基于EFAST和PLS的苹果叶片等效水厚度高光谱估算

叶片等效水厚度(EWT)是评估果树生长状况及产量的一个重要参数。为了快速、准确地估算此参数,该文建立苹果叶片EWT归一化近红外水分指数(NDIWI)和扩展傅里叶幅度灵敏度检测方法和偏最小二乘回归(EFAST-PLS)估算模型并验证。使用2012年和2013年在中国山东省肥城县潮泉镇获取的整个生育期苹果叶片EWT和配套的光谱数据,比较NDIWI和EFAST-PLS联合模型。在EFAST-PLS联合模型中,EFAST用来选择光谱敏感波段,PLS用来回归分析。NDIWI与EFAST-PLS模型的决定系数(R^2)分别为0.2831和0.5628,标准均方根误差(NRMSE)分别为8.00%和6.25...

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Published in农业工程学报 Vol. 32; no. 12; pp. 165 - 171
Main Author 冯海宽 李振海 金秀良 杨贵军 万鹏 郭建华 于海洋 杨福芹 李伟国 王衍安
Format Journal Article
LanguageChinese
Published 国家农业信息化工程技术研究中心,北京 100097 2016
北京市农业物联网工程技术研究中心,北京 100097
农业部农业信息技术重点实验室,北京 100097
北京农业信息技术研究中心,北京 100097
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ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2016.12.024

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Summary:叶片等效水厚度(EWT)是评估果树生长状况及产量的一个重要参数。为了快速、准确地估算此参数,该文建立苹果叶片EWT归一化近红外水分指数(NDIWI)和扩展傅里叶幅度灵敏度检测方法和偏最小二乘回归(EFAST-PLS)估算模型并验证。使用2012年和2013年在中国山东省肥城县潮泉镇获取的整个生育期苹果叶片EWT和配套的光谱数据,比较NDIWI和EFAST-PLS联合模型。在EFAST-PLS联合模型中,EFAST用来选择光谱敏感波段,PLS用来回归分析。NDIWI与EFAST-PLS模型的决定系数(R^2)分别为0.2831和0.5628,标准均方根误差(NRMSE)分别为8.00%和6.25%。研究结果表明:EFAST-PLS模型估算苹果叶片EWT潜力巨大,考虑到应用简单,NDIWI也有可取之处。
Bibliography:spectrum analysis; models; moisture content; apple leaves; equivalent water thickness; extended fourier amplitude sensitivity test; partial least squares; normalized difference infrared water index
11-2047/S
Equivalent water thickness(EWT) is an important parameter for evaluating the growth status and yield of fruit tree. The objectives of this study were(i) to establish and verify a model for the EWT of the apple leaves, in which the regression models, the extended Fourier amplitude sensitivity test- partial least squares(EFAST-PLS), and the normalized difference infrared water index(NDIWI) model were tested, and(ii) to compare the performances of the proposed models respectively using the EFAST-PLS and the NDIWI model. Spectral reflectance of leaves and concurrently the apple leaves' EWT parameters were acquired in Tai'an area, Shandong, China during apple growth seasons of 2012-2013. Firstly, the apple leaves' EWT sensitivity was analyzed through the EFAST and the PROSPECT model; the results showed that the
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2016.12.024