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

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1 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 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 () without a blue band that has the best similarity with the 3-band EVI, and to investigate the crosssensor continuity of the 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 equation. The similarity between EVI and was validated at the global scale. After a linear adjustment, the AVHRR was found to be comparable with the MODIS. The good agreement between the AVHRR and MODIS suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another longterm vegetation record different from the NDVI counterpart. Keywords: Vegetation indices, EVI,, 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: N R EVI = G N + C R C B + L 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 respectively 1. 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) retrievals 4. 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 1 2 (1) *zjiang@ .ariozna.edu; phone ; fax ; tbrs.arizona.edu Remote Sensing and Modeling of Ecosystems for Sustainability IV, edited by Wei Gao, Susan L. Ustin, Proc. of SPIE Vol. 6679, 66795, (27) X/7/$18 doi: / Proc. of SPIE Vol

2 adaptation of EVI should be compatible. The development of the 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 longterm 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 and the AVHRR. 2. DERIVATION OF THE Huete 2 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 and N + l R + l N R N + R + 2l (2), (3) respectively 2. 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=2l. In this paper, Eq. (2) is denoted as a soil-adjusted SR (SASR). The L value is usually determined as.5 and thus l =.25. NIR reflectance Isoline (R, N) Soil line Y=X -l α Red reflectance β γ -l E Fig. 1. The isolines of the SAVI and the soil-adjusted simple ratio (SASR) and their angle in red-nir reflectance space. Proc. of SPIE Vol

3 1.8 SAVI SASR 1:1 line 2.4 SAVI SASR EVI 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, SAVI = 1+ L tan α (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, ( γ ) = tan( α + π 4) SASR = tan (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, ( β ) = tan( α + β ) LVI (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, (LVI ) is ( β ) LVI = tan (7) By subtracting LVI 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 where LVI = G = G N + [ tan( α + β ) tan β ] ( N R) R tan( π 4 + β ) + L ( 1 tan β ) sec 2 β = G G (8-1) ( 1 tan β ) 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 (8) Proc. of SPIE Vol

4 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. in this paper. 3. DATA AND METHODS 4 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 4 sites, from 18 February 2 to 19 December 25. 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 1 (the best 3 levels among 16 quality assurance (QA) levels), aerosol quantity 1 (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 equation (Eq. 8). The mean absolute difference (MAD) between EVI and 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. 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 (R 2 ) between these two indices was also used as a reference of similarity since an optimal 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 decreases rapidly when L increases for to.5, and increases for higher L (Fig. 3a). The MAD for the SAVI case at point β=, L=.5 in Fig. 3(a) is much smaller than the MAD for the NDVI case at point β=, L=. As shown in Fig. 1, β varies between 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=22.381º (tan(β) = 7/17) and L= The optimal G value corresponding to the minimum MAD is 2.57 (Fig. 3b). The R 2 between the EVI and increases rapidly when L increases from to.5, and then decreases for higher L values (Fig. 4). The SAVI has a much higher R 2 with the EVI than the NDVI, and the resulting R 2 between the EVI and with the optimal parameters is.9986, very close to the maximum R 2 values in Fig. 4, indicating a strong linear relationship between these two indices Soil-adjustment factor (L) NDVI SAVI.33 SASR Beta (degrees) (a) Soil-adjustment factor (L) Beta (degrees) (b) Fig. 3. (a) The mean absolute difference (MAD) between the EVI and and (b) the optimal G as functions of β and soil adjustment factor (L). Proc. of SPIE Vol

5 Soil-adjustement factor (L) SAVI SASR.25 NDVI Beta (degrees) Fig. 4. Coefficient of determination (R 2 ) between the EVI and as a function of β and soil-adjustment factor (L). For simplicity, G in the equation can be set to 2.5, and with the optimal parameter values, the equation (Eq. 8) becomes N R EVI 2 = 2.5 (9) N + 2.4R VALIDAITON 5.1 Global vegetation index images (a) Proc. of SPIE Vol

6 Ocean -.2 to to to (b) Fig. 5. Comparison of global MODIS 1 km, 16-day composite Vegetation Indices during Jul. 27-Aug. 11, (DOY ) 2 composite period, (a) EVI, (b). The global 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. 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 Percent (%) EVI NDVI Global QA-accepted data 7/27/2 Percent (%) /22/ 7/27/ 1/31/ 1/17/1 Global QA-accepted data VI -EVI (a) (b) Fig. 6. Comparison of (a) MODIS EVI, and NDVI histograms, and (b) EVI- difference histograms using QA-accepted data. The global histograms of the vegetation indices for the Jul 27- Aug 11, 2 composite period are shown in Fig. 6 (a) for QA-accepted cases. The global NDVI histogram has two peaks, one at.1 and another at.88. The histograms of global Proc. of SPIE Vol

