Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis

Leaf water content is an important variable for understanding plant physiological properties. This study evaluates a spectral analysis approach, continuous wavelet analysis (CWA), for the spectroscopic estimation of leaf gravimetric water content (GWC, %) and determines robust spectral indicators of GWC across a wide range of plant species from different ecosystems. CWA is both applied to the Leaf Optical Properties Experiment (LOPEX) data set and a synthetic data set consisting of leaf reflectance spectra simulated using the leaf optical properties spectra (PROSPECT) model. The results for the two data sets, including wavelet feature selection and GWC prediction derived using those features, are compared to the results obtained from a previous study for leaf samples collected in the Republic of Panama (PANAMA), to assess the predictive capabilities and robustness of CWA across species. Furthermore, predictive models of GWC using wavelet features derived from PROSPECT simulations are examined to assess their applicability to measured data.The two measured data sets (LOPEX and PANAMA) reveal five common wavelet feature regions that correlate well with leaf GWC. All three data sets display common wavelet features in three wavelength regions that span 1732-1736 nm at scale 4, 1874-1878 nm at scale 6, and 1338-1341 nm at scale 7 and produce accurate estimates of leaf GWC. This confirms the applicability of the wavelet-based methodology for estimating leaf GWC for leaves representative of various ecosystems. The PROSPECT-derived predictive models perform well on the LOPEX data set but are less successful on the PANAMA data set. The selection of high-scale and low-scale features emphasizes significant changes in both overall amplitude over broad spectral regions and local spectral shape over narrower regions in response to changes in leaf GWC. The wavelet-based spectral analysis tool adds a new dimension to the modeling of plant physiological properties with spectroscopy data. (c) 2012 Elsevier GmbH. All rights reserved.

叶片含水量是了解植物生理特性的一个重要变量。本研究评估了一种光谱分析方法——连续小波分析（CWA），用于叶片重量含水量（GWC，%）的光谱估算，并确定了来自不同生态系统的多种植物的GWC的可靠光谱指标。CWA既应用于叶片光学特性实验（LOPEX）数据集，也应用于一个由使用叶片光学特性光谱（PROSPECT）模型模拟的叶片反射光谱组成的合成数据集。对这两个数据集的结果，包括小波特征选择以及使用这些特征得出的GWC预测，与之前对巴拿马共和国（PANAMA）采集的叶片样本的研究结果进行了比较，以评估CWA在不同物种间的预测能力和稳健性。此外，还检验了使用从PROSPECT模拟中得出的小波特征建立的GWC预测模型，以评估它们对实测数据的适用性。两个实测数据集（LOPEX和PANAMA）揭示了五个与叶片GWC相关性良好的常见小波特征区域。所有三个数据集在三个波长区域都显示出常见的小波特征，分别是在尺度4下的1732 - 1736 nm、尺度6下的1874 - 1878 nm和尺度7下的1338 - 1341 nm，并且能够准确估算叶片GWC。这证实了基于小波的方法对于估算代表各种生态系统的叶片的GWC的适用性。从PROSPECT得出的预测模型在LOPEX数据集上表现良好，但在PANAMA数据集上不太成功。高尺度和低尺度特征的选择强调了随着叶片GWC的变化，在宽光谱区域的整体振幅以及在较窄区域的局部光谱形状的显著变化。基于小波的光谱分析工具为利用光谱数据对植物生理特性进行建模增添了新的维度。（c）2012爱思唯尔集团。保留所有权利。