高光谱定量反演火龙果茎枝叶绿素含量的研究

火龙果是近年来引进我国的营养价值高、经济效益好的新型水果，肉质茎枝是其主要光合器官，与常见果树具有较大差异。为探索以茎枝为光合作用器官的植被的光谱特征及其生化组分的估测方法，以火龙果为研究对象，在贵州省典型种植区罗甸县开展了4个氮肥梯度田间试验，同步测定不同养分丰缺程度下的火龙果茎枝高光谱和相应叶绿素含量数据；然后分析火龙果茎枝光谱数据的演化规律，并采用数学变换、连续小波变换算法并结合相关性分析算法处理分析火龙果茎枝光谱数据，提取并筛选特征波段；最后利用偏最小二乘算法构建火龙果茎枝叶绿素含量估测模型。研究结果表明：（1）火龙果肉质茎枝的原始光谱曲线整体趋势与常见绿叶植物相似，但随施氮量的增加，火龙果近红外处的光谱反射率逐渐降低，变化趋势与常见绿叶植物相反，茎枝光谱的吸收峰（谷）随施氮量的增加呈升高（加深）的趋势。（2）数学变换中的一阶微分与在L₁-L₅尺度内的连续小波变换能有效提升光谱对叶绿素含量的敏感性，火龙果茎枝原始光谱与叶绿素含量的敏感区域主要位于730～1 400 nm，数学变换与连续小波变换均能提升光谱对叶绿素含量的敏感性。与常见绿叶植物相比，火龙果茎枝敏感波段分布相对分散，且多位于730 nm附近与近红外区域（1 100～1 600 nm）。（3）数学变换和连续小波变换能明显提升光谱对火龙果茎枝叶绿素含量的估测能力，其中基于一阶微分的估测模型与基于连续小波变换L₁与L₄的估测模型分别为数学变换与连续小波变换的最优模型，其验证精度分别为R2_验证=0.625，RMSE=0.048，RPD=1.238（一阶微分）；R2_验证=0.678，RMSE=0.037，RPD=1.652（连续小波变换）；表明高光谱技术可以作为火龙果茎枝叶绿素含量和营养诊断的无损监测手段。该研究为完善不同植被类型基于高光谱指数的叶绿素反演提供了补充。

The Quantitative Study on Chlorophyll Content of Hylocereus polyrhizus Based on Hyperspectral Analysis

Pitaya is a new kind of fruit with high nutritional value and good economic benefit which was introduced into China for a short time. Its stems are the most important photosynthetic organs，which is quite different from the common green leaf fruit trees. In order to explore the spectral characteristics and the estimation method of biochemical components of vegetation using stems for photosynthesis，the field experiments were carried out at four nitrogen application levels in Luodian Guizhou，the chlorophyll content of Hylocereus polyrhizus stems were taken as the research object. Firstly，hyperspectral reflection data and chlorophyll content data of Hylocereus polyrhizus stems under different nitrogen nutrient were measured simultaneously；Secondly，the hyperspectral data were analyzed by mathematical transform，continuous wavelet transform（CWT）and correlation analysis algorithm to extract and screen the characteristic bands；Finally，the chlorophyll content estimation model of stem was established by partial least squares regression（PLSA）. The results showed that：（1）The overall trend of the original spectral curve of Hylocereus polyrhizus stems is similar to common green leafed plants，the bands sensitive to chlorophyll content of branches are mostly located in the red edge and near-infrared region. In the near-infrared region，the variation of stems spectrum with nitrogen application is different from that of green leaves. The absorption peak （valley） of Hylocereus polyrhizus branches spectrum increased （deepened） with the increase of nitrogen application. （2）First derivative（FD）and CWT in the scale of L₁-L₅ can effectively improve the sensitivity of the spectrum to chlorophyll content. The sensitive region of the original spectrum and chlorophyll content of Hylocereus polyrhizus stems is located in 730～1 400 nm. Both the mathematical transform and CWTcan significantly improve the sensitivity of the spectrum to chlorophyll content，but the distribution of sensitive bands is relatively scattered，and there are more sensitive bands in the red edge （730 nm） and near infrared region（1 100～1 600 nm），which is different from the distribution of chlorophyll content sensitive bands in leaves. （3）Both the mathematical transformation and CWT can significantly improve the spectral estimation ability of chlorophyll contentin Hylocereus polyrhizus stems. The estimation model based on FD the optimal models of mathematical transformation，the verification accuracy is R2_verification=0.625，RMSE=0.048，RPD=1.238（FD）. The model based on L1 and L4 has relatively high modeling accuracy and estimation accuracy，which is the best model with R2_verification=0.678，RMSE=0.037，RPD=1.652（CWT）. Hyperspectral technology can be used as a non-destructive monitoring method for chlorophyll content and nutrition diagnosis of Pitaya. This study provides a supplement for improving the retrieval of chlorophyll content of different vegetation types based on hyperspectral index.