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. 


     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.

*             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.


     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
     -------    -------------------    ---------------------------------
        1          0.58  -  0.68       Daytime Cloud and Surface Mapping
        2          0.725 -  1.10       Surface Water Delineation, Vegetation
        3          3.55  -  3.93       Sea Surface Temperature (SST),
                                       Nighttime Cloud Mapping
        4*        10.5   -  11.5       Surface Temperature, Day/Night Cloud
        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 

     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.


                     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 

     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. 


                           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

             I  = 1 IS CENTERED AT 179.5W
             J  = 1 IS CENTERED AT 89.5N

             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


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 
     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

     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 
     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 

     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., 

                          r(SRp + D2p / L1p) - (1 + D1p / L1p)
                  NDVIc = ------------------------------------
                          r(SRp + D2p / L1p) + (1 + D1p / L1p)


               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 
               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 
               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, 

            6. Registration of daily data was manually adjusted by matching 
               shore lines with those of the CIA world data bank II (Gorny, 

            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 

      - 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 

      - 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 

      - 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-
      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:
      Login example: telnet daac.gsfc.nasa.gov
      Username:  daacims
      password:  gsfcdaac

      You will be asked to register your name and address during your first

      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.


14.1  Tape Products.


14.2  Film Products. 


14.3  Other Products. 


                         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