  To meet the demanding of spectral reconstruction in the visible and
near-infrared wavelength, the spectral reconstruction method for typical
surface types is discussed based on the USGS /ASTER spectral library and
principal component analysis (PCA). A new spectral reconstructed model is
proposed by the information of several typical bands instead of all of the
wavelength bands, and a linear combination spectral reconstruction model is
also discussed. By selecting 4 typical spectral datasets including green
vegetation, bare soil, rangeland and concrete in the spectral range of 400-900
nm, the PCA results show that 6 principal components could characterized the
spectral dataset, and the relative reconstructed errors are smaller than 2%. If
only 6-7 selected typical bands are employed to spectral reconstruction for all
the surface reflectance in 400-900 nm, except that the reconstructed error of
green vegetation is about 3.3%, the relative errors of other 3 datasets are all
smaller than 1.6%. The correlation coefficients of those 4 datasets are all
larger than 0.99, which can effectively satisfy the needs of spectral
reconstruction. In addition, based on the spectral library and the linear
combination model of 4 common used bands of satellite remote sensing such as
490, 555, 670 and 865 nm, the reconstructed errors are smaller than 8.5% in
high reflectance region and smaller than 1.5% in low reflectance region
respectively, which basically meet the needs of spectral reconstruction. This
study can provide a reference value for the surface reflectance processing and
spectral reconstruction in satellite remote sensing research.
