3.3.3 LTSK9Vegetation Index
A. Algorithm Outline(1) Algorithm Code:LTSK9(2) Product Code:

VGI(3) PI names:

G18 Dr. Alfredo Huete(4) Overview of algorithm (Standard level)
Algorithm objectives
The algorithms has the following objective :• To compute two vegetation indices (VI's) from the atmospherically corrected andcomposited reflectances;• The normalized difference vegetation index (NDVI) and• The enhanced vegetation index (EVI).
• To produce global vegetation index maps on a consistent temporal (16 days) andspatial (1km) resolution
B. Theoretical Description(1) Methodology and Logic Flow
A general flowchart describing the vegetation index algorithm is presented in figure 1.
The vegetation index algorithm will process atmospherically corrected data generated by LTSK
1. Both normalized difference vegetation index (NDVI) and the enhanced vegetation index(EVI) maps will be produced for this phase.

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Figure 1: Flow diagram for Vegetation Index (LTSK 9).

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(2) Physical and Mathematical Aspects of the Algorithms
GLI vegetation index products
Operational monitoring of the earth’s vegetative cover currently involves the utilization ofspectral vegetation indices (VI's) as precise radiometric measures of spatial and temporal
patterns of vegetation photosynthetic activity, vegetation condition, and vegetation change. VI's
are designed to measure spatial and temporal changes in vegetation at regional, continental, andglobal scales on an inter-annual and seasonal basis. They are also used as intermediaries in theassessment of various plant biophysical parameters, such as leaf area index (LAI), percent green
cover, green biomass, and fractional absorbed photosynthetically active radiation (fAPAR).
Since their role is to measure 'variations' or changes in vegetation, they are not restricted to themeasurement of a single parameter, such as LAI. Vegetation indices are thus highly useful in thedetection of any type of vegetation change, regardless of type of change, whether it is a change
in biophysical parameter, species composition, stress, land -use, or a combination of change
factors.
The compositing approach developed here allows for the computation of numerous
vegetation indices, in that it is the reflectances that are composited in a consistent fashion. The
computation of the vegetation index then becomes straightforward and unlike other approaches,
one can compute and implement different VI's at local, regional, or global scales. Two different
VI's are proposed for the GLI instrument; one is a sta ndard, normalized difference vegetationindex (NDVI) and the other is an enhanced vegetation index (EVI). The NDVI, which is theratio between the difference in the red and near- infrared and their sum, has been the most widelyused index in global vegetation studies, including phenological studies of vegetation growing
season; land cover classification; and global climate system models (Goward et al. 1985;
Townshend et al. 1994; Tucker and Seller 1986; Malingreau et al. 1989; Justice et al. 1985;
Sellers et al. 1994; Townshend et al. 1991). These studies have shown a qualitative
correspondence of differences in NDVI with observed or recognized variations in vegetation
growth. With appropriate 'translation coefficients', the NDVI global data set acts as a continuity
index allowing one to take advantage of a 25 year AVHRR-NDVI data record as well as tocouple a GLI-NDVI with a MODIS-NDVI, etc. The translation coefficients involve
simultaneous measurements of NDVI over validation test sites and take into account differences
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in sensor bandwidths, center wavelengths, and calibration accuracies.
The NDVI is a scaled, non-linear transform of the simple ratio (SR = NIR/red), originallydeveloped to enhance the vegetation signals over sparsely vegetated rangelands (Deering,1978);NDVI = (ρ nir -ρ red) / (ρ nir +ρ red ).

(1)

The NDVI retains certain favorable ratioing properties, which reduce noise and uncertainty
associated with instrument characteristics and external sources of noise (e.g., cloud shadows).
There are also certain disadvantages, including non-linearity and scaling problems; an
asymptotic (saturated) signal at the high biomass end of vegetated canopies (figure 2), andsensitivity to canopy backgrounds (soil, litter, snow…) over open canopy conditions (Huete etal., 1997).