7 EVI and are consistent and distribute more normally than the NDVI histogram. The histograms of EVI and matched very well, with only minor disagreements between.6 and % of the EVI- differences were between -.2 and.2, on average, for the 4, 2-21 composite periods, with the EVI- difference histograms almost seasonally independent (Fig. 6b). The mode of the differences indicated the amplitude of the is slightly smaller than the amplitude of the EVI, at the order of APPLICATION OF TO AVHRR DATA Youngwook et al. 7 investigated the cross-sensor continuity of VIs, and found that the AVHRR was linearly related to the MODIS, but with a lower dynamic range of values. At the level of top of canopy, the relationship between them was found to be MODIS-= AVHRR-.13 (1) With R 2 = Daily 1-km AVHRR data at three Earth observing system (EOS) land validation core sites, Harvard Forest LTER (Lat , Lon ), Konza Prairie LTER (Lat 39.82, Lon ) and Bondville covered by broadleaf cropland (Lat 4.7, Lon ), were extracted. AVHRR red and NIR reflectances were atmospherically corrected by using the 6S model 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, AVHRR was linearly adjusted by using Eq (1). 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 values were computed according to Eq. (9). The adjusted AVHRR followed the seasonal profiles of the Terra and Aqua quite well at the Harvard forest site (Fig. 7a). Only a delay of green-up of the AVHRR profile can be found in comparison with the Terra profile in 23. The adjusted AVHRR profile matched the Terra very well before the peak of the growing season of 23, but went down later than the Terra after the peak at the Bondville cropland site (Fig. 7b). The amplitude of the Aqua was evidently lower than those of the Terra and AVHRR. The three profiles were quite consistent at the Konza prairie site (Fig. 7c). The adjusted AVHRR was slightly higher than the Terra and Aqua at the end of the growth season. The good agreement between the adjusted AVHRR and MODIS 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 from two sensors over the three sites, which are independent of the linear adjustment of the AVHRR. R 2 between the Terra and the Aqua was highest at each site, since the two satellites are equipped with the same instruments (MODIS). The AVHRR had a lower R 2 with the Terra at the Harvard site (.718) in comparison with R 2 with the Aqua (.776). But at the Konza site, AVHRR was correlated slightly better with the Terra than with the Aqua. R 2 between any two instruments were very high (>.93) at the Bondville site, suggesting the similar shapes of the three profiles, even though the amplitude of the Aqua profile was evidently lower than the other two amplitudes (Fig. 7b). By combining all the data at the three sites, R 2 between the Terra and AVHRR (.816) was slightly lower than that between the Terra and Aqua (.867), but slightly higher than R 2 between the Aqua and AVHRR (.772). Table 1. Comparison of the coefficient of determination (R 2 ) between from two sensors over the three sites. Harvard Bondville Konza Total Terra-AVHRR Aqua-AVHRR Terra-Aqua Proc. of SPIE Vol

8 .8 Adjusted AVHRR Terra Aqua Harvard forest site (3 3 1-km pixels) a /1/3 7/2/3 1/1/4 7/1/4 12/31/4 7/1/5 (a) Date.8 Adjusted AVHRR Terra Aqua Bondville (3 3 1-km pixels).8 Adjusted AVHRR Terra Aqua Konza Prairie (3 3 1-km pixels) /1/3 3/3/3 5/3/3 7/3/3 9/2/3 11/2/3 1/2/4 Date 1/1/3 3/3/3 5/3/3 7/3/3 9/2/3 11/2/3 1/2/4 Date (b) (c) Fig. 7. Comparison of time series of MODIS Terra and Aqua with adjusted AVHRR 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 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 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 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 was analyzed and validated at the global scale. The EVI- differences were found to be almost within ±.2 when atmospheric influences are insignificant. The 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 was found to be comparable with the MODIS at three EOS land validation core sites. The good agreement between the adjusted AVHRR and MODIS suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data. The discrepancy between from the two instruments may be caused by the different atmospheric correction and compositing Proc. of SPIE Vol

9 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 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 #NNG4HZ2C (A. Huete, P.I.), NASA grant #NNG4GJ22G for the assessment of Vegetation Index Environmental Data Records with NPP VIIRS (J. Privette, P.I.), and NASA EOS grant NNG4GL88G (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, (22). 2. A. R. Huete, A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25, (1988). 3. Y. J. Kaufman, and D. Tanré, Atmospherically resistant vegetation index (ARVI) for EOS-MODIS, IEEE Transactions on Geoscience and Remote Sensing, 3, (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, 16, (27). 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, (26). 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 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, (27) 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, (1997). Proc. of SPIE Vol