FSR_NDVI.DOC
1. TITLE
1.1 Data Set Identification
Fourier-Adjustment, Solar zenith angle corrected, Interpolated
Reconstructed (FASIR) Normalized Difference Vegetation Index (NDVI).
FASIR NDVI (Monthly ; NASA/GSFC)
1.2 Data Base Table Name.
Not applicable.
1.3 CD-ROM File Name.
\DATA\VEGETATN\FSR_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 .FSR 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 publications when ever these data are used:
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., 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.
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 work was funded by a NASA Earth Observing System - Interdisciplinary
Science (EOS-IDS) grant (Sellers-Mooney). David Strauss of COLA, Dept. of
Meteorology of the University of Maryland is thanked for his advice on
Fourier series. The members of the GIMMS group at NASA Goddard Space
Flight Center, in particular Wayne Newcomb, are thanked for their
assistance. Computer code that aided in processing of the FASIR data set
was developed by Hong Zhang, Laboratory for Global Remote Sensing,
University of Maryland.
3. INTRODUCTION
3.1 Objective/Purpose
The 1 X 1 degree FASIR - NDVI data set was compiled to correct the 1 X 1
degree NDVI data (see the NDVI.DOC on this CD-ROM) for:
- Data drop-outs occurring fewer than three months in a row.
These drop-outs may be caused by cloud cover, atmospheric constituents
(aerosols, molecules, gases, water vapor), or could be missing data.
- Changes in the NDVI as a result of variations in solar zenith angle.
- Missing data (>= 3 months) for evergreen needle bearing vegetation
types, occurring for the most part in the Northern Hemisphere during
winter.
- Persistent cloudiness (>= 3 months) over tropical forests.
The corrected NDVI is referred to as FASIR-NDVI (see section 3.2). The
FPAR, LAI, Greenness, Roughness Length and Albedo data sets were
calculated (see following documents FPAR.DOC, LAI.DOC, GREENNSS.DOC,
ROUGHNSS.DOC and SUR_ALBD.DOC) using the FASIR-NDVI data set. These
five data sets were used in the simple biosphere model (SiB2) of
Sellers et al (1994, 1995a), to calculate the energy and water exchange
between the biosphere and the lower boundary layer of the atmosphere.
3.2 Summary of Parameters
FASIR: Fourier based Adjustment, Solar zenith angle correction,
Interpolation of missing data and Reconstruction of
evergreen broadleaf land cover types (tropics). FASIR is
calculated from NDVI and VEG_CLSS.VGC data.
Inputs to FASIR-NDVI:
NDVI: Normalized Difference Vegetation Index calculated from
AVHRR channel 1 and 2 normalized radiance data with NDVI =
(L2 - L1)/(L2 + L1)
VEG_CLSS.VGC: Land cover classification data (this CD-ROM).
Solar Zenith Angle: calculated with NOAA/NASA AVHRR Land
Pathfinder navigation routines (James and Kalluri, 1994).
3.3 Discussion.
FASIR NDVI was developed to account for errors in the NDVI data.
In order to fix some of the problems in the NDVI data set, approximate
corrections were developed to obtain a more complete and consistent
coverage of the globe. Some of the corrections to the NDVI data are made,
specifically with the use of SiB2 in mind, e.g., SiB2 uses NDVI values
to calculate FPAR and LAI values, which in turn are used to calculate
(amongst others) photosynthetic rate, albedo and roughness length.
Missing NDVI data (which can be substituted by zero NDVI values) for
needle bearing evergreen vegetation during winter, are not a problem for
the calculation of photosynthetic rates, because these values will be
close to zero. However, for the calculation of roughness length, a zero
NDVI value is not acceptable, because this value would translate to a
zero LAI, and by the way SiB2 is formulated, to a zero roughness length.
This would result in erroneous estimates for the aerodynamic resistance.
(see also NDVI document, and Sellers et al 1994).
4. THEORY OF MEASUREMENTS
Note: the description in the NDVI document provides general information
on the purpose of this data set.