Figure 2: NDVI saturation.
Progress toward improved monitoring of vegetation patterns and biospheric processes ispartly restricted by limitations of the sensor systems and partly by inherent limitations of using
empirical or semi-empirical spectral indices (Goward et al. 1991; Myneni et al. 1995). Goward
et al. (1991) state that errors of ±50% are possible with the AVHRR-NDVI product owing tosuch factors as poor sensor calibration, poor image registration, variable angular- induced pixel
sizes, and incomplete cloud screening.
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We also propose the use of a new 'enhanced vegetation index' (EVI) for increased
sensitivity over a wider range of vegetation conditions, removal of soil background influences,and removal of residual atmospheric contamination effects present in the NDVI. The soil
background adjustment is based on the soil adjusted vegetation index (SAVI) (Huete 1988;
Huete et al. 1994); which optimizes the NDVI by accounting for differential red and nearinfrared extinction through a photosynthetically active canopy via Beer's law (Fig. 3);

NIR

5.0 (0 .6 6 )3 . 0 ( 0 . 50 )2 . 0 ( 0 .3 3 )1 .5 (0 .2 0 )

PVI
A

20

1 (0 )

C

15

D

B

DRY SOIL

105

RATI O(NDVI)WET SOIL

Red

l2
E

l1

Figure 3. Optimization of the NDVI for canopy background correction via use of vegetation
isolines in accordance with Beer's law.
An atmospheric resistant term, derived from the atmospherically resistant vegetationindex (ARVI) (Kaufman and Tanré 1992) is included to correct for variable aerosol effects in the
red band on a pixel by pixel basis. The ARVI utilizes the difference in radiance between the blue
and red channels, via a γ function, to correct the radiance in the red channels and stabilize theindex to temporal and spatial variations in gaseous and particulate pollutants. The enhanced
vegetation index (EVI) was developed to optimize the vegetation signal from deserts torainforests while minimizing aerosols and canopy background sources of uncertainty (Liu andGLI Algorithm Description

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Huete, 1995). The feedback-based structure of the equation takes the form:

EVI = 2.5 (ρ nir -ρ red) / (L + ρ nir +C1 ρ red - C2ρ blue),(2)

where L is the canopy background and snow correction caused by differential NIR and redradiant transfer (transmittance) through a canopy; and C1 and C2 are the coefficients of theaerosol ‘resistance’ term, which uses the blue band to correct for aerosol effects in the red band
(Kaufman and Tanre, 1992). This equation is not as effective as the NDVI in 'ratioing' bands toremove noise, and thus is susceptible to the noise and uncertainty remaining in instrument
calibration and in the derivation of atmospherically-corrected reflectances. However, aerosol
variations are considerably reduced via the self-correcting combination of the red and blue
channels and today's sensors, such as GLI, as less prone to instrument noise compared with the
AVHRR. The currently used coefficients, L = 1; C1 = 6; and C2 = 7.5, are fairly robust andhave been applied to Landsat TM, ground observation data, and simulated canopy model data
(Liu and Huete, 1995, Huete et al 1997).
Huete et al. (1996) found that the EVI can refine global vegetation studies by extending therange of sensitivity of the VIs into both•

Densely vegetated, forested regions, and•Sparsely vegetated, arid and semi-arid regions.

Figure 4 shows the performance of both NDVI and EVI over a wide range of conditions
including:•

Vegetation temporal dynamics•

Atmosphere condition (clouds and aerosol)•

Varying viewing geometry

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Figure 4: VI performance over variable atmosphere conditions, viewing geometry, andvegetation. The stability of EVI is due in part to its atmosphere resistance

Increased sensitivity in densely vegetated areas was accomplished by placing more weight to the
NIR reflectance component of the EVI equation. The less absorbing and greater penetration andscattering properties of the NIR enables increased sensitivity to gree n biomass and prevents VI
saturation at the highest levels of vegetation (Figure 5). In this figure we see the saturation of the
NDVI over densely vegetated, forested areas depicted in the form of histograms, where the
NDVI has clearly saturated while the EVI maintains a normal distribution which better
represents the large range in LAI values in this scene (LAI = 2 - 8).

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Figure 5. NDVI and EVI histograms of a MODIS scene over South America
Changes in land use, land cover, and net primary production are difficult to detect in a‘saturated’ mode (Townshend et al. 1991, Huete et al, 1997). An index, such as EVI withextended, linear response over a wider range of vegetation conditions would not only minimize
the ‘saturation’ problem but would also allow for more accurate aggregation and scaling ofmulti-resolution data sets. In Fig. 6, an example of the difficulty in detection of land use changes
with a 'saturated' NDVI is shown relative to the SAVI and EVI.

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Figure 6. 250 m simulated imagery from Landsat TM over Amazon Forest showing land use
clearing in the top portion of the image.

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The aerosol correcting methodology is shown below for the EVI (Fig. 7):(a) Color composite of smoke plume in Oregon TM image.

(b)

(c)

NDVI image of Oregon smoke plume.

