2-band Enhanced Vegetation Inde x without a blue band and ......The MAD also varies with , with...

2-band Enhanced Vegetation Index without a blue band and its application to AVHRR data

Zhangyan Jiang*, Alfredo R. Huete, Youngwook Kim, Kamel Didan

Department of Soil, Water, and Environmental Science, University of Arizona, Tucson, AZ, USA 85721

ABSTRACT

The enhanced vegetation index (EVI) has been found useful in improving linearity with biophysical vegetation properties and in reducing saturation effects found in densely vegetated surfaces, commonly encountered in the normalized difference vegetation index (NDVI). However, EVI requires a blue band and is sensitive to variations in blue band reflectance, which limits consistency of EVI across different sensors. The objectives of this study are to develop a 2-band EVI (EVI2) without a blue band that has the best similarity with the 3-band EVI, and to investigate the cross-sensor continuity of the EVI2 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). A linearity-adjustment factor (β) was introduced and coupled with the soil adjustment factor (L) used in the soil-adjusted vegetation index (SAVI) in the development of the EVI2 equation. The similarity between EVI and EVI2 was validated at the global scale. After a linear adjustment, the AVHRR EVI2 was found to be comparable with the MODIS EVI2. The good agreement between the AVHRR and MODIS EVI2 suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another long-term vegetation record different from the NDVI counterpart.

Keywords: Vegetation indices, EVI, EVI2, MODIS, AVHRR, cross-sensor continuity

1. INTRODCION There are currently two vegetation index products generated with data from the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). The enhanced vegetation index (EVI) was developed to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences:

LBCRCNRNGEVI

+−+−

=21

(1)

where N, R, and B are atmospherically corrected or partially atmosphere corrected (Rayleigh and ozone absorption) surface reflectances in near-infrared, red and blue bands respectively1. The coefficients adopted in the EVI algorithm are, L=1, C1=6, C2=7.5, and G (gain factor) =2.5.

The EVI not only gains its heritage from the soil-adjusted vegetation index (SAVI)2 and the atmospherically resistant vegetation index (ARVI)3, but also improves the linearity with vegetation biophysical parameters and enhances sensitivity over highly vegetated areas, encompassing a broader range in leaf area index (LAI) retrievals4.

Since the role of the blue band in the EVI does not provide additional biophysical information on vegetation properties, but rather is aimed at reducing noise and uncertainties associated with highly variable atmospheric aerosols, a 2-band

*[email protected]; phone 1 520 621-1791; fax 1 520 621-1791; tbrs.arizona.edu

Remote Sensing and Modeling of Ecosystems for Sustainability IV, edited by Wei Gao, Susan L. Ustin,Proc. of SPIE Vol. 6679, 667905, (2007) · 0277-786X/07/$18 · doi: 10.1117/12.734933

Proc. of SPIE Vol. 6679 667905-1

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adaptation of EVI should be compatible. The development of the EVI2 would enable extension to remotely sensed data from instruments without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), for cross-sensor applications by generating a backward compatibility of the EVI to the historical AVHRR record, thus complementing the NDVI long-term record. The purpose of this study are to develop and evaluate a 2-band EVI, without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good, and to investigate the cross-sensor continuity between the MODIS EVI2 and the AVHRR EVI2.

2. DERIVATION OF THE EVI2 Huete2 found that vegetation biophysical isolines in NIR-red reflectance space, i.e., lines of constant vegetation amount but varying pixel brightness, are neither parallel to a soil line, nor converge at the origin as in the case of NDVI isolines, but instead, approximately converge at a point E(- l , - l ) on a soil line (Y=X) shifted from the origin in the negative direction (Fig. 1). Shifting the origin toward negative values is equivalent to adding an offset or constant, l , to the red and NIR reflectance, such that the simple ratio (SR) and the NDVI become

lRlN

++ (2)

and

lRNRN

2++− , (3)

respectively2. In order to maintain the amplitude of Eq. (3) as that of the NDVI, a gain, (1+L), is multiplied to Eq. (3), such that the SAVI equation is obtained, where L=2 l . In this paper, Eq. (2) is denoted as a soil-adjusted SR (SASR). The L value is usually determined as 0.5 and thus l =0.25.