The derivation of the FASIR corrections depends heavily on assumptions
made with respect to the distribution of errors in the NDVI. FASIR is
divided into the following steps:
FA: Fourier Adjustment
The Fourier adjustment requires an input of 12 monthly values at a time.
In general, it is assumed that the effects of atmosphere, clouds, off-
nadir viewing etc. will lower the value of the NDVI. It is further
assumed that the development of NDVI-time series is smooth, i.e., sudden
changes in the NDVI are most likely to be related to distortions of the
time-series. Using these two assumptions, a technique was developed which
fits a smooth series through an NDVI sequence for a year. The smoothed
NDVI curve is compared with the values of the original series. Points
below the fitted curve are likely to be unreliable (errors tended to
decrease the NDVI). Based on the distance to the fitted curve weights
were calculated and a new curve was fitted with strong emphasize on the
reliable points (points above the first fitted curve).
S: Solar zenith angle adjustment:
We currently do not have sufficient data to do a full solar zenith angle
correction over the entire range of NDVI. By comparing the tails (i.e.,
the high and low end) of NDVI distributions taken for a number of
vegetation types and for different solar zenith angle intervals, and by
assuming that the the NDVI values at these tails are from equally green
(or dead) targets, (in other words we say that changes at the tails of
distributions taken for different solar zenith angle intervals are the
result of changes in solar zenith angle) we estimate the result of solar
zenith angle effect for the high and low ends of the NDVI. Lacking
further information, we assume the solar zenith angle effect to be linear
over the range of NDVI, even though we know from ground measurements that
quite often this is not the case. This procedure requires an assumption
about vegetation type. In this version, we made use of the satellite-
based vegetation classification of DeFries and Townshend (1994a, b)
instead of the in situ-based product described in Sellers et al (1994).
The DeFries and Townshend (1994a, b) classification data are on the
ISLSCP Initiative I CD-ROM Volume I.
I: Interpolation
The interpolation of NDVI data for evergreen needle leaf ground cover
types during the winter months is necessary to estimate values for leaf
area index, roughness length, and albedo using SiB2.
R: reconstruction:
The reconstruction over tropical forests (broad leaf evergreen vegetation
type, class 1 in VEG_CLSS.VGC) is done to remove the effects of cloud,
not removed by the Fourier Adjustment. We take the maximum NDVI over the
year to represent the value for each month. This is not a very desirable
procedure, since it removes the seasonality in the data. However,
failing to adjust the data, will result in underestimates for the NDVI,
which translates into low estimates of FPAR. This would result in severe
underestimation of LAI, because the FPAR-LAI relationship is exponential.
5. EQUIPMENT
5.1 Instrument Description.
See the NDVI document.
5.1.1 Platform (Satellite, Aircraft, Ground, Person...).
See the NDVI document.
5.1.2 Mission Objectives.
See the NDVI document.
5.1.3 Key Variables.
See the NDVI document.
5.1.4 Principles of Operation.
See the NDVI document.
5.1.5 Instrument Measurement Geometry.
See the NDVI document.
5.1.6 Manufacturer of Instrument.
See the NDVI document.
5.2 Calibration.
See the NDVI document.
5.2.1 Specifications.
See the NDVI document.
5.2.1.1 Tolerance.
See the NDVI document.
5.2.2 Frequency of Calibration.
See the NDVI document.
5.2.3 Other Calibration Information.
See the NDVI document.
6. PROCEDURE
6.1 Data Acquisition Methods.
The 1 X 1 degree FASIR-NDVI data set is derived from the 1 X 1 degree
global NDVI data set (1982 - 1990), and the 1 X 1 degree land cover
data (VEG_CLSS.VGC). Two years from the full 8 years data set are
extracted for this CD-ROM.
6.2 Spatial Characteristics.
6.2.1 Spatial Coverage.
The coverage is global. Data in 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).
6.2.2 Spatial Resolution.
The data are given in an equal-angle lat/long grid that has a
spatial resolution of 1 * 1 degree lat/long.
6.3 Temporal Characteristics.
6.3.1 Temporal Coverage.
January 1987 through December 1988.