EVI image of Oregon smoke plume.

Figure 7. NDVI and EVI images of a smoke plume in Oregon showing aerosol correcting ability of the EVI.

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The GLI VI products will be spatially and temporally re-sampled, and designed to provide
cloud free vegetation index maps at nominal resolutions of 1 km. The composited surface
reflectance data from each pixel is used to compute both the NDVI and the EVI gridded products.
The bands used to compute the VI are as follows:

Red bandGLI band 13(673-683 nm, 1km band)

NIR bandGLI band 19(860-870 nm, 1km band)Blue band

GLI band 5(450-470 nm, 1km band)

These bands are much narrower than the 250m equivalent bands, and provide increased
chlorophyll sensitivity (band 13) and avoids water vapor absorption (band 19). The blue bandprovides aerosol resistance in the EVI.
The gridded VIs is produced at 16-day (half- month). Monthly gridded VI products will also be
produced but based on temporal averaging of the 16 days products.
C. Practical Considerations
In this section we shall explore some of the proposed algorithms aspects with respect toimplementation, programming and data interfacing.
(1) Programming, Procedural, Running Considerations
1.1 Compositing and Vegetation Index Algorithm Implementation
Due to the flow of data within the land processing algorithms (figure 1), the compositing
algorithm (LTSK 10) executes first and generates one data set representative of the composite
period (16 days). The atmosphere correction algorithm (LTSK 1) follows and generates theatmospherically corrected surface reflectance product for Rayleigh and Ozone.

Once thiscorrected surface reflectance data is present (L2A_LC) the vegetation index algorithm (LTSK 9)
executes and generates the same data set with the addition of vegetation indices (NDVI, EVI).
This algorithm is simple, in that most of the work was performed earlier by LTSK 10, and LTSK
1. Hence, it only computes both NDVI and EVI and appends them to the L2A_LC standard
product.
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Figures 8 to 12 are 16 day prototype product of the global GLI-NDVI/EVI , they are theresult of applying the GLI-MVC/CMVC algorithm to a set of SeaWiFS and MODIS daily global
data. We note that the SeaWiFS data is corrected for Rayleigh and ozone absorption and has aresolution of 8x8 km, covering the entire globe, MODIS on the other hand is corrected for all
atmospheric contaminations including aerosol.

These prototyping exercises show favorable

results and indicate that the algorithm is performing very well and as designed.
1.2 Numerical and computation considerations
As described above, the LTSK 9 algorithm is a continuation of the LTSK 10 algorithm.
Atmospherically corrected reflectance resulting from the LTSK 1 algorithm are simply used tocompute NDVI and EVI. The LTSK 9 algorithm will process each L2A_LC scene and theresulting product archived. This algorithm will run once every 16 days on the GLI gridded data
sets. Computationally this algorithm is not demanding because LTSK 10 carried the most
crucial work of compositing the data earlier and the LTSK 1 performed the atmospherecorrection.
(2)

Calibration and Validation

2.1 GLI VI prototyping with SeaWiFS data
A month's worth of daily SeaWiFS data (September 1997)were obtained from the GSFC
(Nazmi Saleous, 1998) to test and prototype the GLI vegetation index compositing algorithm.
The daily data was mapped to a global 8 km equal area grid, resulting in global spatial
dimensions of 2500 by 5000 pixels (Rows and Columns). The SeaWiFS data was in the
Hierarchical Data Format (HDF) and consisted of 11 data fields (Reflectances, Geolocations). It
is important to mention that GLI consists of 9 land bands (1km band) and in the processes togeneralize our algorithm we copied the 8th SeaWiFS band to create the 9th GLI band. The
reflectance data was corrected for Rayleigh scattering and ozone absorption. Cloud flags were
produced during the execution by using a maximum BLUE band of 0.25 (Any data above the0.25 mark is considered cloudy). The land/sea mask was an ancillary data layer acquired with theglobal data sat. More specific information on the SeaWiFS data can be found on the NASA
internet home page.
Daily, global 8 km SeaWiFS data were processed to prototype the GLI-MVC and GLIconstrained view angle MVC (CMVC) vegetation index compositing algorithms. Both NDVIGLI Algorithm Description

3.3.3-12and EVI were produced. Figures 8 - 11 depict the results of this prototyping technique.We notealso that the curre nt algorithm has built in flexibility to assess the differences between MVC and
CMVC, in conjunction with the cloud cover.

Figure 8. NDVI composite: GLI MVC algorithm prototype with SeaWiFS data set.

