[1]余蛟洋,常庆瑞*,由明明,等. 基于高光谱和BP神经网络模型苹果叶片SPAD值遥感估算[J].西北林学院学报,2018,33(2):156-165.[doi:10.3969/j.issn.1001-7461.2018.02.26]
　YU Jiao-yang,CHANG Qing-rui*,YOU Ming-ming,et al. Estimation of Apple leaf SPAD Value Based on Hyperspectrum and BP Neural Network[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2018,33(2):156-165.[doi:10.3969/j.issn.1001-7461.2018.02.26]
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 基于高光谱和BP神经网络模型苹果叶片SPAD值遥感估算()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第33卷
期数:
2018年第2期
页码:
156-165
栏目:
出版日期:
2018-03-31

文章信息/Info

Title:
 Estimation of Apple leaf SPAD Value Based on Hyperspectrum and BP Neural Network
文章编号:
1001-7461(2018)02-0156-10
作者:
 余蛟洋; 常庆瑞*; 由明明; 张卓然; 罗丹
（西北农林科技大学 资源环境学院，陕西 杨陵 712100）
Author(s):
 YU Jiao-yang; CHANG Qing-rui*; YOU Ming-ming; ZHANG Zhuo-ran; LUO Dan
 (College of Resources and Environment,Northwest A&F University,Yangling,Shaanxi 712100,China)
关键词:
 ; 苹果; SPAD值; 高光谱; 光谱特征参数; 逐步回归分析; BP神经网络
Keywords:
 ; apple;  SPAD value;  hyperspectrum;  spectral characteristic parameter;  stepwise regression analysis;  BP neural network
分类号:
S771.8
DOI:
10.3969/j.issn.1001-7461.2018.02.26
文献标志码:
A
摘要:
 叶绿素含量表征植被的营养生长状况，为西北地区苹果的大面积、无损、实时生长监测提供科学依据，使用SVC HR-1024I型便携式野外光谱辐射仪和SPAD-502型叶绿素仪测定不同生育期苹果叶片光谱反射率和SPAD值。分析不同生育期苹果叶片SPAD值及其高光谱变化特征，不同生育期叶片SPAD值与原始光谱反射率和光谱特征参数的相关性，构建基于光谱特征参数的单因素回归模型、多元线性逐步回归模型和基于逐步回归分析的BP神经网络模型，并对反演模型进行验证。结果表明，1）从新梢开始生长到果实成熟，苹果叶片SPAD值呈现先上升后下降趋势；2）基于光谱特征参数构建估算叶片SPAD值的单因素回归模型、多元线性逐步回归模型和基于逐步回归分析的BP神经网络模型均通过显著性检验，在秋梢停止生长期各模型反演和预测精度均最高；3）在各生育期，基于光谱特征参数建立的单因素回归模型中，均以蓝边幅值Db和绿峰面积SRg为自变量建立的回归方程拟合和预测能力最优；4）在各生育期，基于逐步回归分析的BP神经网络模型反演和预测能力较单因素回归模型和多元逐步回归模型表现最优，建模R2和验证R2分别达到0.90和0.84以上，验证RMSE<4.41，验证RE<8.42%，是一种快速、高效估算苹果叶片SPAD值的良好反演方法。
Abstract:
 Growth status of vegetation was characterized by leaf chlorophyll content.In order to provide a scientific basis for the growth monitoring of large scale coverage,lossless and real-time processing of apple trees at northwest region,the models for estimating chlorophyll content of apple leaves at different growth stages based on hyperspectrum were constructed.The field experiments were conducted in Shaozhai Village of Fufeng,Shaanxi Province.During different growth periods,the hyperspectral reflectance of apple leaf measurements was collected by SVC HR-1024I field-portable spectroradiometer,and at the same time,chlorophyll relative content (soil and plant analyzer development,SPAD) of apple leaves was obtained by using SPAD-502.There were totally 120 samples collected at different period,three fourths of which were utilized as the training set and the remaining one quarter as validation set.The model constructed relied on the training set and the validation set was evaluated,respectively.We analyzed the rules between the different growth stages and SPAD value,hyper-spectral reflectance,the correlations between spectral reflectance,17 spectral characteristic parameters and SPAD values of apple leaves at different growth stages.Then single factor regression models and multiple stepwise regression models based on spectral characteristic parameters were established respectively to estimate SPAD value.And 17 spectral characteristic parameters selected by stepwise regression analysis as the input parameters,the measured SPAD values as the output parameters,BP neural network model for each stage was respectively built.Then we compared the predictive power of traditional regression models and multiple stepwise regression models to BP neural network model.The results showed that 1) from shoot-growing stage to fruit maturity stage,SPAD value of apple leaves rose in the first stage,and then decreased.At the same time,the leaf spectral reflectance was gradually getting smaller in the visible light region with the increase of SPAD value,while the leaf spectral reflectance rose in the near infrared region.2) The single factor regression models,multiple stepwise regression models based on spectral characteristic parameters and BP neural network based on stepwise regression analysis were approved by significant testing,which had the highest modeling precision and validation precision at autumn shoot pause growth period.3) The single factor regression models based on blue edge amplitude and green peak area respectively had the highest modeling and prediction accuracy at different growth stages.4) Compared to single factor regression models,multiple stepwise regression models,BP neural network model had the best modeling and verification accuracy in each growth period.The coefficient of determination (R2) for the modeling was higher than 0.90,and the coefficient of determination was greater than 0.84 for the validation set,the corresponding value of root mean square error (RMSE) were lower than 4.41,the relative error (RE) was less than 8.42%.Therefore,BP neural network model is an optimal model for the estimation of apple leaf SPAD value and may provide a theoretical basis for the improvement of remote sensing inversion accuracy of apple chlorophyll content.

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备注/Memo

备注/Memo:
 收稿日期：2017-07-11修回日期：2017-10-10
基金项目：国家高技术研究发展计划（863计划）资助项目（2013AA102401）；中央高校基本科研业务项目（2452017108）。
作者简介：余蛟洋，女，在读硕士，研究方向：资源环境监测与评价。E-mail：yujiaoyang@nwsuaf.edu.cn
*通信作者：常庆瑞，男，博士生导师，研究方向：土地资源与空间信息技术。E-mail:changqr@nwsuaf.edu.cn
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