太阳成8722(中国)有限公司-GREEN NO.1

标题：Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods作者：Zhai, Yanfang; Cui, Lijuan; Zhou, Xin; Gao, Yin; Fei, Teng; Gao, Wenxiu

来源出版物：INTERNATIONAL JOURNAL OF REMOTE SENSING 卷：34 期：7 页：2502-2018 出版年：APR 10 2013

摘要：Nitrogen, phosphorus, and potassium are some of the most important biochemical components of plant organic matter, and hence, estimation of their contents can help monitor the metabolism processes and health of plants. This study, conducted in the Yixing region of China, aimed to compare partial least squares regression (PLSR) and support vector machine regression (SVMR) methods for estimating the nitrogen (C N), phosphorus (C P), and potassium (C K) contents present in leaves of diverse plants using laboratory-based visible and near-infrared (Vis-NIR) reflectance spectroscopy. A total of 95 leaf samples taken from rice, corn, sesame, soybean, tea, grass, shrub, and arbour plants were collected, and their C N, C P, C K, and Vis-NIR reflectance data were measured in a laboratory. The PLSR and SVMR methods were calibrated to estimate the C N, C P, and C K contents of the obtained samples from spectral reflectance. Cross-validation with an independent data set was employed to assess the performance of the calibrated models. The calibration results indicated that the PLSR method accounted for 59.1%, 50.9%, and 50.6% of the variation of C N, C P, and C K, whereas the SVMR method accounted for more than 90% of the variation of C N, C P, and C K. According to cross-validation, the SVMR method achieved better estimation accuracies, which had determination coefficients of 0.706, 0.722, and 0.704 for C N, C P, and C K, respectively, than the PLSR method, which had determination coefficients of 0.663, 0.643, and 0.541. It was concluded that the SVMR method combined with laboratory-based Vis-NIR reflectance data has the potential to estimate the contents of biochemical components.

入藏号：WOS:000315722200015

文献类型：Article

语种：English

扩展关键词：MULTIPLE LINEAR-REGRESSION; HYPERSPECTRAL DATA; DIFFUSE-REFLECTANCE; CANOPY NITROGEN; SPECTRAL REFLECTANCE; CROSS-VALIDATION; SPECTROMETRY; WHEAT; NIR; INFORMATION

通讯作者地址：Gao, Wenxiu ,Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

电子邮件地址：wxgao@whu.edu.cn

地址：

[Zhai, Yanfang; Zhou, Xin; Gao, Yin; Fei, Teng] WuhanUniv, Sch Resource & Environm Sci,Wuhan430079, Peoples RChina.

[Zhai, Yanfang; Zhou, Xin; Gao, Yin; Fei, Teng]WuhanUniv, Key Lab Geog Informat Syst, Minist Educ,Wuhan430079, Peoples RChina.

[Zhai, Yanfang] Chongqing Inst Surveying & Mapping,Chongqing400014, Peoples RChina.

[Cui, Lijuan] Chinese Acad Forestry, Inst Wetland Res,Beijing100091, Peoples RChina.

[Gao, Wenxiu] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

研究方向：Remote Sensing; Imaging Science & Photographic Technology

ISSN：0143-1161