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Figure 9. EVI composite: GLI MVC algorithm prototype with SeaWiFS data set.
2.2 GLI VI prototyping with MODIS data
We extensively used MODIS data (available since April 2000) to prototype the GLI vegetationindex and compositing algorithms. Due to the science similarity of the MODIS and GLI
compositing algorithms, the results of this prototyping were very comparable (figure 12-15).

Figure 10: GLI NDVI, CV-MVC prototype using MODIS data. (Equal-rectangular projection)

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Figure 11: GLI NDVI map of the Japanese islands (prototype with MODIS data)
2.3.

Validation of the GLI Vegetation Index productThere are two 'distinct' VIs, designed to measure different properties of the vegetation

canopy. There is the NDVI, which is very useful in deriving two-dimensional vegetation
parameters such as fAPAR and percent green cover; and there is the enhanced vegetation index(EVI), which is strongly correlated with canopy structural parameters, such as leaf area index(LAI). These two VIs are unique in that the NDVI will quickly 'saturate' under high biomass
conditions (as does %cover and fAPAR) of agriculture and forests. The EVI does not saturate
and maintains sensitivity throughout the Earth's biomes, including tropical rainforests. Because
the EVI is sensitive to structural parameters, it is easy to detect land use changes/conversions as
well, as in the Amazon deforestation (the NDVI is useless and saturates out). Furthermore, the
EVI is not sensitive to canopy background and can be designed to remove (minimize) aerosol
influences. This is important since GLI will not have aerosol corrections over land (only oceans).
We have used 'real' ground radiometric data over various crops and grasses, including
corn, cotton, sorghum, and wheat. We have also created a Landsat TM-based global biome data
set and have tested/developed our algorithms over all biome types such as desert, grasslands,
woodlands, forests, etc..., with snow conditions, lakes, and burning (smoke) conditions. Finally,
we are using the SAIL and DISORD (Myneni) canopy radiant transfer models to verify andunderstand the nature of the algorithms.
There are two components to the validation of the VI:(2) First there is a radiometric validation since the VIs are unitless and dependent on thequality of the input (reflectance) parameters;(3) secondly, there is the biophysical validation of the VIs or the outputs of the VI equation to see that the VI really measures a change in vegetation amount and that sensitivity is
maintained throughout the wide range of environmental conditions on the planet.
For the radiometric part, we are using field radiometers that can be flown on light aircraft(or helicopter) just above the canopies. These need not be expensive and can be non- imaging
spectral profilers (radiomete rs). We also need the radiometer to measure background reflectanceproperties, understory and soil, and canopy transmittance (in forests). For the biophysical part,

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we plan to implement LAI and fAPAR sensors, like ceptometers, quantum sensors, and LAI
LiCOR-2000 instruments.
2.3.1 Validation procedure:In the radiometric sense, we will validate by ensuring that the top-of-canopy reflectances/
VI’s agree with that from the GLI sensor. The top-of-canopy values encompass the average ofairborne, linear transects of at least 1 km length. The 1 km transects encompass a homogeneous
patch of the dominant land cover type. Additional transects are flown beyond the homogeneoussite to cover the natural heterogeneity of any given 10 km by 10km area, including‘endmembers’. The measurements are made (helicopter/aircraft) at below 150 m altitude to
avoid atmospheric effects. Nadir values are obtained and matched with those from GLI(adjusted to nadir view pixels). The measurements are made prior to and after the time of GLI
overpass to ensure solar zenith angle correspondence. The airborne radiometers are calibrated
and extended to GLI sensor responses via simultaneous measurements made at bright and dark,
uniform, non-vegetated sites. Sun photometers are needed to characterize and correct for theatmosphere. The figure below shows the Radiometric validation component.

MODIS, GLI, (ETM, ASTER,
MISR, Vegetation)

Atmospheric Correction

TRANSFER FIELD
RADIOMETERS
GLI/MODIS radiometers

Level 1b Calibration
radiometers(Air & Ground)

Uniform Surfaces(Railroad Playa, ...)

