滞尘影响下的茶树叶片水分高光谱估算

减少叶面滞尘对茶树叶片水分有效光谱信息提取的干扰，有利于建立更加稳健的茶树叶片水分高光谱估算模型。以“舒茶早”为研究对象，通过田间随机采集鲜叶样品，测定叶片原始光谱反射率、含水量以及滞尘率。比较分析滞尘对茶树叶片原始光谱的影响，分别基于归一化计算与比值计算方法构建新波段植被指数，并利用相关系数法筛选叶片水分含量相关性最高的新波段植被指数，结合相对变率分析获取滞尘对叶片水分估算影响不敏感的待选指数。通过分析不同滞尘条件下新建植被指数和已有水分指数与滞尘的响应关系，筛选出滞尘影响下茶树叶片水分估算的最优植被指数，最终构建茶树叶片水分估算的高精度模型。结果表明：（1）位于711~1 378 nm波段范围的叶片光谱反射率受滞尘影响呈现显著降低的趋势，随着滞尘率增大光谱反射率减小，且无尘叶片反射率与有尘状态反射率具有明显聚类现象，相同状态下的不同叶片反射率差异性极显著。（2）新波段植被指数、已有水分指数与茶树叶片含水量之间的相关性以及基于该指数构建的茶树叶片水分估算模型的精度，在滞尘影响下均呈现明显的下降趋势。（3）在滞尘混合状态下，以1 298和1 325 nm为中心波段的新建比值植被指数对滞尘敏感性最低，且与叶片含水量相关性高，为最优植被指数，其建立的茶树叶片水分高光谱估算模型具有较高的预测精度（y=0.245x-0.241，R2=0.854，RMSE=0.001），并且实测值与预测值具有较好的一致性。因此，该研究可为茶树的水分精细化管理提供依据，并可为基于高光谱信息构建复杂环境条件下的水分估算高精度模型提供新思路。

Hyperspectral Estimation of Tea Leaves Water Content Under the Influence of Dust Retention

In order to reduce the influence of dust retention on the extraction of effective spectral information of tea leaf and to establish a more robust water content estimation model of tea leaf by spectrum. We took “Shu Chazao” as the research object and collected samples of tea leaves by random sampling. Then the hyperspectral information, leaf water content and dust retention rate of leaves were measured. The correlation coefficient method was used to extract feature information. Newly-built vegetation indexes were constructed by the normalization calculation method and ratio calculation method, The relative variability analysis was used to screen the candidate indexes that reduce the impact of dust retention on the accuracy of the leaf water content estimation model. By comparing the response relationship between newly-built vegetation indexes and existing water indexes under the different conditions of dust retention, the optimal vegetation index estimation model of tea leaf water content which less affected by dust retention, was selected. Finally, the high-precision estimation models of the tea leaf water content with the optimal vegetation index were established and verified. The results show that, dust leaves’ spectral reflectance is higher than clean leaves in 711~1 378 nm bands. The correlation between the water content of the tea leaves and vegetation index is affected by dust retention, but its correlation direction is not. Dust retention also makes the accuracy value of tea leaf water content estimation model decreased. The newly-built ratio index (RVI_{(1 298, 1 325)}) with 1 298 and 1 325 nm as the center band is least affected by leaf dust retention under complex environmental conditions. Therefore, it is the optimal vegetation index, and the hyperspectral estimation model of tea leaf water content constructed by RVI_{(1 298, 1 325)} has higher estimation accuracy, better sensitivity and stability (y=0.245x-0.241, R2=0.854, RMSE=0.001). In conclusion, this study provides a basis for the refined water management of tea trees and provides new ideas that high-precision models of water content estimation is constructed by hyperspectral information under complex environmental conditions.