采用1维离散小波HAAR、DB4、SYM4对LOPEX 93数据库中的6条水稻反射光谱曲线进行10层小波分解。利用小波近似系数重构信号，采用步长行走法计算重构信号的小波分形维数。研究各尺度下小波分形维数、小波细节系数方差、小波细节系数信息熵、小波近似系数重构方差的特征。结果表明水稻光谱曲线具有分形特征，分形计算中相关系数值均大于0.9证明分形计算的有效性。4个参数的尺度特征揭示了水稻光谱曲线特征尺度转折点出现在尺度6，当水稻光谱分辨率小于64 nm，才能较好地反映光谱曲线峰谷细节特性。通过田间实测18条水稻光谱，计算各尺度的两种植被指数及植被指数与叶绿素的相关系数，进一步证明这一结论。

Six rice reflectance spectra in LOPEX 93 database were used as samples for decomposition into 10-layer signals based on the one-dimensional discrete wavelet types HAAR, DB4 and SYM4. The goals of this work were to reconstruct the signal through wavelet approximation coefficients and to calculate the wavelet fractal dimension of the reconstruction signal using the walking divider method. On each scale, we have determined the wavelet fractal dimension, wavelet detail coefficient variance, wavelet detail coefficient entropy, and approximate wavelet coefficient reconstruction curve variance. The results show fractal characteristics present in rice spectra, and proved the validity of fractal calculation by correlation coefficients greater than 0.9. The four parameters revealed that the turning point of the rice spectral characteristic scale is present on the sixth scale when rice spectral resolution is less than 64 nm, in order to better reflect spectral peak-valley specific features. Through the measurement of 18 rice spectra in the field, this conclusion is further evidenced by two kinds of vegetation indexes and the correlation coefficients of two kinds of vegetation indexes and chlorophyll values on each scale.