5 percent albedo.
5.2.2 Frequency of Calibration.
For GAC data, NOAA provides calibration parameters on tape that
relate the data to a preflight standard (preflight calibration).
These parameters generally do not change during the time of
operation of a satellite (with exception of NOAA-11). The
preflight calibration does not take degradation of the sensors
into account. To adjust for sensor degradation in the NDVI an
approximate correction was used (see Los, 1993, and section 9 of
this document).
5.2.3 Other Calibration Information.
(See Section 9).
6. PROCEDURE
6.1 Data Acquisition Methods.
NOAA-9 AVHRR level 1B data for 1987 - 1988 were acquired from NOAA-NESDIS
and processed by the GIMMS group into monthly maximum value NDVI
composites at 8 km resolution.
6.2 Spatial Characteristics.
6.2.1 Spatial Coverage.
The coverage is global. Data in each file are ordered from North
to South and from West to East beginning at 180 degrees West and
90 degrees North. Point (1,1) represents the grid cell centered
at 89.5 N and 179.5 W (see section 8.4).
The NDVI data coverage is between latitude of 75 degrees South and
North with the exception of Greenland and New Zealand, because
data for these areas were not available in the GIMMS continental
data set. Missing data occur in the Northern Hemisphere during the
winter months (see errors, section 10.1). Because New Zealand
data were mapped only incidentally, monthly composites were
calculated from the entire 1982-1990 1 X 1 degree NDVI data set
(see section 9). NDVI data collected at estimated solar zenith
angles greater than 85 degree were eliminated, which explains the
occurrence of missing data for the (Southern Hemisphere) winter in
South-America.
6.2.2 Spatial Resolution.
The data are given in an equal-angle lat/long grid that
has a spatial resolution of 1 X 1 degree lat/long.
6.3 Temporal Characteristics.
6.3.1 Temporal Coverage.
January 1987 through December 1988 (Data acquisition switched from
NOAA-9 to 11 in November 1988)
6.3.2 Temporal Resolution.
Monthly.
7. OBSERVATIONS
7.1 Field Notes.
Not applicable.
8. DATA DESCRIPTION
8.2 Type of Data.
--------------------------------------------------------------------------------
| 8.2.1 | | | |
|Parameter/Variable Name | | | |
--------------------------------------------------------------------------------
| | 8.2.2 | 8.2.3 | 8.2.4 | 8.2.5 |
| |Parameter/Variable Description |Range |Units |Source |
--------------------------------------------------------------------------------
|NDVI | | | |
| |Normalized Difference Vegetation |Min = 0.05 $ |[Unitless]* |Holben, |
| |Index calculated from AVHRR |Max = 0.65 $ | |(1986); |
| |channel 1 and 2 radiance data, |missing | |Los et al |
| |normalized for incoming solar |data = | |(1994) |
| |radiation. |-99.000 $ | | |
| | | | | |
--------------------------------------------------------------------------------
$ The minimum and maximum NDVI are for a land surfaces, desert and dense
vegetation, respectively. The values are approximate. Where there are
missing data (see section 6.2.1) a fill value of -99.000 is used.
* NDVI units are non-dimensional, a fraction with a potential range between -1
and 1.
8.3 Sample Data Base Data Record.
Not applicable.
8.4 Data Format
The CD-ROM file format is ASCII, and consists of numerical fields of
varying length, which are space delimited and arranged in columns and
rows. Each column contains 180 numerical values and each row contain 360
numerical values.
Grid arrangement
ARRAY(I,J)
I = 1 IS CENTERED AT 179.5W
I INCREASES EASTWARD BY 1 DEGREE
J = 1 IS CENTERED AT 89.5N
J INCREASES SOUTHWARD BY 1 DEGREE
90N - | - - - | - - - | - - - | - -
| (1,1) | (2,1) | (3,1) |
89N - | - - - | - - - | - - - | - -
| (1,2) | (2,2) | (3,2) |
88N - | - - - | - - - | - - - | - -
| (1,3) | (2,3) | (3,3) |
87N - | - - - | - - - | - - - |
180W 179W 178W 177W
ARRAY(360,180)
8.5 Related Data Sets
GIMMS first generation continental NDVI data set, this is the source
data set for the 1 X 1 degree NDVI data.