β

α

-l

-l

Soil li

ne Y

=X

Isolin

e

Red reflectance

NIR

refle

ctan

ce

γ

E

(R, N)

Fig. 1. The isolines of the SAVI and the soil-adjusted simple ratio (SASR) and their angle in red-NIR reflectance space.



0 0.2 0.4 0.6 0.80

0.2

0.4

0.6

0.8

1

1.2

1.6

2

2.4

SAVISASR1:1 line

EVISA

VI

SAS

R

Fig. 2. Relationships between the EVI with the SAVI and SASR generated from MODIS data as described in section 3.

Fig. 2 presents the relationships between EVI with SAVI and SASR generated from high quality MODIS data with low aerosol quantities (data description is in section 3). Both the SAVI and the SASR are not linear related to the EVI across all vegetation density levels. The SAVI is less sensitive than the EVI, but, on the contrary, the SASR is more sensitive than the EVI when EVI values are high. A VI lies between these two VIs is expected to be linearly related to the EVI.

Recently, Jiang et al.5 showed that the SAVI can be expressed as,

( ) ( )αtan1 LSAVI += (4)

where α is the angle between the soil line and a SAVI isoline as indicated in Fig. 1. The SASR also can be expressed as a tangent function of an angle,

( ) ( )4tantan παγ +==SASR (5)

where γ is the angle between a SASR isoline (same as the SAVI isoline) and the horizontal line across point E (Fig. 1). Thus a Linearized vegetation index (LVI) comparable to the EVI could be obtained by adjusting the constant angle π/4 in Eq. (5) to a variable angle β,

( ) ( )βαβ += tanLVI (6)

where β describes a line across E deviating from the soil line in the clockwise direction in Fig. 1. The LVI value of the soil line, Y=X, (LVI0) is

( )βtan0 =LVI (7)

By subtracting LVI0 from Eq. (6) and multiplying a gain, G΄, in order to maintain the amplitude of the LVI as that of the EVI, the LVI becomes

( )[ ]

( )( ) ( )ββπ

ββα

tan14tan

tantan

−+++−

=

−+′=

LRNRNG

GLVI (8)

where

( )ββ

tan1sec2

−′

=GG (8-1)

β functions as a linearity-adjustment factor since the linearization of the LVI with respect to a VI or a biophysical parameter could be achieved by adjusting the value of this angle. With optimal β, L and G, the differences between the



LVI values and the EVI values would be very small when atmospheric effects are insignificant and no snow and residual cloud are present in pixels, and this optimal LVI is denoted as the 2-band EVI, i.e. EVI2 in this paper.

3. DATA AND METHODS 40 globally distributed sites, representing a wide variety of land cover conditions were selected. The description of these sites can be found in Jiang et al.6. MODIS ASCII subsets of 1 km, 16-day composite Vegetation Index product (MOD13A2), from Collection 4 and the Terra platform, were extracted over the 40 sites, from 18 February 2000 to 19 December 2005. A window of 3×3 pixels, centered on the location of each site, was used to extract red, NIR and blue reflectances over each site. In order to minimize blue band effects on the EVI, only good quality pixels were used to generate the average reflectances for each site at 16-day intervals, from which the VI values were calculated. We defined good quality pixels as those with VI usefulness index≤0010 (the best 3 levels among 16 quality assurance (QA) levels), aerosol quantity≤01 (low aerosol quantity), no mixed clouds, no snow/ice, and no shadow. In total, 2898 measurements, or 53.67% of the measurements were of acceptable QA and then used in the determination of the optimal parameters in the EVI2 equation (Eq. 8).

The mean absolute difference (MAD) between EVI and EVI2 was used as a measurement of similarity. For a given combination of L and β, there is a single, optimal G that minimizes MAD between EVI and EVI2. So the optimal G is a function of L and β and the minimized MAD was calculated as a function of L and β. The coefficient of determination (R2) between these two indices was also used as a reference of similarity since an optimal EVI2 should be linearly related to the EVI, which is also a function of β and L, but independent on G.