GLI/MODIS Tests Sites

Figure13: Radiometric validation component

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In a biophysical sense, 1km transects of biophysical measurements need to be collectedon the ground. Percent cover is obtained from digital aerial photos (on the same aircraft/
helicopter with the radiometer). Leaf area index (LAI) is measured through linear transects of 1
km length with LiCOR-LAI 2000. The error and uncertainty of the LAI measurements are to beobtained from smaller subplots involving direct LAI leaf sampling. LAI is also measured
outside the homogeneous site at lower and higher LAI conditions, so that a relationship curve
between VI and LAI can be plotted for that biome type. All measures are to be taken over awindow of standard solar zenith angles.
For the fAPAR measures, ceptometers and quantum sensors are used over a set of limited
subplots within the 1 km LAI transects. Measurements of fAPAR are made over a large range ofsun angles (entire morning or diurnally), since fAPAR is very sensitive to sun angle and because
fAPAR will be used by the NPP product which requires ‘daily’ fAPAR values.
Biophysical measures need to be made over the following land cover types (biomes),each encompassing a range of low to high vegetation amounts.
•

Shrublands (hyper-arid to semi-arid)•

Grasslands (semi-arid to humid)•

Cereal and broadleaf crops (entire growing season)•

Savannas (woodlands) low to dense trees•

Broadleaf Forests (temperate and tropical regions)•

Needleleaf Forests•

Mixed Forests

2.3.2 Validation test sites (preliminary)
At minimum one site is needed per biome type listed above. Ideally two or three sites perbiome are desired to ensure enough global variability to develop a global validation and error/
uncertainty analysis. We also need a couple of ‘baseline’ sites with no vegetation, like Atacama,Gobi, Sahara, etc... These sites are needed to monitor sensor degradation and drift as well as toreadily calibrate/match GLI sensor responses with field instrumentation. Validation work is
being conducted in cooperation with Japanese validation teams, and in conjunction with the
MODIS validation effort.

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(3) Quality Control and Diagnostic Information
Applying complex algorithms to remote sensing data will increase its uncertainty, theamount of noise, and possibly create values that are not accurate. The idea of implementing
QA/QC checks evolved from previous work with AVHRR. On a pixel basis data will becharacterized by its cloudiness, state of the sensor during on board registration, georeferencing
problems, performance of the algorithms that generated the data, all of which contribute to thefinal quality. For instance the computation of the VIs uses spectral responses at different
positions, it also involves simple difference and rationing, which can lead to floating point andoverflow errors. These types of errors are captured during execution and appended to the data as
quality assurance flags that could be used to assess the usefulness of the product and tocharacterize its validity (figure 14).

Fill value (no landor failure)
Solar zenith angle >65
50% of Daily data is
missing
View zenith < 45
Highest quality
High solar and view

Figure 14: GLI prototype quality layer(4) Exception HandlingAs described in the previous sections, a solid scheme for capturing error and conveying
them to the end us er is implemented. During execution, all computation errors, out of range data,
and missing inputs are passed to the user via pre-defined fill values and quality flags. In case ofalgorithm failure, the processing code will generate messages that indicat es the source and
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possible remedies to the errors.
(5) Constraints, Limitations, Assumptions
Although the algorithm was designed and optimized for processing GLI data, some of theassumption in the design were based on other sensor (MODIS, SeaWiFS, and AVHRR). The
performance and accuracy of the algorithm have so far been measured using data from both
SeaWiFs and MODIS, some of them are:•

GLI bands used for VI computation are much narrower and could exhibit excessive
sensitivity•

The vegetation indices are computed based on atmosphere correction performed oncomposited data, this idea was never tested in operational mode.

•

The orbital coverage of GLI (4 observations at the equator) could affect the resulting VI
product because of the persistence of clouds in that region. Usually more data is requiredin these regions to effectively remove clouds.

Overall, we expect the current algorithm to perform very well. All prototyping indicates that
EVI is expected to be a robust global vegetation index, owing to its atmosphere resistance (whichis crucial in the case of GLI given the lack of aerosol correction) and its increased sensitivity tovegetation variability.
(6) Suggestions and Recommendations
Since data is only corrected for Rayleigh and Ozone and the difference of the bands used, theresulting GLI VI will most likely deviate from those produced by MODIS. It is then importantto take this into considerations when performing across sensor comparison of results. Moreover,the compositing scheme is performed prior to atmosphere correction, this will further complicate
any translation between sensors. Nonetheless, this GLI processing scheme has its advantages:•

Simpler data processing•

MVC/CV-MVC should perform better on non-atmospherically corrected data•

Continuity with AVHRR is easily established due to the similar atmosphere correction
scheme.

We recommend that the validation of the atmospherically corrected surface reflectance product

GLI Algorithm Description

3.3.3-19should be rigorous. This will insure its validity and quality along with that of the GLI VI.

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