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ON CURRENT COMPOSITING ALGORITHMS
J. Qi
USDA-ARS Water Conservation Laboratory, 4331 E. Broadway, Phoenix, Arizona 85040, USA
Y. Kerr
LERTS-CNES-CNRS, 18 Avenue Edouard-Belin, 31055 Toulouse Cedex, France
ABSTRACT:
Several techniques exist for compositing the multitemporal Advanced Very High Resolution Radiometer (AVHRR) data
for vegetation studies. The major pixel selection criteria of these techniques rely cm the characteristics of the NDVI:
appearance of clouds, poor atmospheric conditions, and off-nadir viewing geometries would depress the NDVI values.
Consequently, selecting the pixels with the maximum value of NDVI would presumably eliminate these external
pertubating effects. However, the maximum NDVI does not always correspond to these ideal conditions. In fact, the
NDVI varies with these external factors in an unpredictable way. There was an indication that the maximum NDVI
tpnrtpti to favor the off-nadir view in the forward direction. The resultant composite product would be consequently
affected. To improve the multitemporal data via compositing, therefore, both the pixel selection criteria and the classifier
NDVI need to be modified or corrected for external factors. In this study, the current compositing algorithms were
reviewed, and alternatives were proposed to use the combinations of the red and near-infrared channels and biological
characteristics of vegetation as second criteria in pixel selections. In addition, the traditional classifier NDVI was
replaced with different vegetation indices. The new approach was applied to an AVHRR data set over Hapex study site
in Niger in 1992. The results showed that the new approach improved the AVHRR time series quality and was
promising towards the development of an efficient compositing algorithm. The new approaches will be presented and
limitations will be further discussed.
KEY WORDS: Composite, AVHRR.Vegetation IndexRemote Sensing, Satellite
INTRODUCTION
The advanced very high resolution radiometer (AVHRR) on the National Oceanographic and Atmospheric
Administration (NOAA) satellite series has been the major sensor that provides scientists with continuous remote sensing
data at regional and global scales over much of the Earth’s surfaces for global change studies. The major constraints
of the AVHRR data have been both generic and external problems. The generic problems include the radiometric
calibrations of the sensors, especially the first two spectral channels for which no on-board calibration is available. The
external problems are caused by cloud masking, atmospheric contamination, geometric registration, and sensor viewing
angle variations. Consequently, the observed AVHRR data contain substantial uncertainties, preventing scientists from
making a quantitative analysis of the Earth’s vegetation dynamics (Gutman, 1991).
Compositing techniques have been used to reduce uncertainties due to external factors, especially due to clouds
masking. These techniques involve choosing a subset of data that are cloud-free and have least atmospheric
contaminations from a large data set. Several techniques exist for compositing. The most popular one is the maximum
value compositing (MVC) algorithm proposed by Holben (1986). This algorithm first defines a compositing period
within which the normalized difference vegetation index (NDVI) classifier is assumed to change little. Within each
compositing period, the pixel with the maximum value of NDVI is selected. The rationale behind this is that clouds or
poor atmospheric conditions or larger off-nadir view angles depresses NDVI value. Selecting the maximum value of
NDVI presumably reduces the chances of obtaining cloud-masked, atmosphere-contaminated, and off-nadir viewing
angle data. Because this technique employs a single pixel selection criterion (maximum NDVI), the quality of the
composited data relies on the characteristics of the NDVI.
Although major noises, especially the cloud-masking, can be reduced substantially, problems remain because
of the nature of the NDVI classifier and became of the lack of precise pixel selection criteria. The NDVI is vulnerable
to soil background variations (Huete, 1989) and atmospheric conditions (Kaufman, 1989; Goward et al. 1990). The
maximum NDVI favors off-nadir viewing angles in the forward directions (D’lorio et al., 1990 and 1991). Subsequent
selection of highest NDVI may, therefore, be biased toward larger off-nadir view angles. In addition, the atmosphere
and the view angle effects are coupled (Qi et al., 1994a). Reduction in one noise may be offset by an increase in another
type of noise.
