基于无人机多光谱的棉花多生育期叶面积指数反演  

Leaf Area Index Inversion of Cotton Based on Drone Multi-Spectral and Multiple Growth Stages

作　　者：石浩磊 曹红霞[1] 张伟杰 朱珊 何子建 张泽[2] SHI HaoLei;CAO HongXia;ZHANG WeiJie;ZHU Shan;HE ZiJian;ZHANG Ze(Key Laboratory of Agricultural Soil and Water Engineering in Arid Areas,Ministry of Education,Northwest A&F University,Yangling 712100,Shaanxi;College of Agronomy,Shihezi University,Shihezi 832003,Xinjiang)

机构地区：[1]西北农林科技大学旱区农业水土工程教育部重点实验室,陕西杨凌712100 [2]石河子大学农学院,新疆石河子832003

出　　处：《中国农业科学》2024年第1期80-95,共16页Scientia Agricultura Sinica

基　　金：国家自然科学基金面上项目(52179047);国家重点研发计划(2022YFD1900401)。

摘　　要：【目的】叶面积指数(leaf area index,LAI)是表征作物长势、光合、蒸腾的重要指标。论文旨在研究不同生育期、多生育期无人机多光谱数据棉花LAI估测模型,明确不同生育期间棉花LAI估测模型变化规律,为实时掌握棉花长势并因地制宜进行田间科学管理提供依据。【方法】利用大疆精灵4多光谱无人机获取棉花现蕾期、初花期、结铃期、吐絮期多光谱图像和RGB图像。选用归一化差植被指数(NDVI)、绿度归一化差植被指数(GNDVI)、归一化差红边指数(NDRE)、叶片叶绿素指数(LCI)、优化的土壤调节植被指数(OSAVI)5种多光谱指数和修正红绿植被指数(MGRVI)、红绿植被指数(GRVI)、绿叶指数(GLA)、超红指数(EXR)、大气阻抗植被指数(VARI)5种颜色指数分别建立棉花各生育期及棉花生长多生育期数据集合,结合打孔法获取地面LAI实测数据,使用机器学习算法中偏最小二乘(PLSR)、岭回归(RR)、随机森林(RF)、支持向量机(SVM)、神经网络(BP)构建棉花LAI预测模型。【结果】覆膜棉花LAI随着生育期的变化呈现先增长后下降的趋势,现蕾期、初花期、结铃期内侧棉花叶面积指数均值均显著大于外侧(P<0.05);选择的指数在各时期彼此间均呈显著相关(P<0.05),总体而言,多光谱指数与颜色指数间的相关性随着生育期的进行而呈现下降趋势,选择的指数在各时期均与棉花LAI相关性显著(P<0.05),多光谱指数相关系数介于0.35—0.85,颜色指数相关系数介于0.49—0.71,相关系数绝对值较大的指数多为多光谱指数,颜色指数与棉花LAI的相关系数绝对值较小;估测模型性能结果显示棉花各生育期模型中多光谱指数优于颜色指数,且各指数模型预测性能随着生育期的变化呈现一定规律性,NDVI是预测棉花LAI的最优指数。从模型结果上看,RF模型和BP模型在各生育期下获得了较高的估计精度。初花期LAI反演模型精度最高,最优【Objective】The leaf area index(LAI)is a vital indicator for evaluating crop growth,photosynthesis,and transpiration.The objective of this study is to explore the cotton LAI estimation models based on multi-spectral data from drones at different growth stages and multiple growth stages,clarify the variation patterns of cotton LAI estimation models during different growth stages,and to provide a basis for real-time understanding of cotton growth and scientific field management tailored to local conditions.【Method】The DJI Elf 4 multi-spectral UAV was used to acquire multi-spectral images and RGB images of cotton at budding stage,initial flowering stage,boll setting and open-boll stages.Five multi-spectral indices,namely normalized difference vegetation index(NDVI),normalized green difference vegetation index(GNDVI),normalized difference red-edge index(NDRE),leaf chlorophyll index(LCI),optimized soil adjusted vegetation index(OSAVI),and five color indices,namely modified green-red vegetation index(MGRVI),green-red vegetation index(GRVI),green leaf algorithm(GLA),excess red index(EXR),and visible atmospherically resistant vegetation index(VARI),were selected to build a data set for each growth stage of cotton and multiple growth stages of cotton growth,respectively.Combined with the punching method to obtain actual ground LAI data,the machine learning algorithms of partial least squares regression(PLSR),ridge regression(RR),random forest(RF),support vector machine(SVM)and back propagation(BP)were used to construct a cotton LAI prediction model.【Result】The LAI of cotton exhibited an increasing and then decreasing pattern during the growth stage.Notably,the mean LAI values of cotton at the inner side of the budding stage,initial flowering stage,and boll setting stage were significantly greater than those at the lateral side(P<0.05).The selected indices exhibited significant correlations with each other across the periods(P<0.05).In general,the correlation between multi-spectral index and color index showed a

关 键 词：棉花 叶面积指数 多光谱指数 颜色指数 无人机多光谱 机器学习 

分 类 号：S562[农业科学—作物学]