1 X 1 degree land cover classification (GRID.VGC) (on this CD-ROM).
This data set was generated from existing land cover
classifications,
1 X 1 degree Fourier adjusted NDVI, and NOAA/NASA Pathfinder Land AVHRR
channel 1 data (James and Kalluri, 1994).
from the NDVI and VEG_CLSS.VGC data sets the following data sets were
derived
1 X 1 degree Fourier based adjustment, solar zenith angle correction,
interpolation of missing data and reconstruction of evergreen
broadleaf land cover types (tropics), (FASIR-NDVI) data (on this
CD-ROM).
1 X 1 degree fraction of photosynthetic active radiation absorbed by the
vegetation canopy (FPAR) monthly global data (on this CD-ROM).
1 X 1 degree leaf area index (LAI) global data (on this CD-ROM).
1 X 1 degree Greenness global data (on this CD-ROM).
1 X 1 degree roughness length monthly global data (on this CD-ROM).
1 X 1 degree snow free Surface Albedo monthly global data (on this
CD-ROM).
Note: A special edition of the International Journal of Remote Sensing
(1994) reviews several other global vegetation data sets derived
from satellite data.
1. Global vegetation index (GVI): This data set was prepared by NOAA
(Tarpley et al 1984). The resolution is ca 25 km, GVI is calculated
from channel 1 and 2 data without a normalization for solar flux in
channel 1 and 2. The monthly value is calculated as the average of
weekly maximum value composites. The maximum value composites are
based on the maximum difference between channel 1 and 2, no data from
off-nadir viewing angles are eliminated. An updated version of the
GVI, which will address some of the problems in the old GVI data set
is currently being processed (Gutman et al., 1994/1995)
2. The University of Maryland improved GVI (Goward et al 1994) is derived
from the NOAA GVI (see 1). Adjustments were made to correct for
atmospheric effects, sensor degradation, and viewing and illumination
geometry. Because this data set is derived from the weekly GVI
composites, it is likely to share some of its problems.
3. The NOAA/NASA Pathfinder AVHRR Land Data Set. (James and Kalluri 1994)
The data set contains bi-weekly maximum NDVI composites and associated
data from channels 1 - 5, the solar zenith angle, scan and relative
azimuth angles, day of year, and cloud and quality control flags. The
data were corrected for sensor degradation, are normalized for
incoming solar radiation, and have been corrected for Rayleigh
scattering and ozone absorption. Clouds were detected with the CLAVR
algorithm (Stowe et al 1991). No corrections were made for
atmospheric dust and water vapor. Processing of a 1 X 1 degree NDVI
product from the AVHRR-Pathfinder data is planned.
9. DATA MANIPULATIONS
9.1 Formulas.
9.1.1 Derivation Techniques/Algorithms.
General: The source data for the 1*1 degree NDVI data sets were
the monthly continental GIMMS NDVI composites at GAC resolution.
(Tucker et al 1986, Holben 1986). The GIMMS NDVI data were
averaged over 1*1 degree. Missing values were excluded from
averaging, as were spurious high values resulting from bad scans
(> 950 counts) and data from surface water (lakes, oceans).
Surface water bodies were identified with the boundaries of the
CIA world data bank II (Gorny, 1977).
Processing steps for merging the continental GIMMS NDVI data into
a 1 X 1 degree global data set.