6.3.2 Temporal Resolution.
Monthly.
7. OBSERVATIONS
7.1 Field Notes.
Not applicable.
8. DATA DESCRIPTION
8.1 Table Definition With Comments.
Not applicable.
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 |
--------------------------------------------------------------------------------
|FASIR_NDVI (Sellers et al., 1994) | | | |
| |Normalized Difference Vegetation |Min = 0.05 $ |[Unitless]* |Los et al.|
| |Index, which has been corrected |Max = 0.65 $ | |(1994); |
| |for distortions caused by | | |Sellers et|
| |persistent cloud cover, | | |al. |
| |atmospheric aerosols and water | | |(1994a) |
| |vapor, solar zenith angle | | | |
| |effects, missing data in the | | | |
| |Northern Hemisphere during | | | |
| |winter, and persistent | | | |
| |cloudiness over tropical forests | | | |
| | | | | |
--------------------------------------------------------------------------------
$The minimum and maximum NDVI are for land surfaces, desert and dense
vegetation respectively. Both values are approximate. A minimum value of
0.033 has been inserted into the FASIR_NDVI data sets for regions (Antarctic,
Greenland...) that are classified as missing data in the NDVI data set (see
NDVI.DOC).
*FASIR-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
1 X 1 degree Normalized Difference Vegetation Index (NDVI) global data
(on this CD-ROM).
1 X 1 degree global land cover classification image (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).
1 X 1 degree vegetation classification map (Dorman and Sellers 1989,
Nobre et al. 1991), on this CD-ROM.
Also see section 8.5 (Related Data Sets) in the NDVI document.
9. DATA MANIPULATIONS
9.1 Formulas.
Data have been adjusted using both formulas and rules, the description of
the complete procedure is provided below in section 9.2.
9.1.1 Derivation Techniques/Algorithms.
See section 9.2 below.
9.2 Data Processing Sequence
See Sellers et al., 1994, 1995b for a more elaborate description.
9.2.1 Processing Steps and Data Sets.
The following data sets are used for the calculation of FASIR-
NDVI.
1. 1 X 1 degree NDVI data set (1982 - 1990, two years were
extracted for this CD-ROM) for FA, S, I and R corrections.
2. Land cover classification (VEG_CLSS.VGC, this CD-ROM) for S, I
and R corrections.
3. Estimated solar zenith angles at time of data collection by the
satellite for S correction.
4. Solar zenith angles for noon, local time (15th of the month)
for S correction.
Processing steps
1. Fourier adjustment (uses NDVI data only, data are converted to
NDVI units, different results will be obtained when using
counts). Fill up and count missing data of temporal NDVI data
series Y
IF Y_i .EQ. missing value THEN Y_i = 0
IF number of missing values .LT. 3/4 * 12 THEN BEGIN (else do
nothing)
For each 1 X 1 degree cell a Fourier series of 3
harmonics is fitted with least squares through a time-
series of 12 months using the matrix equation, solve [c]
with
( [F]^T [F] ) [c] = [F]^T [Y]
with
| 1, cos[phi_01], sin[phi_01], cos[2 phi_01], sin[2 phi_01] |
| ., . , . , . , . |
[F] = | ., . , . , . , . |
| ., . , . , . , . |
| 1, cos[phi_12], sin[phi_12], cos[2 phi_12], sin[2 phi_12] |
phi_i = 2 * pi *(i - 1) / 12; i refers to month i
[F]^T = transposed [F]
|a_0|
|a_1|
[c] = |b_1| Fourier coefficients to be solved
|a_2|
|b_2|
|Y_1 |
| . |
[Y] = | . | the NDVI time-series for a particular 1*1 cell
| . |
|Y_12|
The Fourier series Y' is calculated by
Yi' = Sum[a_i * cos[(j-1) phi_i] + b_i * sin[(j-1) phi_i];
{j=1,3}]
b_0 can not be determined because sin[0] = 0;
A weight is calculated based on the difference between Y and Y'
m = Median[ Abs[ Y - Y'] ]
U = [ Y - Y'] / m
Four cases are distinguished to calculate the weight
Wi = 1 -R <= U_i <= R
Wi = (1 + (U_i - R)/K)^2 U_i > R
