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In order to detect the growth status of apple trees based on spectroscopy, an apple orchard was selected as the experimental site located at the outskirts of Beijing. First, the samples of apple tree leaves at each key growth period were collected. Then the spectral reflectance, chlorophyll content and moisture content of the samples were measured respectively. The characteristics of those spectra were analyzed and the correlation between chlorophyll content, moisture content and their spectra were calculated. The results showed that the original spectra were most correlated with leaf chlorophyll content from 511nm to 590nm and from 688nm to 715nm. The correlation coefficients in September were high and the maximum value was 0.6. From the correlation analysis between apples leaves moisture content and their spectra, it was found that the original spectra were most correlated with leaf moisture content at the wavebands of 420~500nm, 640~680nm and 740~860nm, and the correlation coefficients in fruiting period were high. According to the selected sensitive bands, the models for estimating the chlorophyll content and moisture content in apple leaves were built by multiple linear regression analysis (MLRA), principal component analysis (PCA) and artificial neural network (ANN), respectively. The models were tested by the validation set which included 25 samples of apple tree leaves. The forecasting results indicated that the model based on PCA was the best model to predict the chlorophyll content of apple leaves, and the calibration and validation R2 were 0.8852 and 0.8289, respectively. The forecasting model of apple leaf moisture content based on ANN was the best, and the calibration and validation R2 were 0.862 and 0.8375, respectively.