4. RESULTS The MAD between EVI and EVI2 decreases rapidly when L increases for 0 to 0.5, and increases for higher L (Fig. 3a). The MAD for the SAVI case at point β=0, L=0.5 in Fig. 3(a) is much smaller than the MAD for the NDVI case at point β=0, L=0. As shown in Fig. 1, β varies between 0 and π/4, corresponding to the SAVI and SASR cases, respectively. The MAD also varies with β, with intermediate β values resulting in a smaller MAD. The minimum MAD is achieved when β=22.3801º (tan(β) = 7/17) and L=0.5882. The optimal G value corresponding to the minimum MAD is 2.507 (Fig. 3b). The R2 between the EVI and EVI2 increases rapidly when L increases from 0 to 0.5, and then decreases for higher L values (Fig. 4). The SAVI has a much higher R2 with the EVI than the NDVI, and the resulting R2 between the EVI and EVI2 with the optimal parameters is 0.9986, very close to the maximum R2 values in Fig. 4, indicating a strong linear relationship between these two indices.

0 5 10 15 20 25 30 35 40 45Beta (degrees)

0

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

NDVI

SAVIEVI2

0.0033

Soi

l-adj

ustm

ent f

acto

r (L)

SASR

0 5 10 15 20 25 30 35 40 45

Beta (degrees)

Soi

l-adj

ustm

ent f

acto

r (L)

0

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

EVI2

(a) (b) Fig. 3. (a) The mean absolute difference (MAD) between the EVI and EVI2 and (b) the optimal G as functions of β and soil adjustment factor (L).



0 5 10 15 20 25 30 35 40

0.99868

Beta (degrees)

Soi

l-adj

uste

men

t fac

tor (

L)

0

2.00

1.00

0.25

0.50

0.75

1.25

1.50

1.75

SAVI

NDVI

EVI2

SASR

45

Fig. 4. Coefficient of determination (R2) between the EVI and EVI2 as a function of β and soil-adjustment factor (L).

For simplicity, G in the EVI2 equation can be set to 2.5, and with the optimal parameter values, the EVI2 equation (Eq. 8) becomes

14.25.22

++−

=RNRNEVI (9)

5. VALIDAITON 5.1 Global vegetation index images

(a)



Ocean-0.2 to 0.1-0.1 to -0.05-0.05 to 0.00.0-0.050.05-0.0750.075-0.10.1-0.1250.125-0.150.15-0.20.2-0.250.25-0.30.3-0.350.35-0.40.4-0.450.45-0.50.5-0.60.6-0.70.7-0.80.8-0.90.9-1.0

(b)

Fig. 5. Comparison of global MODIS 1 km, 16-day composite Vegetation Indices during Jul. 27-Aug. 11, (DOY 209-224) 2000 composite period, (a) EVI, (b) EVI2.

The global EVI2 image exhibited similar spatial patterns and magnitudes in values of global vegetation conditions as the EVI image (Fig. 5), with both VIs depicting eastern North America, northern South America, and parts of the East Asia with the highest VI values, and Europe and North Asia with intermediate VI values. Over some tropical areas, such as south Asia, the west coast of Africa, and South America, the EVI was slightly larger than EVI2. This is possibly caused by presence of residual mixed clouds in these areas, which result in blue reflectance and EVI artifacts.

5.2 Histogram of vegetation indices

0

0.4

0.8

1.2

1.6EVIEVI2NDVI

Per

cent

(%)

VI

Global QA-accepted data7/27/2000

0 0.2 0.4 0.6 0.8 1.0-0.2

0

20

40

60

-0.04 -0.02 0 0.02 0.04

4/22/007/27/0010/31/001/17/01

Per

cent

(%)

EVI2-EVI

Global QA-accepted data

(a) (b) Fig. 6. Comparison of (a) MODIS EVI, EVI2 and NDVI histograms, and (b) EVI-EVI2 difference histograms using QA-accepted data.

The global histograms of the vegetation indices for the Jul 27- Aug 11, 2000 composite period are shown in Fig. 6 (a) for QA-accepted cases. The global NDVI histogram has two peaks, one at 0.1 and another at 0.88. The histograms of global



EVI and EVI2 are consistent and distribute more normally than the NDVI histogram. The histograms of EVI and EVI2 matched very well, with only minor disagreements between 0.06 and 0.21. 99.2% of the EVI-EVI2 differences were between -0.02 and 0.02, on average, for the 4, 2000-2001 composite periods, with the EVI-EVI2 difference histograms almost seasonally independent (Fig. 6b). The mode of the differences indicated the amplitude of the EVI2 is slightly smaller than the amplitude of the EVI, at the order of 0.0050.

