Numerous satellite sensor systems useful in terrestrial Earth observation and monitoring have recently been launched and their derived products are increasingly being used in regional and global vegetation studies. The increasing availability of multiple sensors offer much opportunity for vegetation studies aimed at understanding the terrestrial carbon cycle, climate change, and land cover conversions. Potential applications include improved multiresolution characterization of the surface (scaling); improved optical-geometric characterization of vegetation canopies; improved assessments of surface phenology and ecosystem seasonal dynamics; and improved maintenance of long-term, inter-annual, time series data records. The Landsat series of sensors represent one group of sensors that have produced a long-term, archived data set of the Earth s surface, at fine resolution and since 1972, capable of being processed into useful information for global change studies (Hall et al., 1991)

Huete, Alfredo

Kim, HoJin

Miura, Tomoaki

Yoshioka, Hiroki

NASA Technical Reports Server

 USE OF EO-1 HYPERION DATA FOR INTER-SENSOR  
CALIBRATION OF VEGETATION INDICES  
 
Alfredo Huete,1 Tomoaki Miura,1 HoJin Kim,1 Hiroki Yoshioka2 
1Department of Soil, Water and Environmental Science, University of Arizona, 85721 USA 
ahuete@ag.arizona.edu; tomoaki@ag.arizona.edu; hjkim@ag.arizona.edu; yoshioka@urban.meijo-u.ac.jp 
2Department of Urban Science, Meijo University, 4-3-3 Nijigaoka, Gifu, 509-0261 Japan 
 
 
1.  INTRODUCTION 
Numerous satellite sensor systems useful in terrestrial Earth observation and monitoring have recently been 
launched and their derived products are increasingly being used in regional and global vegetation studies.  The 
increasing availability of multiple sensors offer much opportunity for vegetation studies aimed at understanding the 
terrestrial carbon cycle, climate change, and land cover conversions. Potential applications include improved multi-
resolution characterization of the surface (scaling); improved optical-geometric characterization of vegetation 
canopies; improved assessments of surface phenology and ecosystem seasonal dynamics; and improved 
maintenance of long-term, inter-annual, time series data records. The Landsat series of sensors represent one group 
of sensors that have produced a long-term, archived data set of the Earth’s surface, at fine resolution and since 1972, 
capable of being processed into useful information for global change studies (Hall et al., 1991). 
 
Spectral vegetation indices are one example of satellite-based products for mapping temporal and spatial variations 
in surface biophysical parameters. Vegetation index products from Advanced Very High Resolution Radiometer 
(AVHRR), SeaWiFS, SPOT-VEGETATION, Moderate Resolution Imaging Spectroradiometer (MODIS), Global 
Imager (GLI), Landsat, and other sensors are now or soon will be widely available for monitoring both seasonal and 
long-term ecosystem dynamics. Their combined use can greatly improve ecosystem spatial and temporal variability 
studies in two ways, (1) through inter-sensor synergies and (2) through multi-sensor data and product continuity 
records. Seasonal and inter-annual vegetation dynamics have been readily observed with moderate resolution 
satellite products, such as the 20-year NOAA-AVHRR normalized difference vegetation index (NDVI) time series 
record (Los, 1993: Roderick et al., 1996). 
 
With the launch of new sensor systems, there is interest in maintaining data continuity and compatibility across the 
sensor-specific data sets. However, there will also exist compatibility problems among the various satellite data 
products due to differences in their sensor characteristics as well as algorithms used (Gao, 2000; Gitelson and 
Kaufman, 1998).  Some of the multi-sensor differences, key to their synergy, may become limitations to data and 
product continuity.  This includes the issue vegetation index (VI) continuity and compatibility among the various 
sensors, which must first be addressed.  In shifting from an older sensor to a newer one, one can take advantage of 
‘state of the art’ technology advancements (e.g., better sensor materials) and improved scientific knowledge (e.g., 
better spectral band configurations), however, there is the dilemma of maintaining data continuity across a time 
series data record while allowing for new and improved algorithms and data processing.  
 