1. Adjustment for sensor degradation was applied to the
GIMMS continental NDVI composites (Los, 1993; Los et al.,
1994).
r(SRp + D2p / L1p) - (1 + D1p / L1p)
NDVIc = ------------------------------------
r(SRp + D2p / L1p) + (1 + D1p / L1p)
with
NDVIc is the NDVI corrected for sensor degradation
SRp is Simple Ratio with NOAA AVHRR preflight calibration
= (1 + NDVIp) / (1 - NDVIp)
NDVIp = NDVI with NOAA AVHRR preflight calibration in NDVI
units
L1p = the normalized radiance in channel 1 (units of
reflectance) an approximation for L1p is used:
1/L1p = a + b SRp (a = -4.34 and b = 7.48)
Note: the following equations can only be applied to NDVI!!
data (hence not GVI!!) from NOAA 9!! Equations are established
with data from Holben et al., 1990, and Kaufman and Holben
(1993). Degradation (r) was slightly revised using additional
information from desert sites.
r is the sensor degradation in channel 1 divided by the
degradation in channel 2
For NOAA-9 (1987 - 10/1988)
r = 0.311528 + 0.517956 (Y-1982) - 0.110568 (Y-1982)^2 +
0.0072339 * (Y-1982)^3
D1p = DC01 * G1p
D2p = DC02 * G2p
DC01,DC02 = the difference between the preflight calibration
offset and the deep space count in resp. channel 1
and 2.
DC01 = -1.9044 + 0.176 (Y-1985) - 0.02504 (Y-1985)^2
DC02 = -4.2445 + 0.820 (Y-1985) - 0.12518 (Y-1985)^2
G1p = A1p * pi * R^2 / F01 = 0.55 * pi * 1 / 1629
G2p = A2p * pi * R^2 / F02 = 0.36 * pi * 1 / 1043
Y = Year and fraction of the year in decimals (e.g.
Jan 15 1991 = 1991.041
Note: the following equations can only be applied to NDVI!!
data from NOAA 11!! Equations are established with data from
Holben et al 1990, and Kaufman and Holben (1993). Degradation
(r) was slightly revised using additional information from
desert sites.
r = minimum of (0.6604181 + 0.065987 * (year - 82) ) and
1.153
D1p = DC01 * G1p
D2p = DC02 * G2p
DC01,DC02 = the difference between the preflight calibration
offset and the deep space count in resp. channel 1
and 2.
DC01 = 1.2
DC02 = 1.0
G1p = A1p * pi * R^2 / F01 = 0.47 * pi * 1 / 1630
G2p = A2p * pi * R^2 / F02 = 0.28 * pi * 1 / 1053
Y = Year and fraction of the year in decimals (e.g.
Jan 15 1991 = 1991.041
A1p,A2p is the gain alpha for channel 1 and 2
(W./microm/sr/m^2) F01,F02 is the solar constant
(W./microm/m^2) R is the earth sun distance in AU here kept 1,
since it cancels in the calculation of the NDVI
2. Average the calibrated NDVI pixels over 1 X 1 degree cells,
exclude missing values. If > 50 % land surface pixels exist and
at least 1 of these values is present in a 1 X 1 degree cell
the NDVI is calculated. Data with > 50 % water pixels were
eliminated using the surface water mask.
3. Merge data sets of all continents and map data
onto linear lat lon projection, in case of
overlap between continents select maximum value
4. Because mapping of New Zealand was incidental, a 12
month data set was created from the maximum values
for each month for the period of 1982 - 1990. This 12
month data set was used as a substitute.
9.2 Data Processing Sequence
9.2.1 Processing Steps and Data Sets
Following is a short description of the processing steps
involved in the compilation of the source monthly GIMMS
continental NDVI data at GAC resolution:
1. Daily data (Channel 1 - red, Channel 2 - near infrared, and
Channel 5 - thermal infrared) were extracted by continent and
mapped onto a projection (conical equidistant projection with 2
parallels for Australia and Hammer Aitoff projection for other
continents (see Mailing 1973 for description of projections).
