Based on the 12 months’ 1km AVHRR data in China

this paper computes four kinds of vegetation index （VI）

that are ratio vegetation index （ RVI ）

normalized vegetation index （ NDVI ）

soil adjusted vegetation index （ SAVI ） and modified soil adjusted vegetation index （ MSAVI ）. Then

we make the same principal components analysis （ PCA ） to them

and find that the PCA transformed first four principal components （ PCA1

PCA2

PCA3

PCA4 ） contribute about 88% cumulative variance

and PCA1 represents VI cumulation of whole year

PCA2 represents VI difference of winter and summer

PCA3 represents VI difference of spring and summer

PCA4 represents VI difference of spring and autumn. In other words

for multitemporal vegetation index of one year

PCA not only compresses the information to the first four principal components

but also extracts the key change information. The PCA1 expresses the basic land cover information

the others extract the seasonal change information of vegetation. However

the outcome of different vegetation index has some differences. As to the cumulative variance of the first four eigenvectors

the biggest is NDVI

89.28%

the second is SAVI

88.40%

and the smallest is RVI

only 87.44%. As to the correlation matrix of four vegetation index

SAVI and MASVI are the most similar

NDVI is much similar with the first two vegetation indices

and RVI is the least similar. Although the primary purpose of VI is to indicate the biomass of vegetation

due to the different features of VI

such as different correlation with leaf area index

different sensitivity to vegetation and different anti disturbance of soil and atmosphere

different VI indicates different biomass for the same vegetation

that is

when we use the same PCA to different VI

the result is not uniform.