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Global Monthly Vegetation CoverReprint of Original Document |
Introduction Page,
5. The present status of GVI data products
The GVI products that have been routinely available at NOAA are B2.0 (daily maps) and B3.0 (weekly composite maps). The work in the GVI project has been based on weekly composites (B3.0). The creation of the third-generation GVI consists of reprocessing the second-generation GVI dataset (April 1985 - present) by the procedures described below and summarized in Fig. 3. Atmospheric, anisotropic and diurnal variability corrections and derivation of surface geophysical characteristics (dashed box) are bypassed in the current work but remain the subject of further research and development. The data products that have recently become available in addition to B3.0 are: B3.1/W15, B3.2/W15, C3.2/M15, and D3.2/M15.
a. GVI weekly products (B3.1/W15 and B3.2/W15)
The counts in AVHRR thermal channels are converted to T4 and
T5 (Kidwell 1991; Kidwell 1994) and corrected for calibration non-linearity (Rao
1993). Reflectances in the solar channels, 
and 
, are calculated
using the updated (Pathfinder) calibration (Rao and Chen 1994), which accounts for sensor
degradation, and then corrected for sun-earth distance. The
NDVI= 
and
PWI=(T4-T5) are calculated at the B3.1 level to retain accuracy since
all B3 product values are packed into eight bits.
Composite imagery in many parts of the world is often cloud contaminated and should be
cloud screened before any geophysical analysis. The B3.2 cloud/clear identification technique
uses T4-thresholds dependent upon month, region, and viewing angle as described
in detail by Gutman et al. (1994). Together with updated calibration, the generation of QC flags
is the major enhancement to the second generation GVI dataset. The B3.2 data products for the
period April 1985 - present include weekly composite maps of the TOA ,
, NDVI, T4, T5,
PWI, the associated solar zenith and
satellite scan angles, and QC flags. The relative azimuth angle ( i.e. the difference between solar
and satellite azimuth angles) needed for atmospheric/ angular corrections is not supplied with the
dataset as a separate file, but is calculated within the reading program appended to the dataset.
An example of a B3.2/W15 product -- weekly composite NDVI map with a QC mask -- is given
in Gutman et al. (1994).
b. GVI monthly products (C3.2/M15)
The C3.2/M15 production flow includes the following steps: 1) QC flags are applied on a
weekly basis, resulting in data gaps, 2) each quantity is averaged over one month for each map
cell to partially fill in the data gaps and reduce some of the angular variability (the number of
cloud-free weeks is stored as an additional map); 3) bilinear spatial interpolation is applied to the
missing data areas with persistent cloudiness in monthly averaged images; 4) 3x3 map cell
smoothing is done to partially account for the imperfection of cloud screening, to filter out
atmospheric and angular variabilities, and to compensate for random spatial sampling from GAC
data into the GVI map cells. The above procedure yields monthly mean values for TOA
,, NDVI, T4, T5, and PWI at
each GVI
map cell for each month of each year. Figure 4 shows examples of C3.2/M15 products -- global
monthly maps of T4 for October 1993, January 1994 and April 1994. The
T4 map represents near maximum rather than daily mean brightness temperature,
because of spacecraft mid-afternoon observation time.
c. GVI monthly climatology (D3.2/M15)
A 5-yr climatology of means and variances at 0.15resolution (D3.2/M15) has been developed
from the C3.2/M15 monthly fields of TOA ,
, NDVI,
T4, T5, and PWI using data from April 1985 to December 1987
(NOAA-9), and from January 1989 to March 1991 (NOAA-11). The NOAA-9 data for 1988 and
most of 1991 onward (after the Mt. Pinatubo eruption) were excluded since no
angular/atmospheric corrections were applied.
Global maps of multiannual means and standard deviations of clear-sky AVHRR-derived
variables have been generated for the first time under the GVI project, although global 3-yr
NDVI statistics have been available during the past year (Hastings and Di 1994). Figure 5 shows
the NDVI 5-yr climatology for July. Despite the short 5-yr base period, the areas with high
interannual vegetation variability are clearly depicted on the map of standard deviations and are
of particular interest to climatologists. The areas in central Siberia, southeast Australia, and
northeast Brazil show high interannual NDVI variability, the latter being attributed to droughts
caused by ENSO during the 5 yr. Figure 6 shows the completion of the annual cycle of the mean
NDVI from Fig. 5. The areas in white (T4< 270 K) are classified as stable
snow/ice and in gray (270 K <T4< 280 K) as transitional (unstable) snow/ice,
both having > 20 %.