The “key” factors affecting continuity and compatibility of VI data sets computed from different sensors involve; 
- sensor calibration and degradation 
- differences in spatial resolutions and their associated point spread functions (PSF) 
- co-registration and geolocation 
- differences in spectral bandpass filters  
- atmospheric correction methods (O3, H2O, aerosols) 
- cloud masking methods and their efficiency 
- compositing techniques and period  
 
Narrow and broad-band vegetation indices were investigated by Elvidge et al. (1995). Miura et al. (2002) utilized 
Hyperion imagery along a Brazil transect and showed VI translation between sensors to be land cover, soil, and 
biomass dependent.  Yoshioka et al. (2003) developed an algorithm for translating VI data among sensors utilizing a 
linear approximation of vegetation isolines and numerical simulations using leaf and canopy radiative transfer 
https://ntrs.nasa.gov/search.jsp?R=20050192436 2019-08-29T20:00:50+00:00Z
 models.  They applied the algorithm to a Hyperion image and were able to significantly reduce the differences in 
NDVI data for a wide range of LAI conditions.  
 
In this study we used EO-1 Hyperion imagery obtained over a set of biome types to generate broadband reflectance 
and VI values for various Earth observing satellite sensors. Multi-sensor comparisons and analyses of vegetation 
index products are made for the “Constellation” series of sensors that include Landsat-7 ETM+, EO-1, Terra-
MODIS/ASTER, and the Multispectral Medium-Resolution Scanner (MMRS), as well as for additional fine and 
moderate resolution sensor systems.  We focus on the spectral issues (filter response function, bandwidth, center 
wavelength) influencing the derived reflectance and vegetation index values and also address the various issues 
involved in multi-sensor synergy use, including translation, data continuity, and scaling.   The objectives for this 
study were (1) to investigate the “spectral” continuity and compatibility of reflectances and VIs among the different 
sensors using Hyperion scenes over a range of land cover types, and (2) to investigate target dependencies (land 
cover, soil, etc.) on the translation coefficients among sensors. 
 
2.  STUDY SITES AND METHODS 
There are many techniques that can be used to analyze multi-sensor differences in VI’s, including the use of ‘real’ 
satellite sensor observations.  The advantages of this approach are that the real data from which we wish to establish 
translation are used and this also encompasses all sources of uncertainty, including filter degradation and calibration 
drift.  The disadvantages are the time intervals between different sensor 'looks' over the same target and the 
confusion created with possible variations resulting from sun angle and atmosphere differences.  One must also be 
precise in co-registration of the two sensor data sets with additional uncertainties resulting from geolocation error.  
A more controlled approach is to utilize finer resolution sensor data and simulate the responses of coarser resolution 
sensor data sets.  Hyperion hyperspectral data are thus ideal to simulate MODIS, SeaWiFS, AVHRR, and GLI 
pixels, the advantages being that only a single atmosphere and sun angle are involved and there are no spatial 
registration errors.  The disadvantage is that the data is synthetic and the spectral response functions and modulation 
transfer function (MTF) need to be approximated.   
 
We utilized Hyperion data over a 400–2500 nm wavelength range with nominal spectral resolution of 10 nm at 30 m 
ground resolution. The radiometrically calibrated level 1A images were first corrected for vertical destriping noise 
by using the average values of assumed homogeneous areas for subsequent pixel adjustment. The data were then 
spectrally convolved to the bandpasses of the sensors of interest and then processed to atmospherically-corrected 
reflectances and VI’s.  The variations in spectral bandpass properties encountered in this study are shown in Fig. 1.  
Some atmospheric simulations were also conducted with the "6S" radiative transfer code.  
 
Figure 1.  Variations in red (left) and near-infrared (right) spectral bandpasses among various Earth observing 
sensors used in vegetation studies. 
 