2. Data were screened for clouds by eliminating values with a
channel 5 brightness temperature below 285 deg. K for Africa
and 273 deg. K for the other continental data sets.
3. Data from scan angles greater than 42 degrees were eliminated.
4. Data were normalized for solar flux at the top of the
atmosphere. The resulting normalized radiance value would
correspond to a reflectance value in the case of an overhead
sun and nadir viewing angle.
5. Daily NDVI was calculated as NDVI = (L2 - L1) / (L2 + L1)
(L2, L1 are channel 2 and 1 normalized radiances,
respectively).
6. Registration of daily data was manually adjusted by matching
shore lines with those of the CIA world data bank II (Gorny,
1977).
7. The maximum NDVI for each pixel was selected over a period of a
month to obtain a monthly value. Selecting the maximum NDVI per
month diminishes the effects of clouds, atmospheric turbidity,
viewing geometry and missing values.
8. The NDVI data were approximately adjusted for sensor
degradation (see section 9.1.1.).
9. Averaging over 1 x 1 deg. and merging the continental data
sets into one global data set.
A more comprehensive description of NOAA-AVHRR data can be found
in Kidwell (1988). For compositing and data processing details see
Holben (1986) and Los et al (1994).
9.2.2 Processing Changes.
Not available at this revision.
9.3 Calculations.
9.3.1 Special Corrections/Adjustments.
Not available at this revision.
9.4 Graphs and Plots.
Not available at this revision.
10. ERRORS
10.1 Sources of Error
The main sources of error result from interference by clouds and the
atmosphere. Effects of variations in viewing and illumination
conditions are strongly reduced by eliminating the margins of the scans
and compositing. Sensor degradation is fairly well adjusted for (Los
1993). Resampling onboard the AVHRR and location and registration
errors are believed to be of little impact on the 1 X 1 degree data. A
rough estimate of the magnitude for several effects is (most of these
reduce the NDVI value)
- Cloud contamination, not detected by the temperature threshold
techniques, can result in changes between -0.1 and -0.2 NDVI units,
predominantly in the tropics and sub-tropics during the monsoon.
- atmospheric constituents result in an overall decrease of about 0.2,
NDVI units but effects vary greatly as a function of space and time.
- Solar zenith angle effect of about 0.1 NDVI difference between low and
high angles. This causes inconsistencies between data collected at the
same location for a different period of time (e.g. summer versus late
autumn) or between data collected at the same time period at different
latitudes.
- the cloud screening technique which is used to identify and eliminate
cloud contaminated data in the production of the continental NDVI data
set is based on the brightness temperature measured with channel 5.
All data with temperature below a threshold temperature are discarded.
This does not only eliminate cloud data but also data from areas with
low surface temperature. Vast areas without data occur in the Northern
Hemisphere during the winter at mid and high-latitudes
- data collection at off-nadir viewing angles by the AVHRR. Large
changes in the NDVI as a result of variations in viewing angle
have been documented on individual GAC pixels. Compositing tends
to select NDVI values from the forward scatter direction, and hence
reduces the variation in viewing angle. The distribution of viewing
angles is skewed, with an average of about 10 degrees off-nadir and a
peak around the maximum forward scatter angle (Gutman, 1991).
When averaged, the variation in viewing angles tends to be further
reduced (regression towards the mean) and variations in the NDVI as
a result of viewing angle will in-part cancel out.
- A small bias towards higher values as a result of compositing during
changes in the state of vegetation at the start and end of the growing
season.
- At the low end of the NDVI, differences in the values can be caused by
variations in the composition of the soil background, rather than
differences in the amount of vegetation.
- Misregistration of the continental NDVI data in combination with
compositing can result in too high values or can blur linear features
in the images (Holben, 1986). Effect on 1 X 1 degree is likely to be
minimal.
- Sensor degradation: This has for the most part been accounted for by
the approximation technique referenced in section 9. The residual
(RMS) error is estimated around 0.002 NDVI (Los, 1993).
