首页 | 本学科首页   官方微博 | 高级检索  
   检索      

一种估测小麦冠层氮含量的新高光谱指数
引用本文:梁亮,杨敏华,邓凯东,张连蓬,林卉,刘志霄.一种估测小麦冠层氮含量的新高光谱指数[J].生态学报,2011,31(21):6594-6605.
作者姓名:梁亮  杨敏华  邓凯东  张连蓬  林卉  刘志霄
作者单位:1. 中南大学地球科学与信息物理学院,长沙410083;徐州师范大学测绘学院,徐州221116
2. 中南大学地球科学与信息物理学院,长沙,410083
3. 金陵科技学院动物科学与技术学院,南京,210038
4. 徐州师范大学测绘学院,徐州,221116
5. 吉首大学生物资源与环境科学学院,吉首,416000
基金项目:国家自然科学基金项目(30570279); 中南大学优秀博士论文扶持项目(2008yb024); 江西省数字国土重点实验室开放基金资助项目(DLLJ201009); 河南理工大学矿山空间信息技术国家测绘局重点实验室开放基金资助项目(KLM201011); 江苏省"青蓝工程"资助项目
摘    要:提出了一种估测小麦冠层氮含量的新高光谱指数--微分归一化氮指数(FD-NDNI)。以FieldSpec Pro FR地物光谱仪采集拔节后至孕穗前小麦的冠层光谱190份,随机抽取142份作为训练集,其余48份作为预测集。将光谱以小波阈值去噪法去噪后,利用其525、570 与730 nm处的一阶导数值,采用差值、比值以及归一化的方法构建了12种光谱指数以实现小麦冠层氮含量的估测,并与mNDVI705、mSR以及NDVI705等22种常用指数进行了比较分析。发现指数FD-NDNI对小麦冠层氮含量的估测结果最佳,其估测模型(指数形式)校正集决定系数(C-R2)与预测集决定系数(P-R2)分别达0.818与0.811,优于mNDVI705等常用指数。进一步分析表明,在各指数中,FD-NDNI对叶面积系数最不敏感,可最有效地避免冠层郁闭度等因素对氮含量估测的影响。为优化结果,采用最小二乘支持向量回归算法(LS-SVR)对模型进行了改进,当模型惩罚系数C与RBF核函数参数g取得最优解6.4与1.6时,其C-R2P-R2分别提高至0.846与0.838,具有比指数模型更高的精度。结果表明:FD-NDNI是小麦冠层氮含量估测的优选指数,LS-SVR为建模的优选算法。

关 键 词:高光谱指数  小麦    叶面积指数  遥感  支持向量回归

A new hyperspectral index for the estimation of nitrogen contents of wheat canopy
LIANG Liang,YANG Minhu,DENG Kaidong,ZHANG Lianpeng,LIN Hui and LIU Zhixiao.A new hyperspectral index for the estimation of nitrogen contents of wheat canopy[J].Acta Ecologica Sinica,2011,31(21):6594-6605.
Authors:LIANG Liang  YANG Minhu  DENG Kaidong  ZHANG Lianpeng  LIN Hui and LIU Zhixiao
Institution:School of Geosciences and Info-Physics,Central South University, Changsha 410083,China;School of Geodesy and Geomatics, Xuzhou Normal University, Xuzhou 221116,China;School of Geosciences and Info-Physics,Central South University, Changsha 410083,China;College of Animal Science and Technology, Jinling Institute of Technology, Nanjing 210038, China;School of Geodesy and Geomatics, Xuzhou Normal University, Xuzhou 221116,China;School of Geodesy and Geomatics, Xuzhou Normal University, Xuzhou 221116,China;College of Biology and Environmental Science, Jishou University, Jishou 416000, China
Abstract:The nitrogen content of canopy leaves of wheat (Triticum aestivum L.) is one of the most important indices for monitoring wheat growth and yield. The Kjeldahl procedure as the common method for N assays is time-consuming, labor intensive and invasive. As a modern technique, hyperspectral remote sensing is an effective and non-invasive method for rapid estimation of plant nitrogen contents. In this study, a novel hyperspectral index, first derivative normalized difference nitrogen index (FD-NDNI), was developed to estimate the nitrogen content of wheat canopy by hyperspectral remote sensing technology. A total of 190 canopy samples of wheat ranging from jointing to booting stage were scanned by a FieldSpec Pro FR spectrometer, and the spectral data were then assigned randomly to calibration (142 data) and prediction (48 data), respectively. The data were pretreated by wavelet threshold denoising before analysis. Using the fist derivative spectra at 525, 570 and 730 nm and the methods of difference, ratio and normalization, 12 new hyperspectral indices were developed to quantify the nitrogen content of wheat canopy. These indices were then compared with 22 commonly used hyperspectral indices including mNDVI705, mSR and NDVI705. The accuracy of the index FD-NDNI developed was higher than that by the hyperspectral indices commonly used, as indicated by a calibration coefficient of determination (C-R2) of 0.818 and a predicted coefficient of determination (P-R2) of 0.811 of the estimation predicted by FD-NDNI. A further analysis showed that the FD-NDNI index described an exponential equation, thererfore the FD-NDNI prediction was concise and unaffected by passivation. The sensitivity analysis of the susceptibility of FD-NDNI to interference of canopy density showed that the R2 of the correlation between FD-NDNI and the leaf area index (LAI) was 0.536, which was lower than that between the commonly used hyperspectral indices and LAI. FD-NDNI was least sensitive to LAI among the hyperspectral indices and therefore least affected by canopy density when used to estimate the nitrogen content of wheat canopy. FD-NDNI was therefore an ideal spectral index sensitive to prediction, but insensitive to interference. An algorithm of the least squares support vector regression (LS-SVR) was finally used to optimize the FD-NDNI model. A step-search procedure in which a long step size was set first to determine the range of values and then followed by a short step size to determine the specific values, was carried out for rapid optimization of the penalty coefficient C and the RBF kernel function parameter g of LS-SVR models. When the parameters C and g reached the optimal values of 6.4 and 1.6, respectively, the C-R2 and P-R2 of the model reached 0.846 and 0.838, respectively, which were higher than those of the exponential model, and indicated that the LS-SVR model was more accurate. The results suggested that FD-NDNI was an optimal hyperspectral index for estimation of the nitrogen content of wheat canopy, and LS-SVR algorithm was a preferred modeling method.
Keywords:hyperspectral index  wheat  nitrogen  leaf area index  remote sensing  support vector regression
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《生态学报》浏览原始摘要信息
点击此处可从《生态学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号