通过高光谱数据在鲜叶和冠层尺度上反演茶叶品质相关参数

With the advent of hyperspectral remote sensing (also referred to as imaging spectrometry), the remote sensing of vegetation has developed considerably. Hyperspectral reflectance is composed by a large number of narrow and continuously spaced spectral bands, usually over 100 bands along with the visible, near infrared and shortwave infrared spectral plane.Particularly, developments in hyperspectral remote sensing have provided new possibilities for estimation of foliar biochemicals, for the narrow spectral channels in hyperspectral techniques make it possible to detect small changes in narrow absorption features caused by the biochemical characteristics of the object, while in traditional broadband remote sensing, some critical information in specific narrowband may be lost. Today, much research has been undertaken to estimate the chemical composition of plants using reflectance spectroscopy, in the fields of grass, savanna trees, agricultural crops and forests. However, often these studies have focused on the detection of chlorophyll and nutrients such as nitrogen. Although some studies have been carried out on the estimation of other biochemical constituents of vegetation, the use of high spectral resolution data for estimating the biochemical parameters of vegetation needs to be explored. This research assesses the biochemical parameters of fresh tea leaves associated with tea's quality using hyperspectral remote sensing approaches at different scales (leaf, canopy), evaluates the accuracy, and further explores the possibility of constructing a physically based model for tea quality estimation, aiming to study the potential of hyperspectral remote sensing for assessing the quality of tea at large scale in the future. this study will show the possibility of hyperspectral remote sensing techniques to retrieve tea quality parameters, by statistical modelling approaches, or by analysing the mechanism between light and layers of tea leaf chemical composition. It is anticipated that these results provide a basis for the future research, and necessary information as well as refined approaches for assessing the quality of tea using air or space-borne remote sensing over large areas.

茶是我国重要的经济作物之一。鲜叶中的总茶多酚，游离氨基酸含量是影响茶叶品质的关键因素。虽然现有光谱分析技术已可对干粉样品进行定量分析，但目前从茶鲜叶和茶树冠层对茶质量进行遥感反演，特别是对茶品质相关的生化参数的估测还不多见。本申请课题以茶叶作为研究对象，在鲜叶和冠层两个尺度探讨从高光谱遥感数据中反演茶叶品质相关的茶多酚和氨基酸浓度参数。本研究计划建立基于反射光谱的茶鲜叶生化参数反演模型，探索鲜叶和冠层光谱响应相关化学参数浓度变化的模式，总结叶肉细胞结构、水分和冠层结构对反射光谱的影响。还将建立专门针对茶鲜叶的生物化学-光学物理模型（Tea-PROSPECT），将茶叶中重要化学成分茶多酚的光学固有特征融入现存的物理模型中，并基于此尝试建立普适性的茶叶质量反演模型。研究将奠定茶鲜叶和冠层光谱对酚氨比浓度响应的理论基础。

项目背景：精准农业是当今世界农业发展的新潮流，常常通过全球定位系统，地理信息系统和遥感技术，定位、定时、定量地监测农作物生长状况，实施现代化农事管理，谋求最大经济效益和环境效益。高光谱遥感技术，又称成像光谱遥感技术，是在测谱学基础上逐渐发展起来的新型遥感技术。高光谱遥感将光谱波段在一定光谱区域进行细分，使得从地面或空中直接识别植被的精细光谱差异以及内部成分含量成为可能。目前在对茶鲜叶的品质监控上，特别是茶多酚和氨基酸成分的检测上，尚缺乏一种大规模、准实时的有效监测手段。现有化学分析和近红外光谱技术不但费时费力，而且在大范围的茶叶品质时空变化信息获取方面存在局限。本项目旨在使用高光谱遥感数据对茶叶进行质量相关的生物、化学参数反演的机理研究。..研究内容：本项目中讨论的影像茶树质量的化学成分包括总茶多酚，游离氨基酸和可溶性糖，它们的含量和相互之间的配比在很大程度上影响了最终的茶叶口味和品质。项目兼顾不同反演尺度，在干粉、鲜叶和冠层三个尺度，利用NIRS近红外光谱仪，野外光谱仪，无人机机载高光谱成像光谱仪等平台，系统地探讨高光谱遥感技术反演茶叶品质参数的潜力；深入探讨光谱对茶叶质量化学参数相应的光谱——化学机理，注重反演机理的研究，而不仅局限于反演方法的优化；利用机载高光谱成像仪，尝试在近地尺度对茶树冠层生化参数进行反演；通过有针对性的改进叶片辐射传输模型，提高茶鲜叶中多酚类物质的光谱反演普适性和精度。..重要结果：基于独立验证数据的结果显示，对于总茶多酚含量，从干粉到鲜叶再到冠层层次上的实际观测值与模型预测值之间的决定系数R2分别为0.95，0.80和0.84相应的均方根误差分别为0.87，1.56和1.37；和对于游离氨基酸含量，R2依次为0.86，0.82和0.81，相应的均方根误差均为0.11，0.13,0.12；对于可溶性糖含量，三个层次上的R2分别为0.85，0.51和0.60，相应的均方根误差分别为0.20，0.41和0.34。无人机近地遥感尺度上：利用偏最小二乘法预测酚氨比、氨基酸和茶多酚实际观测值与模型预测值之间的决定系数R为0.66，0.62和0.58相应的均方根误差分别为13.27，1.16和10.01。项目针对实验区的茶树在多尺度进行了茶多酚、氨基酸、可溶性糖三大影像茶叶质量的关键质量参数反演并获成功。