Wi = (1 + (U_i + R)/K)^4 U_i < R
Wi = 0 otherwise
and
0 <= W1 <= 1
0 <= W12 <= 1
A second Fourier series is fitted through the data with each point
weighted
( [Fw]^T [Fw] ) [c] = [Fw] [Yw]
with
[Fw]<i> = [W]<i> . [F]<i>
[Yw]<i> = [W]<i> . [Y]<i>
(each column is multiplied by the weights)
<i> column i
The second Fourier series Y" is calculated with coefficients c
The restored data Yf are calculated from Y and Y" according to
the following rules
Yf_i = Max[Y_i, Min [Y"_i, 1.02 * Max [Y_i-2, .. , Y_i+2] ] ]
If three or more missing values in a row in the original values
then
If (Y_j .. Y_j+k .EQ. missing value) .AND. (k .GE. 2)
Yf_i .. Yf_i+k = missing value
The Fourier adjustment is applied to series of 12 months of NDVI
values at a time with 6 month between consecutive series. The
middle 6 months of each series were selected to obtain a 9 year
Fourier adjusted series according to the following scheme
input (month) 1..12, 7..18, 13..24, ..., 97..108
selected 1.. 9, 10..15, 16..21, ..., 100..108
2. Solar zenith angle adjustment
Input a. Fourier adjusted NDVI, years 1985 - 1990, 1982 - 1984
was not incorporated because of a volcanic eruption in
1982 in Chiapas, Mexico, which affected the NDVI data
around the equator. For the interpolation, the entire
data set was used, because no effect of the volcanic
eruption was found at high latitudes where most of the
needle bearing evergreen forests occur.
b. VEG_CLSS.VGC, see Defires and Townshend (1994a, b)
c. Mask with 2 degree intervals of solar zenith angle.
A guess had to be made about the value of the solar
zenith at the time of overpass of the NOAA satellite,
because this information was discarded during
processing of the GIMMS continental NDVI data set. The
estimation of solar zenith angles was done as follows:
The solar zenith angle was calculated for 10 degrees
in the forward scatter direction (=average angle from
which data are selected by compositing), then an
assumption was made for the average day of the month
from which the composite tends to select the data. It
was decided that the minimum solar zenith angle of 5,
15 and 25th day on average, would be likely to concur
with maximum NDVI conditions for the month.
(especially, in the case of growing or senescing
vegetation, high NDVI is concurrent with low solar
zenith angle). For the calculation of solar zenith
angle, the navigation subroutines of the NOAA/NASA
Pathfinder AVHRR Land project (see James and Kalluri,
1994) were used.
d. Mask with solar zenith angles at noon for the middle
of the month. Two effects are concurrent with solar
zenith angle changes, 1: a change in the NDVI as a
result of illumination geometry, this is the effect we
want to account for, and 2: a decrease in the NDVI as
a result of lower ambient temperatures caused by high
solar zenith angles during late autumn, winter and
early spring. Inclusion of this second effect in the
estimation of solar zenith angle effect is clearly
not desirable. To eliminate this second effect from
our data, we calculated a mask with solar zenith
angles at noon, and only used those values in itme c
above. for which the solar zenith angle at noon was
smaller than 35 degrees.
Procedure
I. Create 6 X 12 monthly masks with stratification according to
land cover class (b) and solar zenith angle interval (c.
masked with d.) for the entire period from which data were
collected
II. For each month calculate Fourier adjusted NDVI-distributions
for classes of equal solar zenith angle interval and land
cover class.
III. Combine monthly distributions of equal solar zenith angle
interval and vegetation class for nine years.
IV. Calculate 98 % value of histogram for each solar zenith angle
interval (for 30 < sza < 60) for the following vegetation
classes.