6. APPLICATION OF EVI2 TO AVHRR DATA Youngwook et al.7 investigated the cross-sensor continuity of VIs, and found that the AVHRR EVI2 was linearly related to the MODIS EVI2, but with a lower dynamic range of values. At the level of top of canopy, the relationship between them was found to be

MODIS-EVI2=1.1435×AVHRR-EVI2−0.0013 (10)

With R2=0.9969.

Daily 1-km AVHRR data at three Earth observing system (EOS) land validation core sites, Harvard Forest LTER (Lat 42.538°, Lon -72.171°), Konza Prairie LTER (Lat 39.082°, Lon -96.650°) and Bondville covered by broadleaf cropland (Lat 40.007°, Lon -88.291°), were extracted. AVHRR red and NIR reflectances were atmospherically corrected by using the 6S model8. 16-day composite AVHRR data were generated through the maximum value method, and the composite periods were matched with those of the MODIS data. In order to match the dynamic range of the MODIS EVI2, AVHRR EVI2 was linearly adjusted by using Eq (10). MODIS 1-km, 16-day composite data from the Terra and Aqua platforms were extracted over the three sites. At each site, the average reflectances were computed in a 3×3 window, from which AVHRR and MODIS EVI2 values were computed according to Eq. (9).

The adjusted AVHRR EVI2 followed the seasonal profiles of the Terra and Aqua EVI2 quite well at the Harvard forest site (Fig. 7a). Only a delay of green-up of the AVHRR EVI2 profile can be found in comparison with the Terra EVI2 profile in 2003. The adjusted AVHRR EVI2 profile matched the Terra EVI2 very well before the peak of the growing season of 2003, but went down later than the Terra EVI2 after the peak at the Bondville cropland site (Fig. 7b). The amplitude of the Aqua EVI2 was evidently lower than those of the Terra and AVHRR EVI2. The three EVI2 profiles were quite consistent at the Konza prairie site (Fig. 7c). The adjusted AVHRR EVI2 was slightly higher than the Terra and Aqua EVI2 at the end of the growth season. The good agreement between the adjusted AVHRR and MODIS EVI2 suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another long-term vegetation record different from the NDVI counterpart.

Table 1 listed the coefficients of determination between EVI2 from two sensors over the three sites, which are independent of the linear adjustment of the AVHRR EVI2. R2 between the Terra EVI2 and the Aqua EVI2 was highest at each site, since the two satellites are equipped with the same instruments (MODIS). The AVHRR EVI2 had a lower R2 with the Terra EVI2 at the Harvard site (0.718) in comparison with R2 with the Aqua EVI2 (0.776). But at the Konza site, AVHRR EVI2 was correlated slightly better with the Terra EVI2 than with the Aqua EVI2. R2 between any two instruments were very high (>0.93) at the Bondville site, suggesting the similar shapes of the three EVI2 profiles, even though the amplitude of the Aqua EVI2 profile was evidently lower than the other two amplitudes (Fig. 7b). By combining all the data at the three sites, R2 between the Terra EVI2 and AVHRR EVI2 (0.816) was slightly lower than that between the Terra EVI2 and Aqua EVI2 (0.867), but slightly higher than R2 between the Aqua EVI2 and AVHRR EVI2 (0.772).

Table 1. Comparison of the coefficient of determination (R2) between EVI2 from two sensors over the three sites.

Harvard Bondville Konza Total Terra-AVHRR 0.718 0.933 0.855 0.816 Aqua-AVHRR 0.776 0.935 0.825 0.772 Terra-Aqua 0.836 0.967 0.940 0.867



1/1/03 7/2/03 1/1/04 7/1/04 12/31/04 7/1/050

0.2

0.4

0.6

0.8Adjusted AVHRR EVI2 Terra EVI2 Aqua EVI2

Date

EVI

2

Harvard forest site (3×3 1-km pixels)

a

(a)

1/1/03 3/3/03 5/3/03 7/3/03 9/2/03 11/2/03 1/2/04

0

0.2

0.4

0.6


Date

EVI

2

Bondville (3×3 1-km pixels)

1/1/03 3/3/03 5/3/03 7/3/03 9/2/03 11/2/03 1/2/04

0.2

0.4

0.6


Date

EVI

2Konza Prairie (3×3 1-km pixels)

(b) (c) Fig. 7. Comparison of time series of MODIS Terra and Aqua EVI2 with adjusted AVHRR EVI2 at (a) the Harvard forest site, (b) the Bondville cropland site and (c) the Konza prairie site.

