Overview of the radiometric and biophysical performance of the MODIS vegetation indices
Introduction
One of the primary interests of the Earth Observing System (EOS) program is to study the role of terrestrial vegetation in large-scale global processes with the goal of understanding how the Earth functions as a system. This requires an understanding of the global distribution of vegetation types as well as their biophysical and structural properties and spatial/temporal variations. Vegetation indices (VIs) are spectral transformations of two or more bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations. As a simple transformation of spectral bands, they are computed directly without any bias or assumptions regarding land cover class, soil type, or climatic conditions. They allow us to monitor seasonal, inter-annual, and long-term variations of vegetation structural, phenological, and biophysical parameters.
The Moderate Resolution Imaging Spectroradiometer (MODIS) VI products are designed to provide consistent, spatial, and temporal comparisons of global vegetation conditions that can be used to monitor photosynthetic activity Justice et al., 1998, Running et al., 1994. Two MODIS VIs, the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are produced globally over land at 1-km and 500-m resolutions and 16-day compositing periods. Whereas the NDVI is chlorophyll sensitive, the EVI is more responsive to canopy structural variations, including leaf area index (LAI), canopy type, plant physiognomy, and canopy architecture (Gao, Huete, Ni, & Miura, 2000). The two VIs complement each other in global vegetation studies and improve upon the detection of vegetation changes and extraction of canopy biophysical parameters.
The MODIS NDVI is referred to as the “continuity index” to the existing 20+ year NOAA-AVHRR-derived NDVI time series, which could be extended by MODIS data to provide a longer-term data record for use in operational monitoring studies. The AVHRR-NDVI has been widely used in various operational applications, including famine early warning systems, land cover classification, health and epidemiology, drought detection, land degradation, deforestation, change detection and monitoring Cihlar et al., 1997, Goward et al., 1991, Tucker et al., 1985. The NDVI is also an important parameter to various kinds of local, regional, and global scale models, including general circulation and biogeochemical models Peterson et al., 1988, Potter et al., 1999. They serve as intermediaries in the assessment of various biophysical parameters, such as green cover, biomass, LAI, and fraction of absorbed photosynthetically active radiation (fAPAR) Asrar et al., 1984, Sellers, 1985, Tucker, 1979. The AVHRR-NDVI time series has been successfully used in many studies on the interannual variability of global vegetation activity and in relating large-scale interannual variations in vegetation to climate (Myneni, Keeling, Tucker, Asrar, & Nemani, 1997).
The normalized difference vegetation index (NDVI) is a normalized ratio of the NIR and red bands,where ρNIR and ρred are the surface bidirectional reflectance factors for their respective MODIS bands. The NDVI is successful as a vegetation measure in that it is sufficiently stable to permit meaningful comparisons of seasonal and inter-annual changes in vegetation growth and activity. The strength of the NDVI is in its ratioing concept, which reduces many forms of multiplicative noise (illumination differences, cloud shadows, atmospheric attenuation, certain topographic variations) present in multiple bands. The NDVI is functionally equivalent to the simple ratio (SR=NIR/red) such that NDVI=(SR−1)/(SR+1). The main disadvantage of the NDVI is the inherent nonlinearity of ratio-based indices and the influence of additive noise effects, such as atmospheric path radiances. The NDVI also exhibits scaling problems, asymptotic (saturated) signals over high biomass conditions, and is very sensitive to canopy background variations with NDVI degradation particularly strong with higher canopy background brightness (Huete, 1988).
Non-ratioing vegetation indices such as the perpendicular vegetation index (PVI) and the green vegetation index (GVI) are generally more linear with less saturation problems, but require external and sensor noise removal in the derivation of surface reflectances that are input to VI computation Crist & Cicone, 1984, Richardson & Wiegand, 1977. Current emphasis in the EOS era involves operational ‘external’ noise removal through improved calibration, atmospheric correction, cloud and cloud shadow removal, and standardization of sun-surface-sensor geometries with bidirectional reflectance distribution function (BRDF) models. This minimizes the need for ‘ratioing-based’ indices and allows for the introduction of alternative and enhanced vegetation indices for operational monitoring of the Earth's vegetation (Verstraete & Pinty, 1996). 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. The equation takes the form,where ρ are atmospherically corrected or partially atmosphere corrected (Rayleigh and ozone absorption) surface reflectances, L is the canopy background adjustment that addresses nonlinear, differential NIR and red radiant transfer through a canopy, and C1, C2 are the coefficients of the aerosol resistance term, which uses the blue band to correct for aerosol influences in the red band. The coefficients adopted in the EVI algorithm are, L=1, C1=6, C2=7.5, and G (gain factor)=2.5 Huete et al., 1994, Huete et al., 1997.