Problems are likely to occur when data acquisition changes from one to
the next sensor. The November 1988 data are believed to be spurious,
because during this month data from two sensors were used to calculate
the composite, prior to applying the correction for sensor degradation.
We applied the calibration for the AVHRR onboard NOAA-9 to this month,
because degradation was less severe, and therefore compositing is likely
to favor selection of NOAA-9 data.
10.2 Quality Assessment.
10.2.1 Data Validation by Source.
The lack of data validation for this data set, as for any global
data set, is a major concern, however, the (continental) NDVI
data have been used in a vast number of studies, both on
local and global levels. The NDVI has been related to the rate
of carbon assimilation (Box et al., 1989; Tucker et al., 1986),
seasonal sum of above ground biomass (Justice, 1986; Prince and
Justice, 1991; Prince, 1991; Tucker et al., 1981), LAI (Tucker
et al., 1986), FPAR (Sellers et al., 1992), with various degrees
of success. In general, the relationship between FPAR and NDVI
is fairly strong, and is the driver of the other NDVI -
vegetation parameter relationships
10.2.2 Confidence Level/Accuracy Judgment.
The NDVI data set is believed to reflect the general patterns of
vegetation distribution and the changes in vegetation in a
qualitative sense. For quantitative use of the data, the errors
given in section 10.1 can form serious limitations.
10.2.3 Measurement Error for Parameters and Variables
Errors are generally systematic and lower the NDVI. For dense
vegetation cover (NDVI > 0.6) the approximate error values are:
cloud: -0.1 .. -0.2 (in the tropics and in the sub-
tropics during the monsoon, in mountainous
areas etc.).
atmosphere: -0.1 .. -0.2
off nadir viewing: +0.02 forward -0.05 backscatter
solar zenith angle: -0.1
missing data (assume NDVI = 0), -0.1 .. -0.2
misregistration: + 0.02
compositing: (0 .. +0.05)
soil: (for the low end and mid range of NDVI only)
+ 0.05 .. + 0.15
10.2.4 Additional Quality Assessment Applied.
Not available at this revision.
11. NOTES
11.1 Known Problems With The Data.
Not available at this revision.
11.2 Usage Guidance.
The NDVI data set reflects global patterns of vegetation. However,
serious errors may be present in the data set that could limit the
validity of conclusions, especially for specific locations.
11.3 Other Relevant Information.
Not available at this revision.
12. REFERENCES
12.1 Satellite/Instrument/Data Processing Documentation.
Kidwell, K. B., 1988. NOAA polar orbiter data (TIROS-N, NOAA-6, NOAA-
7, NOAA-8, NOAA-9, NOAA-10 and NOAA 11) users guide. (National
Oceanic and Atmospheric Administration: Washington, DC 20233).
12.2 Journal Articles and Study Reports.
Box, E. O., B. N. Holben, and V. Kalb, 1989. Accuracy of vegetation
index as a predictor of biomass. primary production and net CO2 flux
Vegetatio, 80:71-89.
Dorman, J. L., and P.J. Sellers, 1989. A Global climatology of albedo,
roughness length and stomatal resistance for atmospheric general
circulation models as represented by the simple biosphere model
(SiB). Journal of Applied Meteorology, 28:833-855.
Gorny, A.J., 1977. World Data Bank II, General user guide, Central
Intelligence Agency, Washington DC.
Goward, S.N., S. Turner, and D.G. Dye, 1994. The University of
Maryland Improved Global Vegetation Index Product. International
Journal of Remote Sensing (in press).
Gutman, G., 1991, Vegetation indices from AVHRR: An update and future
prospects. Remote Sensing of the Environment, 35, 121-138.
Gutman, G., Tarpley, D., Ignatov, A., Olsen, S., (1994/1995) The
enhanced NOAA Global Land data set from the Advanced Very High
Resolution Radiometer. submitted to the Bulletin of the American
Meteorological Society.
