Abstract The leaf optical properties model PROSPECT has been widely used to retrieve foliar chemistry in reverse mode from directional-hemispherical reflectance factor (DHRF) spectra measured with integrating sphere equipped spectrometers. With bidirectional reflectance factor (BRF) spectra, some researchers attempted to invert PROSPECT after a modification to the latest version of the model. However, the retrieval accuracy varies greatly with chemical constituents and can be low for some of them, such as dry matter content. This paper proposes a new approach called PROCWT by coupling PROSPECT with continuous wavelet transform (CWT) to suppress the surface reflectance effect and enhance the absorption features of chemical constituents. Instead of the reflectance spectra, the wavelet coefficient spectra generated after CWT were used to construct the merit function for model inversion. Given that the multi-scale decomposition of CWT enables enhancement of chemical-specific absorption features, the use of PROCWT at different scales of wavelet decomposition could lead to improved retrievals of biochemical parameters. The performance of PROCWT was evaluated for estimating foliar chemicals of wheat and rice crops from BRF spectra measured with a leaf clip equipped spectrometer over a two-year field experiment. PROCWT was also compared with the standard PROSPECT inversion (STANDARD), the PROSPECT inversion with the subtraction of surface reflectance (PROREF), and the simplified PROCOSINE (sPROCOSINE). Our results demonstrated that the contribution of surface reflectance component was significant for BRF spectra and the effect of surface reflectance could be suppressed by PROCWT as well as PROREF and sPROCOSINE. Compared with STANDARD, PROCWT and the two traditional methods significantly improved the retrieval accuracies for pigments and leaf water content, but only PROCWT produced significant improvement for dry matter content with a decrease of 14.79 g/m2 in the root mean squared error (RMSE) (30% of the mean) over the entire experimental dataset by enhancing dry matter absorption features. High scales of wavelet decomposition were favorable for the estimation of carotenoid and water contents and low scales for the estimation of chlorophyll and dry matter contents. The difference in optimal scale revealed the separation of overlapping absorption features attributed to various chemical constituents. In addition, the newest PROSPECT-D outperformed PROSPECT-5B in the retrieval of chlorophyll content but not for carotenoid. This new physically-based approach could be beneficial to analysts attempting to retrieve leaf chemicals from BRF spectra alone and close-range reflectance imagery of crops and even other vegetation types.

中文翻译：

---

PROCWT：将 PROSPECT 与连续小波变换相结合，以改进从叶片双向反射光谱中检索叶片化学成分

摘要 叶光学特性模型 PROSPECT 已广泛用于从配备积分球的光谱仪测量的定向半球反射系数 (DHRF) 光谱中以反向模式检索叶化学。对于双向反射系数 (BRF) 光谱，一些研究人员在对模型的最新版本进行修改后尝试反转 PROSPECT。然而，检索精度因化学成分而异，其中一些化学成分可能较低，例如干物质含量。本文提出了一种称为 PROCWT 的新方法，通过将 PROSPECT 与连续小波变换 (CWT) 耦合来抑制表面反射效应并增强化学成分的吸收特性。而不是反射光谱，CWT后产生的小波系数谱被用来构造模型反演的优值函数。鉴于 CWT 的多尺度分解能够增强化学特异性吸收特征，在不同尺度的小波分解中使用 PROCWT 可以改善生化参数的检索。评估 PROCWT 的性能，用于从 BRF 光谱估计小麦和水稻作物的叶面化学物质，该光谱使用配备叶夹的光谱仪在两年的田间试验中测量。PROCWT 还与标准的 PROSPECT 反演 (STANDARD)、减去表面反射率的 PROSPECT 反演 (PROREF) 和简化的 PROCOSINE (sPROCOSINE) 进行了比较。我们的结果表明，表面反射分量对 BRF 光谱的贡献是显着的，并且表面反射的影响可以被 PROCWT 以及 PROREF 和 sPROCOSINE 抑制。与STANDARD相比，PROCWT和两种传统方法显着提高了色素和叶片水分含量的反演精度，但只有PROCWT对干物质含量有显着提高，均方根误差（RMSE）降低了14.79 g/m2（通过增强干物质吸收特征，在整个实验数据集上增加平均值的 30%。高尺度小波分解有利于估计类胡萝卜素和水含量，低尺度有利于估计叶绿素和干物质含量。最佳尺度的差异揭示了归因于各种化学成分的重叠吸收特征的分离。此外，最新的 PROSPECT-D 在提取叶绿素含量方面优于 PROSPECT-5B，但在类胡萝卜素方面则不然。这种新的基于物理的方法可能有益于试图从 BRF 光谱和作物甚至其他植被类型的近距离反射图像中检索叶子化学物质的分析人员。