Hyperion imagery over a range of international core land validation sites of varying land cover types and surface 
conditions were utilized in this study, including a Hyperion data over Maricopa Agriculture Center in Arizona, the 
 Mandalgovi steppe site in Mongolia, and the Harvard forest long-term ecologic research (LTER) site (Fig. 2). At 
each of the Hyperion sites, data were extracted from a 20 x 20 pixel area for further analyses. 
 
 
Figure 2.  EO-1 Hyperion imagery over the study sites used in the evaluation of vegetation indices. Maricopa 
September 29, 2001 (left); Mandalgovi August 31, 2001 (middle); and Harvard forest September 5, 2001 (right). 
 
3.  RESULTS 
The Hyperion bandpass-convolved results over the Harvard forest study area are shown in Fig. 3 for the MODIS, 
Landsat-7 ETM+, and NOAA-AVHRR 14 sensors under atmospherically corrected (surface) and uncorrected (top-
of-atmosphere, TOA) conditions.  There were much higher inter-sensor variations encountered with the TOA 
results, indicating the strong interactions of atmosphere and sensor bandpass on the resulting red, NIR, and NDVI 
data.  The AVHRR varies the most from the other sensors with higher red reflectances and lower NIR and NDVI 
values.  The surface NDVI values from the AVHRR are lower than the other 2 sensors due to the higher red 
reflectance response.  The Maricopa Agriculture study site had a bi-modal range of surface conditions with bare soil 
and highly-vegetated crops present in the same scene (Fig. 4).  The red reflectance results among the 3 sensors 
varied only slightly in both TOA and surface cases while the NIR results only varied in the uncorrected, TOA data 
with AVHRR yielding the lowest reflectances and MODIS the highest.  This may be an atmosphere water vapor 
effect with the AVHRR bandpass the most vulnerable to water vapor while the MODIS NIR bandpass was designed 
to be free of water vapor contamination.  The TOA- NDVI values followed the same pattern with AVHRR having 
the lowest values and MODIS the highest.  By contrast, the surface-based NDVI values differed less significantly. 
 
In the following graphs we analyze in more detail the inter-sensor spectral bandpass effects on the computed 
vegetation indices.  The top portion of Fig. 5 shows that all bands are highly inter-correlated, as would be expected 
since they are measuring within the same portion of the spectrum.  One can also note, however, that the inter-sensor 
relationships have significant slope and/ or intercept differences.  The AVHRR and ASTER NIR reflectances, for 
example, deviate significantly from the 1:1 line (Fig. 5).  In the lower portion of Fig. 5, we plotted the surface 
reflectance difference (ρsensorX - ρMODIS) to highlight these deviations.  Using the MODIS reflectances as the 
reference (any sensor could be used for the reference case), we see that the differences associated with a different 
bandpass in the red and NIR region are dependent on the initial MODIS reflectance condition and that a second 
sensor’s response could result in higher or lower values.  In the case of the red bandpass, maximum deviations from 
the MODIS value occur at low and high reflectance conditions with deviations increasing as the MODIS surface 
reflectance departs from 0.15 in either direction (Fig. 5).  In the case of the NIR reflectances, minimum variations 
occur at low MODIS reflectances (<0.33) with deviations becoming stronger as MODIS NIR reflectance increases 
in value.  Another observation worth noting is that for any given MODIS reflectance values, there are varying 
 reflectance values for a second sensor, e.g., a MODIS reflectance value of 0.40 in the NIR yields numerous ASTER 
reflectances. 
 
 
Figure 3.  Histograms of red, NIR, and NDVI data over the Harvard forest study site for the 3 sensors, MODIS, 
Landsat-7 ETM+, and NOAA-AVHRR-14, and for TOA and atmospherically-corrected (surface) conditions.   
 
 
Figure 4. Histograms of red, NIR, and NDVI data over the Maricopa Agriculture Center study site for the 3 sensors, 
MODIS, Landsat-7 ETM+, and NOAA-AVHRR-14, and for TOA and atmospherically-corrected (surface) 
conditions. 
  