Figure 7 shows the July climatology of ,
T4, and PWI. The global and seasonal distribution of PWI agrees with the observed
distribution of precipitable water (Tuller 1968). This suggests a potential for PWI as a measure
of large-scale water vapor distribution over land (see Section 2b). The multispectral GVI data
can be used for deriving other land variables (e.g. LST and albedo) and for improving
classifications of bioclimates based on temporal analysis of NDVI alone (e.g. Tucker et al. 1985;
Townshend et al. 1987).
d. Prototype of a global land monitoring system
A prototype global land monitoring system has been developed based on a Silicon Graphics
UNIX workstations. It is an evolving system, in which some parts can be improved and/or
replaced, and is suitable for routine operations, requiring very little human intervention.
Each week, composite B3.0 data are automatically accessed and copied to a UNIX workstation magnetic disc. The production of B3.1 and B3.2 follows ( i.e. calibration and generation of QC masks). The enhanced weekly product is then appended to the existing NOAA archive. At the beginning of each month, the previous five B3.2 weekly products are processed at the C level (i.e. the QC flags are applied and monthly averaging, interpolation and smoothing are carried out). Then, the monthly standardized anomalies are generated and made available for climate analysis and interpretation. Display of the seasonal and year-to-year dynamics on a global scale of the derived land products is now possible by means of the animation loops on workstations using a menu-driven program and the Interactive Data Language (IDL) software.
An example of the standardized anomalies 
for July over the United
States during 1985-1994 (Fig. 8) illustrates the multiannual potential of the GVI products. (Note
that the positive and negative anomalies for the whole time series are not mutually balanced
since the base period for
climatology development includes only five of the nine presented years.) In general, the derived
anomalies compare well with the maps of the Palmer drought index produced by NOAA and the
U.S. Department of Agriculture, except for some special situations. For example, the areas,
where the 5-yr mean NDVI estimates are biased, are not easily interpreted, particularly in the
west, where PDI indicates dry conditions during all five yr. During most of the NOAA-11
observational period, dry areas in the west also show a "greening" trend, which we attribute to
residual calibration error and which seems to be partially compensated for by the sun-angle effect
in 1994 (Gutman and Ignatov 1995). Further, note that both moisture deficit (droughts) and
excess (floods) are associated with lowered NDVI. The "Great Flood" of 1993 in the area of
Iowa
and the flooding of 1994 in northern Florida appear as patterns of negative anomalies in Fig. 8.
Additional information, such as thermal IR and/or microwave measurements should be used to
distinguish insufficient from excessive moisture conditions. The derived temperature anomalies
are affected by the diurnal variability of
the observed brightness temperature as a result of
satellite orbit drift (see Section 4), which presents difficulties in monitoring AVHRR temperatures (Gutman and Ignatov 1995). Corrected temperature time series from AVHRR and/or data from other sensors should alleviate this problem.
e. Remaining uncertainties and potential enhancements
All of the processing levels have potential for improvement. The calibration and cloud
screening should be refined. The B3.3-B3.5 products are yet to be generated. The use of optimal
averaging and interpolation on the Clevel is preferable to fill in the data gaps. These, however,
require the spatial/temporal correlation functions, which are not available now. The D level can
be improved by using longer time series and developing other statistics.
tropospheric aerosol effects. Several studies have shown that much of the contribution to angular variability in the observed radiances is due to surface anisotropy (Roujean et al. 1992). Thus, even if the atmospheric corrections are made, it is unclear how the results could be interpreted. A single globally applicable correction is not possible due to the variability of surface effects in space
and time. Models of only a few vegetation types are unlikely to be generic because of topography and mixture of vegetation types. Surface and atmospheric diurnal variability is most pronounced in the thermal data, resulting from satellite orbit drift. Normalization to common observation time is needed for monitoring interannual variability in surface temperature.
infeasible, an alternative approach has been proposed for developing TOA regional empirical angular/diurnal variability functions (REAF) for each limited region of the global land surface for limited periods - for example, monthly (Gutman 1994a,b). (Note that diurnal variability can be expressed as sun-angle dependence for a given latitude; geostationary satellite data could be used for developing this dependence.) The number of the REAFs may be reduced using cluster analysis at a later stage. The REAFs are in turn used to normalize all data to a common sun-target-sensor geometry. Pilot studies indicate that the time series become more stable after TOA normalization (Gutman 1994b; Ba et al. 1995). The effective elimination of angular biases allows more reliable interpretation of the results and widens the area of applicability for analysis. The methodology has to be improved and tested further before its global development is undertaken.
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