Spectroscopic determination of leaf water content using continuous wavelet analysis
Spectroscopic determination of leaf water content using continuous wavelet analysis
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DOI：
10.1016/j.rse.2010.11.001
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发表时间：
2011-02-15
影响因子：
13.5
通讯作者：
Sanchez-Azofeifa, A.
中科院分区：
文献类型：
--
作者：
Cheng, T.;Rivard, B.;Sanchez-Azofeifa, A.
重量含水率(GWC，%)是衡量叶片含水率的常用指标，它描述了每片叶子的水分与干物质的比率。到目前为止，由于不可预测的水分和干物质比例变化对光谱响应的混杂影响，叶片的光谱反射率和GWC之间的关系还知之甚少。很少有研究试图从叶片的反射光谱估计GWC，特别是对于各种物种。本文利用连续小波分析对巴拿马热带森林中47种植物的265个叶片样品的反射光谱(350-2500 nm)进行了光谱分析。对每个反射光谱进行连续小波变换，以生成作为波长和尺度的函数的小波功率标度图。为了识别对GWC变化最敏感的小波特征(系数)，建立了以干质量(LWCD)和新鲜质量(LWCF)为函数的小波能量与GWC之间的线性关系。在1300-2500 nm范围内观测到的8个小波特征与LWCD具有很强的相关性，但光谱指数与叶片GWC之间的相关性很差。特别是，两个特征捕获了反射光谱宽泛形状的幅度变化，三个特征捕获了以1730 nm和2100 nm附近为中心的干物质(如蛋白质、木质素、纤维素)吸收的形状和深度的变化。用于预测LWCD和LWCF的8个小波特征没有显著差异；然而，用于确定LWCD和LWCF的预测模型不同。由单一小波特征得到的最准确的LWCD和LWCF的均方根误差(RMSE)分别为28.34%(R2=0.62)和4.86%(R2=0.69)。使用组合特征的模型对LWCD和LWCF的预测有显著改善，RMSE分别为26.04%(R2=0.71)和4.34%(R2=0.75)。这些结果为准确估计LWCD和LWCF的短波红外(SWIR)光谱范围内的干物质吸收特征提供了新的见解。这种新出现的光谱分析方法可以应用于其他复杂的数据集，包括广泛的物种，并可能适用于估计基本的叶片生化元素，如氮、叶绿素、纤维素和木质素。(C)2010 Elsevier Inc.保留所有权利。
The gravimetric water content (GWC, %), a commonly used measure of leaf water content, describes the ratio of water to dry matter for each individual leaf. To date, the relationship between spectral reflectance and GWC in leaves is poorly understood due to the confounding effects of unpredictably varying water and dry matter ratios on spectral response. Few studies have attempted to estimate GWC from leaf reflectance spectra, particularly for a variety of species. This paper investigates the spectroscopic estimation of leaf GWC using continuous wavelet analysis applied to the reflectance spectra (350-2500 nm) of 265 leaf samples from 47 species observed in tropical forests of Panama. A continuous wavelet transform was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength and scale. Linear relationships were built between wavelet power and GWC expressed as a function of dry mass (LWCD) and fresh mass (LWCF) in order to identify wavelet features (coefficients) that are most sensitive to changes in GWC. The derived wavelet features were then compared to three established spectral indices used to estimate GWC across a wide range of species.Eight wavelet features observed between 1300 and 2500 nm provided strong correlations with LWCD, though correlations between spectral indices and leaf GWC were poor. In particular, two features captured amplitude variations in the broad shape of the reflectance spectra and three features captured variations in the shape and depth of dry matter (e.g., protein, lignin, cellulose) absorptions centered near 1730 and 2100 nm. The eight wavelet features used to predict LWCD and LWCF were not significantly different; however, predictive models used to determine LWCD and LWCF differed. The most accurate estimates of LWCD and LWCF obtained from a single wavelet feature showed root mean square errors (RMSEs) of 28.34% (R-2 = 0.62) and 4.86% (R-2 = 0.69), respectively. Models using a combination of features resulted in a noticeable improvement predicting LWCD and LWCF with RMSEs of 26.04% (R-2 = 0.71) and 4.34% (R-2 = 0.75), respectively. These results provide new insights into the role of dry matter absorption features in the shortwave infrared (SWIR) spectral region for the accurate spectral estimation of LWCD and LWCF. This emerging spectral analytical approach can be applied to other complex datasets including a broad range of species, and may be adapted to estimate basic leaf biochemical elements such as nitrogen, chlorophyll, cellulose, and lignin. (C) 2010 Elsevier Inc. All rights reserved.