Holben, B. N., 1986. Characteristics of maximum-value composite
images for temporal AVHRR data. International Journal of Remote
Sensing, 7:1435-1445.
Holben, B. N., Y. Kaufmann, and J. Kendall, 1990. NOAA-11 AVHRR
Visible and near-IR inflight calibration. International Journal of
Remote Sensing, 11:1511-1519.
James, M.E., S.N.V. Kalluri, 1994. The Pathfinder land data set: an
improved coarse resolution data set for terrestrial monitoring.
International Journal of Remote Sensing, (in press).
Justice, C. O., (ed.), 1986. Special edition of the International.
Journal of Remote Sensing, 7:1385-1622.
Kaufman, Y. J., and B. N. Holben, 1993. Calibration of the AVHRR
visible and near-IR bands by atmospheric scattering, ocean glint and
desert reflection. International Journal of Remote Sensing, 14:21-52.
Kumar, M, Monteith, 1982. Remote sensing of plant growth. In:
Plants and the daylight spectrum (London: Academic Press).
Los, S.O., 1993. Calibration adjustment of the AVHRR Normalized
Difference Vegetation Index without recourse to the component channel
data. International Journal of Remote sensing, 14:1907-1917.
Los, S.O., C.O. Justice, C.J. Tucker, 1994. A global 1 by 1 degree NDVI
data set for climate studies derived from the GIMMS continental NDVI
data. International Journal of Remote Sensing, 15(17):3493-3518.
Maling, 1973. Coordinate systems and map projections. (London: George
Phillip and Son Ltd.).
Prince, S. D., 1991. Satellite remote sensing of primary production:
comparison of results for Sahelian grasslands 1981-1988.
International Journal of Remote Sensing, 12:1301-1311.
Prince, S. D., and C. O. Justice (eds.), 1991. Review of grassland
remote sensing exercises using AVHRR data. International Journal of
Remote Sensing, 12:1133-1421.
Sato, N., P.J. Sellers, D.A. Randall, E.K. Schneider, J. Shukla, J.L.
Kinter Y.T. Hou, and E. Albertazzi, 1989. Effects of implementing the
Simple Biosphere Model (SiB) in a general circulation model.
Journal of Atmospheric Science, 46:2757-2782.
Sellers, P.J., 1985. Canopy reflectance, photosynthesis and
transpiration. International Journal of Remote Sensing, 16:1335-
1372.
Sellers, P.J., J.A. Berry, G.J. Collatz, C.B. Fields, F.G Hall,
1992. Canopy reflectance, photosynthesis and transpiration. III. A
reanalysis using improved leaf models and a new canopy integration
scheme. Remote Sensing of Environment, 42:187-216.
Sellers, P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.J.
Collatz, and D.A. Randall, 1994. A global 1*1 degree NDVI data set
for climate studies. Part 2: The generation of global fields of
terrestrial biophysical parameters from the NDVI. International
Journal of Remote Sensing, 15(17):3519-3545.
Sellers, P.J., D.A. Randall, C.J. Collatz, J.A. Berry, C.B. Field, D.A.
Dazlich, C. Zhang, and C.D. Collelo, 1995a. A revised land surface
parameterization (SiB2) for atmospheric GCMs. Part 1: Model
formulation. submitted to Journal of Climate.
Sellers, P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.J.
Collatz, and D.A. Randall, 1995b. A revised land surface
parameterization (SiB2) for atmospheric GCMs. Part 2: The generation
of global fields of terrestrial biophysical parameters from satellite
data. Submitted to Journal of Climate.
Stowe, L.L., E.P. McClain, R. Carey, P. Pellegrino, G.G. Gutman,
P. Davis, C. Long, and S. Hart, 1991. Global distribution of cloud
cover derived from NOAA/AVHRR operational satellite data. Advances in
Space Research, 3:51-54.