The “atmospheric resistance” concept is based on the wavelength dependency of aerosol effects, utilizing the more atmosphere-sensitive blue band to correct the red band for aerosol influences (Kaufman & Tanré, 1992). The objective is to find a function, fλ(ρλ, ρblue−ρred), that is stable against variations of atmospheric aerosol condition. The EVI was found to perform well in the heavy aerosol, biomass burning conditions in Brazil (Miura, Huete, van Leeuwen, & Didan, 1998). Later, Miura, Huete, Yoshioka, and Holben (2001) showed the atmosphere-resistant VIs to successfully minimize residual aerosol effects resulting from the dark target-based atmospheric correction (DTAC) approach utilized in the MODIS surface reflectance products (Vermote, El Saleous, & Justice, 2002, this issue). The DTAC-derived surface reflectances are subject to errors due to the assumptions and characteristics of the algorithm, with the main source of uncertainty associated with the assumed or estimated DT surface reflectance. Other errors are due to the spatial heterogeneity of aerosol optical thickness since coarse grid-based corrections are often applied. Miura et al. (2001) found that these errors preserve the wavelength dependency of aerosol effects such that the atmosphere resistance concept could still be applied with improved results to the MODIS EVI.
The canopy background correction is relevant for vegetation monitoring since 70% of the Earth's terrestrial surface consists of open canopies with significant canopy background signals exerting some effect on the canopy reflectance properties (Graetz, 1990). These open canopies include deserts, tundra, grasslands, shrublands, savannas, woodlands, wetlands, and many open forested areas. Canopy backgrounds include a wide variety of weathered geologic substrates, leaf litter, water, and snow. Canopy background noise is most severe at intermediate levels of vegetation (40–60% cover) since it is the ‘coupled’ influence of the canopy background and transmittance properties of the overlying canopy that determine the extent of noise in the VIs. This ‘background’ influence must be removed in order to better interpret spatial and temporal variations associated with vegetation from variations associated with the canopy background. The fundamental objective of vegetation indices is to isolate the ‘green’, photosynthetically active signal from the spatially and temporally variable 'mixed' pixels, to allow meaningful spatial and temporal intercomparisons of vegetation activity.
In this paper, we describe the status of the MODIS VI products and analyze their performance over the first 12 months of MODIS instrument operation. The MODIS VIs were evaluated as to their ability to monitor and assess vegetation conditions over a diverse range of biomes. Site intensive comparisons were made at four validation test sites utilizing low altitude, aerial reflectance measurements and field-based biophysical measurements. Comparisons were also made between the MODIS VI products and the NDVI product from the NOAA-14 AVHRR sensor. The objective was to demonstrate the performance and validity of the MODIS VI products over various biome types and to assess their capability for vegetation change detection and biophysical parameter retrievals.
Section snippets
MODIS VI product (algorithm) description
The MODIS standard VI products include two, gridded vegetation indices (NDVI, EVI) and quality analysis (QA) with statistical data that indicate the quality of the VI product and input reflectance data. They are currently produced at 16-day intervals at 500-m and 1-km resolutions with limited production at 250-m resolution. A coarser, climate modeling grid (CMG) version of the VI products at a resolution of 25 km is also in development, as are monthly versions of all the VI products (Fig. 1).
Methods
To evaluate the performance of the MODIS VI products, we utilized airborne radiometric measurements, higher resolution Landsat ETM+ imagery, and in situ field biophysical data collected over four validation test sites representing a variety of land surface biome types (Table 1). The test sites were large enough to permit reasonable MODIS coverage yet small enough to enable a sufficient number of independent field observations. Independent retrievals of surface reflectances and VIs were made
Radiometric comparisons
A comparison of MODIS nadir-view data (single day) with coincident MQUALS data provides an assessment of how well the VI products are performing prior to temporal compositing. Of the four sites flown with MQUALS, we were only successful in obtaining coincident, cloud-free, nadir-view MODIS imagery at the semiarid Jornada site on May 9, 2000 (Fig. 4). The MODIS and MQUALS reflectances and VIs matched fairly well over the three land cover types within the Jornada site with MODIS blue, red, and
Discussion and conclusions
In this preliminary performance and validation study, we demonstrated the utility of the MODIS VI products in providing useful radiometric and biophysical information for land surface characterization. Four validation test sites were sampled in a consistent manner with an identical and ‘traceable’ MQUALS radiometric package. In conjunction with simultaneous field sampling, MQUALS allowed us to collect a self-contained set of biophysical and radiometric data from the same ground pixels, which
Acknowledgements
This work is supported by NASA/MODIS Contract no. NAS5-31364. We thank Hiroki Yoshioka, Karim Batchily, Fricky Keita, and Hugo Rodriguez in helping with field and airborne data collection.
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