7. CONCLUSION In this study, a 2-band EVI without a blue band was developed and preliminarily validated using global MODIS data. The EVI2 can be used as an exact substitute of the EVI for good observations, i.e., good QA pixels that contain no cloud or snow and low aerosol amounts. The EVI2 takes the formula of the LVI, but with optimized parameter values, obtained through analysis of the two-dimension parameter (L-β) space, such that the mean absolute difference between EVI and EVI2 was minimized using a global dataset. The linear vegetation index (LVI) incorporates the soil-adjustment factor of the SAVI with a linearity-adjustment factor, β. It is through the linearity-adjustment factor β, that the sensitivity of an index can be adjustable and make it possible to be comparable with the EVI. The similarity between the EVI and EVI2 was analyzed and validated at the global scale. The EVI-EVI2 differences were found to be almost within ±0.02 when atmospheric influences are insignificant.

The EVI2 can be used for sensors without a blue band, such as the AVHRR, to produce an EVI-like vegetation index, complementary to the NDVI. After a linear adjustment, AVHRR EVI2 was found to be comparable with the MODIS EVI2 at three EOS land validation core sites. The good agreement between the adjusted AVHRR and MODIS EVI2 suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data. The discrepancy between EVI2 from the two instruments may be caused by the different atmospheric correction and compositing



schemes. Bidirectional reflectance properties should be taken into account since even the same MODIS instruments onboard the different satellites (Terra and Aqua) produce VIs with difference to some extent. The relationships and continuity between the MODIS and AVHRR EVI2 should be further investigated extensively over various land covers and geographic locations.

ACKNOWLEDGEMENTS

The authors thank Tomoaki Miura and Ramon Solano-Barajas for their preparations of AVHRR data. This work was supported by NASA MODIS contract #NNG04HZ20C (A. Huete, P.I.), NASA grant #NNG04GJ22G for the assessment of Vegetation Index Environmental Data Records with NPP VIIRS (J. Privette, P.I.), and NASA EOS grant NNG04GL88G (T. Miura, P.I.).

REFERENCES 1. A. R. Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao and L. G. Ferreira, “Overview of the radiometric and

biophysical performance of the MODIS vegetation indices,” Remote Sensing of Environment, 83, 195-213 (2002). 2. A. R. Huete, “A soil-adjusted vegetation index (SAVI),” Remote Sensing of Environment, 25, 295-309 (1988). 3. Y. J. Kaufman, and D. Tanré, “Atmospherically resistant vegetation index (ARVI) for EOS-MODIS,” IEEE

Transactions on Geoscience and Remote Sensing, 30, 261-270 (1992). 4. R. Houborg, H. Soegaard, and E. Boegh, “Combining vegetation index and model inversion methods for the

extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data,” Remote Sensing of Environment, 106, 39-58 (2007).

5. Z. Jiang, A. R. Huete, J. Li, and Y. Chen, “An analysis of angle-based with ratio-based vegetation indices,” IEEE Transactions on Geoscience and Remote Sensing, 44, 2506-2513 (2006).

6. Z. Jiang, A. R. Huete, K. Didan, and T. Miura, “Development of a 2-band Enhanced Vegetation Index without a blue band,” Remote Sensing of Environment, in review 2007.

7. Y. Kim, A. R. Huete, and T. Miura, “Compatibility of enhanced vegetation index across sensors: An evaluation with convolved Hyperion data,” Remote Sensing of Environment, to be submitted, (2007)

8. E. F. Vermote, D. Tanré, J. L. Deuze, M. Herman, and J. J. Morcrette, “Second simulation of the satellite signal in the solar spectrum, 6S: an overview,” IEEE Transactions on Geoscience and Remote Sensing, 35, 675-686 (1997).



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