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 crosssensor
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 longterm
vegetation record different from the NDVI counterpart.
Current earth observing satellite sensors have different temporal, spectral and spatial characteristics that present
problems in the establishment of long term, time series data records. Vegetation indices (VI's) are commonly used in
deriving long term measures of vegetation biophysical properties, which have been shown useful in interannual climate
studies and phenology studies. While significant improvements have been made with new sensors, and algorithms, and
processing methods, backward compatibility of VI's is desired so that the long term record can extend back and utilize
the AVHRR record to 1981. Conversely, any reprocessing of the AVHRR record should consider steps to allow forward
compatibility with newer sensors and products. In this study we evaluated the use of sensor-specific enhanced vegetation
index (EVI) and normalized difference vegetation index (NDVI) data sets, using a time sequence of Hyperion images
over Tapajos National Forest in Brazil over the 2001 and 2002 dry seasons. We computed NDVI, EVI, and a 2-band
version of EVI (EVI2) for different sensor systems (AVHRR, MODIS, VIIRS, SPOT-VGT, and SeaWiFS) and
evaluated their differences and continuity in the characterization of tropical forest phenology. We also analyzed the
influence of different atmosphere correction scenarios to assess noise in the phenology signal. Our analyses show that
EVI2 maintains the desirable properties of increased sensitivity in high biomass forests across all sensor systems
evaluated in this study. We further conclude that EVI2 can be extended to the AVHRR time series record and
compliment that current NDVI time series record.
Long term data records require the effective integration of new sensor technologies and improved algorithms to better characterize global and climate change impacts on ecosystems, while preserving the fundamental attributes of the existing data record. In this study, we investigated key determinants in the spectral translation and extension of MODIS Vegetation Index products across current sensor systems and to the NPOESS (VIIRS) era. We used simulated sensor-specific data sets derived from hyperspectral data using field spectroroadiometers and Hyperion sensors to investigate inter-sensor translation and continuity issues of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). We also investigated the use of data fusion of satellite VI time series with in-situ flux tower time series measurements of photosynthesis, and the use of data fusion with tower-based continuous measures of broadband/hemispherical VI's as possible reference data sets for the inter-calibration of satellite VI time series from different sensor systems. Preliminary comparisons are presented with actual satellite VI measurements from SPOT-VEGETATION, Terra- and Aqua-MODIS, and AVHRR sensors. We found that with a consistent atmosphere correction scheme and a generalized compositing procedure, translation of multi-sensor datasets can be achieved with certain limitations.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.