1 + 6 evergreen broadleaf (used for classes 1,6
and 8)
2 + 3 + 4 + 5 mix deciduous/evergreen broad/needle leaf
(also used for 10)
12 agriculture (used for 7, 9, 11, 12)
for classes with bare soil and bare soil and shrubs (9 and
11) combined calculate NDVI at 2%
V. Plot NDVI 98% against solar zenith angle and fit data with
a non-linear least squares method (routine nls from S-Plus
language, see Bates and Chambers, 1992) for the range of 30
to 60 degrees solar zenith angle.
NDVI_{S,P98}= NDVI_{S=0, P98) - k1 * (S - PI()/6)^k2
S = solar zenith angle (in radians)
S=0 = overhead solar zenith angle
P98 = 98 % of NDVI distribution for a particular vegetation
class
The NDVI_{SZA, P98} is held constant for solar zenith angles
larger than 60 degrees. The NDVI 2% (_02) of classes 9 and 11
is held constant throughout the range (the parameters k1 and
k2 were found not to deviate significantly (at 95 %
confidence level) from 0 and 1, respectively). We removed
three outliers from the 98% data for the agriculture class
(type 12 in VEG_CLSS.VGC). We used criterium residual < - 1.5
median of absolute values of the residuals.
Data are adjusted according to
NDVI_{S=0} = (NDVI_{S} - NDVI_{P2}) * (NDVI_{S=0,P98} -
NDVI_{P2}) (NDVI_{S,P98} - NDVI_{P2}) +
NDVI_{P2}
with NDVI_{S,P98} dependent on class and NDVI_{P2} constant:
IV refers to land cover class, see documentation
VEG_CLSS.VGC.
================================================
vegetation parameters
================================================
1 0.618 0.034 0.2403 2.8216
2 0.686 0.034 0.5529 2.5069
3 0.686 0.034 0.5529 2.5069
4 0.686 0.034 0.5529 2.5069
5 0.686 0.034 0.5529 2.5069
6 0.618 0.034 0.2403 2.8216
7 0.630 0.034 0.1702 1.6091
8 0.618 0.034 0.2403 2.8216
9 0.630 0.034 0.1702 1.6091
10 0.686 0.034 0.5529 2.5069
11 0.630 0.034 0.1702 1.6091
12 0.630 0.034 0.1702 1.6091
IV NDVI_{S=0,P98} NDVI_{P2} k1 k2
Table with NDVI_{S=0,P98} and NDVI_{P2} values for
each vegetation type
3. Interpolation
The missing NDVI data in evergreen needle bearing forests
during winter are a problem for the calculation of snow free
albedo and surface roughness within SiB2. We therefore have to
make an estimate of the "snow free" NDVI for these cases. This
is done by taking the average October value for the period of
1982 - 1990 to represent the NDVI of evergreen needle leaf
vegetation cover in the Northern Hemisphere. For the Southern
Hemisphere we use the average of the March values. We assume
that for these months near the end of the growing season, the
NDVI signal is primarily from evergreen vegetation because most
of the deciduous vegetation has no leaves. We replace missing
values when we find three or more missing values in a row.
(Note: We can estimate up to two missing values with the
Fourier adjustment.) The rules used are:
If (three or more missing data in a row) and (class 4)
then for November until April
NDVI = Max (NDVI, avg of all NDVI values for October for that
particular pixel). Missing October values are excluded from
averaging.
4. Reconstruction of evergreen broadleaf classes
Even though the Fourier adjustment is believed to have removed
a large proportion of the cloud effects in the NDVI data, we
still found fairly low NDVI values over the tropical forests,
which, because of the exponential relationship between FPAR and
leaf area index (LAI), resulted in fairly low estimates of LAI.
