基于高光谱遥感的苹果树冠层叶片全氮量估测  

Estimation of Total Nitrogen Content in Canopy Leaves of Apple Trees Based on Hyperspectral Remote Sensing

作　　者：夏媛媛 冯全 杨森 郭发旭 XIA Yuanyuan;FENG Quan;YANG Sen;GUO Faxu(School of Mechanical and Electrical Engineering,Gansu Agricultural University,Lanzhou Gansu 730070,China)

机构地区：[1]甘肃农业大学机电工程学院,甘肃兰州730070

出　　处：《农业工程》2024年第2期20-28,共9页AGRICULTURAL ENGINEERING

基　　金：兰州市科技局计划项目(2021-1-149);甘肃省高等学校产业支撑引导项目(2019C-11)。

摘　　要：快速准确获取大面积果园冠层叶片全氮含量(leaf nitrogen content,LNC)是实现现代精准农业的基本要求。通过无人机高光谱成像仪(391.9~1006.2 nm)采集了甘肃省静宁县两个典型果园的果树冠层光谱图像,包括人工灌溉的苹果示范园与自然降雨的苹果园,综合比较两区共160份冠层叶片样本的原始光谱反射率(OD)、倒数光谱(RT)、对数光谱(LF)和一阶微分光谱(FD),构建任意两个光谱波段集组合的差值光谱指数(difference spectral index,DSI)、土壤调节植被指数(soil adjusted vegetation index,SAVI)、归一化差值光谱指数(normalized different spectral index,NDSI),分析3种光谱指数与叶片氮含量的相关性,利用一元线性回归模型与光谱指数构建两区最佳苹果冠层LNC估测模型。结果表明,人工灌溉区的FD-SAVI(825,536)、自然降雨区的LF-SAVI(854,392)与LNC的相关性最强,并基于FD-SAVI、LF-SAVI构建一元线性回归模型。人工灌溉区构建的FD-SAVI-ULRM估测模型精度最高,验证集R²和ERMSE分别为0.6601和0.0678;自然降雨区构建的LF-SAVI-ULRM估测模型精度最高,验证集R2和ERMSE分别为0.6746和0.0665。试验采用LNC模型绘制出两个试验区的苹果树冠层叶片LNC估测图,实现对果园叶片全氮含量的精准掌握及精细化管理。Quickly and accurately obtaining total nitrogen content(LNC)of canopy leaves in large-scale orchards is a basic requirement for achieving modern precision agriculture.Canopy spectral images of two typical orchards in Jingning County,Gansu Province were collected using a drone hyperspectral imager(391.9 to 1006.2 nm),including artificially irrigated apple demonstration orchards and naturally rained apple orchards.Original spectral reflectance(OD),reciprocal spectrum(RT),logarithmic spectrum(LF),and first-order differential spectrum(FD)of 160 canopy leaf samples from two regions were comprehensively compared.Difference spectral index(DSI),soil adjusted vegetation index(SAVI),and normalized differential spectral index(NDSI)for any combination of two spectral bands were constructed,correlation between three spectral indices and leaf nitrogen content was analyzed,and a univariate linear regression model and spectral indices were used to construct the best LNC estimation model for apple canopy in two regions.Research showed that correlation between FD-SAVI(825,536)in artificial irrigation areas and LF-SAVI(854,392)in natural rainfall areas was the strongest,and a univariate linear regression model was constructed based on FD-SAVI and LF-SAVI.FDSAVI-ULRM estimation model constructed in artificial irrigation areas has the highest accuracy and validation set R2 and ERMSE were 0.6601 and 0.0678;LF-SAVI-ULRM estimation model constructed in natural rainfall areas has the highest accuracy,with validation set R2 and ERMSE was 0.6746 and 0.0665.This experiment used the LNC model to draw the LNC estimation maps of apple tree canopy leaves in two experimental areas,achieving precise control and refined management of total nitrogen content of orchard leaves.

关 键 词：高光谱 遥感 全氮含量 植被指数 反演 苹果树 

分 类 号：S24[农业科学—农业电气化与自动化]