地上生物量（AGB）是作物长势评价及产量预测的重要指标, 因此快速准确地估算AGB至关重要。 由于传统植被指数（VIs）估算多生育期的AGB存在饱和现象, 因此, 利用VIs结合基于离散小波转换（DWT）的影像小波分解（IWD）技术提取的高频信息和连续小波转换（CWT）技术提取的小波系数, 探究VIs, VIs+IWD和VIs+CWT对于AGB的估算能力。 首先, 基于无人机平台分别获取马铃薯现蕾期、 块茎形成期、 块茎增长期、 淀粉积累期的数码影像和成像高光谱影像以及地面实测的AGB数据。 其次, 利用数码影像通过IWD技术提取3种高频信息和利用高光谱反射率数据通过CWT技术提取小波系数以及构建6种高光谱植被指数。 然后, 将植被指数、 高频信息和小波系数分别与AGB进行相关性分析, 并挑选出不同尺度下相关系数绝对值较高的前10波段。 最后, 以VIs, VIs+IWD和VIs+CWT这3种变量分别使用偏最小二乘回归（PLSR）方法构建AGB估算模型, 并对比不同模型估算AGB的效果。 结果表明: （1）每个生育期选取的6种植被指数、 3种高频信息和10种小波系数与AGB的相关性均达到0.01显著水平, 整个生育期相关性均呈现先升高后降低的趋势, 其中以小波系数得到的相关性最高、 高频信息次之, 植被指数最低。 （2）对比分析每个生育期的3种估算模型, 以VIs+CWT为输入变量的估算效果最好, VIs+IWD的估算效果次之, 而VIs的估算效果最差, 说明基于小波分析构建的模型适用性较广、 稳定性较强。 （3）每个生育期分别以3种变量利用PLSR方法构建的AGB估算模型均在块茎增长期达到最高精度（VIs: 建模R2=0.70, RMSE=98.88 kg·hm-12, NRMSE=11.63%; VIs+IWD: 建模R2=0.78, RMSE=86.45 kg·hm-12, NRMSE=10.17%; VIs+CWT: 建模R2=0.85, RMSE=74.25 kg·hm-12, NRMSE=9.27%）。 通过VIs分别结合IWD和CWT技术利用PLSR建模方法, 可以提高AGB估算精度, 为农业指导管理提供可靠参考。

It is essential to estimate above-ground biomass (AGB) quickly and accurately, and AGB is an important indicator of crop growth evaluation and yield prediction. Due to the saturation of AGB in multiple growth periods estimated by traditional vegetation indexes (VIs). Therefore, the study attempts to use VIs combined high-frequency information extracted by image wavelet decomposition (IWD) based on discrete wavelet transform (DWT) technology and wavelet coefficients extracted by continuous wavelet transform (CWT) technology, explore the estimation capabilities of VIs, VIs+IWD and VIs+CWT for AGB. Firstly, the hyperspectral and digital images of the unmanned aerial vehicle (UAV) and measured AGB were acquired during the potato budding stage, tuber formation stage, tuber growth stage, and starch accumulation stage. Secondly, three high-frequency information were extractedby using digital images through IWD technology, wavelet coefficients were extracted by using hyperspectral reflectance through CWT technology and six hyperspectral vegetation indexes were constructed. Then, the correlation between vegetation index, high-frequency information and wavelet coefficients and AGB was analyzed, and the top 10 bands with high absolute values of correlation coefficients at different scales were selected. Finally, the partial least square regression (PLSR) was used to construct and compare AGB estimation models with VIs, VIs+IWD and VIs+CWT. The results showed that: (1) 6 vegetation indexes, 3 high-frequency information and 10 wavelet coefficients selected in each growth period were significantly correlated with AGB, and the correlation decreased after increased in the whole growth period, in which the wavelet coefficients was the highest, the nextwas high frequency information, and the vegetation index was the lowest. (2) The three estimation models of each growth period were compared and analyzed, the estimation effect of VIs+CWT was the best, and that of VIs was the worst, indicating that the model based on wavelet analysis has wide applicability and strong stability. (3) The AGB estimation models constructed by PLSR method with three variables in each growth period reached the highest accuracy in the tuber growth period (VIs: modeling R2=0.70, RMSE=98.88 kg·hm-2, NRMSE=11.63%; VIs+IWD: modeling R2=0.78, RMSE=86.45 kg·hm-2, NRMSE=10.17%; VIs+CWT: modeling R2=0.85, RMSE=74.25 kg·hm-2, NRMSE=9.27%). The PLSR method through VIs combined with IWD and CWT technology were used to improve the accuracy of AGB estimation, which provide a reliable reference for agricultural guidance and management.