利用无人机影像反演水稻SPAD值的最优空间窗口确定  

Optimizing spatial window selection for rice SPAD value retrieval using multispectral UAV images

作　　者：刘一博 裴杰 方华军[3,4] 刘鹏宇 刘四义 邹耀鹏 LIU Yibo;PEI Jie;FANG Huajun;LIU Pengyu;LIU Siyi;ZOU Yaopeng(School of Geospatial Engineering and Science,Sun Yat-sen University,Zhuhai 519082,China;Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China,Ministry of Natural Resources,Zhuhai 519082,China;Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;The Zhongke-Ji’an Institute for Eco-Environmental Sciences,Ji’an 343000,China;State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing 100085,China)

机构地区：[1]中山大学测绘科学与技术学院,珠海519082 [2]自然资源部华南热带亚热带自然资源监测重点实验室,珠海519082 [3]中国科学院地理科学与资源研究所生态系统观测与模拟重点实验室,北京100101 [4]中科吉安生态环境研究院,吉安343000 [5]中国科学院生态环境研究中心城市与区域生态国家重点实验室,北京100085

出　　处：《农业工程学报》2023年第19期165-174,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：广东省基础与应用基础研究基金项目(2021A1515110442);井冈山农高区省级科技专项“揭榜挂帅”项目(20222-051244);中科吉安生态环境研究院院长基金项目(ZJIEES-2022-02);井冈山农高区科技计划项目(井农科字[2021]51号)。

摘　　要：通过无人机多光谱影像反演农作物理化参数、动态监测作物长势是精准农业发展的重要方向。然而,由于无人机影像多具有较高的空间分辨率,地面采样点与影像上对应像素的空间范围往往不匹配,导致所构建的反演模型精度降低。为确定利用无人机多光谱影像反演水稻叶绿素含量的最优空间窗口,该研究分别采集水稻孕穗期、抽穗期和成熟期多光谱影像,以不同大小和形状的空间窗口对影像进行处理并计算多种植被指数,将不同窗口处理的植被指数与地面实测SPAD(soil and plant analyzer development)值进行相关性分析,将相关性最高的一组植被指数所对应的空间窗口确定为最优空间窗口,并以该组植被指数与地面实测SPAD值为依据,分别构建支持向量机、随机森林、极限学习机、广义线性模型和多元逐步回归模型,分析各模型在水稻各生育期对SPAD值的反演精度。结果表明:经过空间窗口处理后各植被指数与SPAD值间的相关系数与处理前相比均有较大提升,圆形空间窗口下各生育期的最优窗口半径分别为35、25、25个像素,方形空间窗口下各生育期的最优窗口边长分别为71、41、61个像素,方形窗口处理效果与圆形窗口近似;利用支持向量机模型反演水稻SPAD值的效果最优,且在孕穗期反演精度最高,决定系数为0.718,均方根误差为1.849,平均绝对误差为1.465。研究结果可为其他作物理化参数反演的空间窗口选择提供参考,为无人机利用多光谱监测作物长势、发展精准农业提供技术支持。Unmanned aerial vehicle(UAV)remote sensing has emerged as a crucial approach in precision agriculture,due to the high timeliness,low cost of data acquisition,and superior spatial resolution.Physical and chemical parameters of crops can be estimated to enable the dynamic monitoring of crop growth using UAV multispectral imagery.However,the high spatial resolution of UAV imagery often leads to the misalignment between ground sampling points and corresponding image pixels,even the accuracy of inversion models.This study aims to investigate the optimal spatial window for the UAV-based multispectral inversion of rice chlorophyll content.A DJI Phantom 4-M UAV was employed to obtain the multispectral images from a rice experimental field in the National Agricultural Science and Technology Park of the Jinggangshan in Xingqiao Town,Ji'an City,Jiangxi Province,China.The images were also collected during the boosting,heading,and maturation stages of rice growth,with a uniform resolution of 2.7 cm per pixel.The UAV was equipped with a multispectral sensor consisting of five spectral bands(450 nm(blue),560 nm(green),650 nm(red),730 nm(red edge),and 840 nm(near-infrared))and a TimeSync time synchronization system with the centimeter-level positioning accuracy.The ground chlorophyll content(SPAD)measurements were obtained concurrently with the UAV multispectral data acquisition using a SPAD-502Plus chlorophyll content meter.The sample area was selected at the center of each rice paddy,where three to five rice plants were sampled.The SPAD value was measured for the upper,middle,and lower sections of each rice leaf.The average value of each rice plant was then determined to represent the SPAD value of the sample.Additionally,the latitude and longitude of the sampling points were recorded using network RTK services.Spatial windows of varying sizes and shapes were employed to process the acquired images.Various vegetation indices were computed using the processed images.The correlation coefficients between the vegetation indices ge

关 键 词：无人机 多光谱 水稻 叶绿素含量 空间窗口 反演 

分 类 号：S127[农业科学—农业基础科学] S252