Tarpley, J.D., S.R. Schneider, R.L. Money, 1984. Global vegetation
indices from the NOAA-7 meteorological satellite. Journal of Climate
and Applied Meteorology, 23:491-494.
Tucker, C. J., B. N. Holben, J. H. Elgin, and J. E. McMurtry,
1981. Remote sensing of total dry matter accumulation in winter
wheat. Remote Sensing of Environment, 11:171-189.
Tucker, C. J., I. Y. Fung, C. D. Keeling, and R. H. Gammon, 1986.
The Relationship of Global Green Leaf Biomass to Atmospheric CO2
Concentrations. Nature, 319:159-199.
12.3 Archive/DBMS Usage Documentation.
Contact the EOS Distributed Active Archive Center (DAAC) at NASA Goddard
Space Flight Center (GSFC), Greenbelt Maryland (see Section 13 below).
Documentation about using the archive or information about access to the
on-line information system is available through the GSFC DAAC User
Services Office.
13. DATA ACCESS
13.1 Contacts for Archive/Data Access Information.
GSFC DAAC User Services
NASA/Goddard Space Flight Center
Code 902.2
Greenbelt, MD 20771
Phone: (301) 286-3209
Fax: (301) 286-1775
Internet: daacuso@eosdata.gsfc.nasa.gov
13.2 Archive Identification.
Goddard Distributed Active Archive Center
NASA Goddard Space Flight Center
Code 902.2
Greenbelt, MD 20771
Telephone: (301) 286-3209
FAX: (301) 286-1775
Internet: daacuso@eosdata.gsfc.nasa.gov
13.3 Procedures for Obtaining Data.
Users may place requests by accessing the on-line system, by sending
letters, electronic mail, FAX, telephone, or personal visit.
Accessing the GSFC DAAC Online System:
The GSFC DAAC Information Management System (IMS) allows users to
ordering data sets stored on-line. The system is open to the public.
Access Instructions:
Node name: daac.gsfc.nasa.gov
Node number: 192.107.190.139
Login example: telnet daac.gsfc.nasa.gov
Username: daacims
password: gsfcdaac
You will be asked to register your name and address during your first
session.
Ordering CD-ROMs:
To order CD-ROMs (available through the Goddard DAAC) users should
contact the Goddard DAAC User Support Office (see section 13.2).
13.4 GSFC DAAC Status/Plans.
The ISLSCP Initiative I CD-ROM is available from the Goddard DAAC.
14. OUTPUT PRODUCTS AND AVAILABILITY
14.1 Tape Products.
None.
14.2 Film Products.
None.
14.3 Other Products.
None.
15. GLOSSARY OF ACRONYMS
AVHRR Advanced Very High Resolution Radiometer
CD-ROM Compact Disk (optical), Read Only Memory
DAAC Distributed Active Archive Center
EOS Earth Observing System
GAC Global Area Coverage
GCM General Circulation Model of the atmosphere
CIA Central Intelligence Agency
FASIR (NDVI) Fourier Adjusted, Solar zenith angle correction,
Interpolation (of missing data during winter), and
Reconstruction of NDVI (over tropical forests).
FPAR Fraction of Photosynthetic Active Radiation absorbed by
green vegetation
GIMMS Global Inventory Monitoring and Modeling Studies at
NASA GSFC
GSFC Goddard Space Flight Center
GVI Global Vegetation Index
IDS Inter disciplinary Science
IFOV Instantaneous Field Of View
ISLSCP International Satellite Land Surface Climatology Project
LAC Local Area Coverage
LAI Leaf Area Index
LST Local Solar Time
NASA National Aeronautics and Space Administration
NDVI Normalized Difference Vegetation Index
NOAA National Oceanographic and Atmospheric Administration
pixel Picture element
SiB2 Simple Biosphere model (Sellers et al 1995a,b)
SR Simple Ratio