Figure 5.  Hyperion-generated crossplots of inter-sensor band surface reflectances (top) and their differences using 
MODIS as a reference (bottom). 
 
In Fig. 6, the corresponding VI values are plotted using the sensor differences to highlight the deviations.  The 
NDVI comparisons behaved similar to the red bandpass comparisons in Fig. 5.  Thus, the difference in NDVI 
between MODIS and AVHRR become greater at higher NDVI values, i.e., MODIS NDVI values are higher than 
AVHRR at higher biomass conditions (up to 0.07 NDVI units).  In the right hand side of Fig. 6 we also compared a 
partially atmosphere corrected data set (only corrected for Rayleigh scattering and ozone absorption) and we found 
that atmosphere plays a bigger role in inter-sensor relationships than bandpass differences among sensors.  However, 
in the case of the enhanced vegetation index (EVI) (Huete et al., 2002), atmosphere-induced variations are not so 
strong and bandpass variations among sensors become greater with higher MODIS EVI values (Fig. 6).  There are 
not as many sensors available for comparisons given the need for a blue band for the EVI computation. 
 
4.  CONCLUSIONS AND DISCUSSION 
We investigated continuity and compatibility of the broadband reflectances and VIs across various Earth observing 
sensors. EO-1 Hyperion data was used in different biome sites to synthetically generate multi-sensor reflectances 
and VI’s, including the NDVI and EVI.   Our analyses focused on the spectral issue (spectral characteristics of 
multiple sensors and their influences on the derived VI values).  The sensors considered in this spectral syntheses 
were MODIS, AVHRR, SeaWiFS, VEGETATION, GLI, ASTER, and ETM+.  The major findings were that:  
 
• VI relationships among sensors were neither linear nor unique and were found to exhibit complex patterns 
and dependencies on spectral bandpasses. 
• From the biophysical point of view, inter-sensor VI relationships varied with land cover types and surface 
characteristics.  Thus, a prior knowledge of such ecosystem parameters as leaf area index (LAI), land cover 
type, and soil brightness are needed for exact translation.  
• Atmospheric contaminations were found to increase the discrepancies and land cover dependencies of 
inter-sensor VI relationships, of which magnitudes depends both on level of atmospheric contaminations 
and on amount of vegetation density. 
 
  
Figure 6. Hyperion generated crossplots of inter-sensor NDVI differences (top) and inter-sensor EVI differences 
(bottom) using MODIS as a reference. 
 
Vegetation index relationships among sensors were found to exhibit complex patterns and dependencies based only 
on their spectral bandpasses. Thus, a reflectance or VI value from one sensor can yield multiple values in a second 
sensor.  NDVI relationships among sensors varied with surface conditions, such as land cover type, biophysical 
parameter amounts, and possibly canopy background.  The results shown here demonstrate that  inter-sensor 
calibration and continuity of VI’s are achievable but require biophysical and land cover characterization of surface 
conditions.  We found that the atmosphere resistant VI’s would provide improved multi-sensor translations, by 
reducing the effects of atmosphere on inter-sensor translation of VI’s.  Other factors that could affect the multi-
sensor VI values to a greater extent include the cloud masking algorithm, BRDF-related effects, and the method of 
compositing, such as maximum value compositing (MVC), constrained view angle MVC (CV-MVC),  minimum 
‘blue’ compositing (‘min. blue’), BRDF- based compositing, etc. 
 
Multi-sensor comparisons with actual data from Landsat, EO-1, MODIS, AVHRR, and Terra are also needed to 
confirm some of the observations reported in this study.  Such studies will become even more relevant in 
establishing a long term time series record involving the AVHRR time series record (1981-) with MODIS (2000-) 
and the next generation of the National Polar-orbiting Operational Environmental Satellite System (NPOESS).   
 
ACKNOWLEDGMENTS 
This work was supported by the NASA EO-1 grant NCC5-478 (A.R. Huete).   
 
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Use of EO-1 Hyperion Data for Inter-Sensor Calibration of Vegetation Indices

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