This situation improved when we took the maximum NDVI of the
year. This step is clearly a quick fix to the problem, we plan
to use more advanced cloud screening procedures in future data
sets so that cloud problems are caught at an earlier stage.
rule used for Reconstruction
if (class .EQ. 1) then
NDVI = Max (NDVI_jan .. NDVI_dec)
9.2.2 Processing Changes.
With this update of FASIR (release 2.0 some changes were made
with respect to the description of FASIR in the Sellers et al
(1994) paper. These changes are:
- use of a different land cover classification (VEG_CLSS.VGC)
- use of the multi-year NDVI data set to derive the solar zenith
angle correction (1985 - 1990 instead of 1987)
- different assumptions about the solar zenith angle at which data
were collected by the satellite (10 degrees off-nadir in the
forward scatter direction, minimum solar zenith angle of 5th,
15th and 25th day of the month)
- use of Pathfinder navigation software instead of the diagrams
published in Kidwell (1988)
- more selective procedure for selection of data (NDVI data with a
solar zenith angle >35 degrees at noon for the 15th of the month
are not used in the derivation of the technique)
- direct estimation of parameters k1 and k2 (see section 9.2.1)
using a non-linear least squares technique (Bates and Chambers,
1992) instead of estimating k1 and k2 from the transformed data.
9.3 Calculations.
Not available at this revision.
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.
Some sources of error in the NDVI data set are not accounted for with
the FASIR corrections, these errors are caused by:
- variations in off-nadir viewing
- soil background reflectance
- a bias as a result of compositing
- long-term atmospheric effects. (e.g., after volcanic eruptions)
The largest uncertainties in the FASIR correction are in steps 3 and 4,
since for these cases data are adjusted without sufficient information.
Errors (overestimates) may be introduced by the Fourier adjustment in
cases of rapid changes in the NDVI during the beginning and end of
season. The solar zenith angle effect on NDVI is assumed to be linear.
This assumption was not always found to hold true in several ground
based and model studies (e.g., Deering et al., 1992).
10.2 Quality Assessment.
10.2.1 Data Validation by Source.
A rigorous verification of the FASIR-NDVI has still to be done
(Sellers et al 1995b).
10.2.2 Confidence Level/Accuracy Judgment.
The FASIR-NDVI data set is believed to give large improvements
over the NDVI data set, especially for areas with persistent
cloud cover and for needle bearing evergreen vegetation during
winter.
10.2.3 Measurement Error for Parameters and Variables.
Errors are generally systematic and lower the NDVI value. For
dense vegetation cover (NDVI > 0.6), the errors approximately
are cloud contamination reduced by 70 - 90 % atmosphere reduced
if incidental (< 1 - 2 months) off-nadir viewing: +0.02 forward
-0.05 backscatter solar zenith angle: reduced missing data
(assume NDVI = October value for needle, otherwise 0), reduced
misregistration + 0.02 compositing (0 .. +0.05). For the low end
of NDVI, the soil can change the NDVI by ca +0.5 - + 1.5 units.
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
FASIR NDVI presents generalized patterns which may result in poor
representations of a specific locale, quantitative conclusions should be
drawn with caution. Nevertheless, FASIR-NDVI should provide a large
improvement over previously used land cover schemes (e.g. Dorman and
Sellers 1989) 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.
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.
DeFries, R. S. and J. R. G. Townshend, 1994a, NDVI-derived land
cover classification at global scales. International Journal of
Remote Sensing, 15:3567-3586. Special Issue on Global Data Sets.
DeFries, R. S. and J. R. G. Townshend, 1994b. Global land cover:
comparison of ground-based data sets to classifications with AVHRR
data. In Environmental Remote Sensing from Regional to Global
Scales, edited by G. Foody and P. Curran, Environmental Remote
Sensing from Regional to Global Scales. (U.K.: John Wiley and
Sons).
Bates M, and Chambers, J.M., 1992, Nonlinear Models, In: Chambers, J.M.
and Hastie, T.J., Statistical models in S, Wadsworth & Brooks, Ca. pp
421-454.
Deering, D.W., E.M. Middleton, J.R. Irons, B.L. Blad, E.A. Walter-
Shea, C.J. Hays, C. Walthall, T.F. Eck, S.P. Ahmad and B.P. Banerjee.
1992. Prairie grassland bi-directional reflectances measured by
different instruments at the FIFE site. Journal of Geophysical
Research, 97:18,887-18,903.
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.
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, 15(17):3347-3363.
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.
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.
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 Destributed 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 Climotology Project
LAC Local Area Coverage
LAI Leaf Area Index
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)
SR Simple Ratio