Abstract
Differences among the normalized difference vegetation indices (NDVIs) simulated from multiple sensors were analyzed using soil isoline equations (SIEs). The upscaled NDVI image was obtained by generating images of red and near-infrared (NIR) bands using an upscaling factor through (1) convolution of a spectral response function (SRF) and hyperspectral data from the EO-1 HYPERION, and (2) adaptation of an area-averaging window. Per-pixel classification was conducted as a solution to the inverse problem of the SIEs, which emulated the spectral behaviors in the red and NIR reflectance subspaces. We compared the NDVIs obtained using three combinations of SRFs that described the existing sensors and identified different trends in the NDVI biases within each class for all sensor combinations, regardless of the scaling factor. These results suggested the potential utility of the SIE-based map for separating clusters of NDVI differences using to the sensor characteristics of the SRF and the spatial resolution.
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