标题：Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance作者：Wang, Junjie; Wang, Tiejun; Skidmore, Andrew K.; Shi, Tiezhu; Wu,Guofeng
来源出版物：REMOTE SENSING 卷：7 期：5 页：5901-5917 DOI：10.3390/rs70505901 出版年：MAY 2015
摘要：The characterization of plant nutrients is important to understand the process of plant growth in natural ecosystems. This study attempted to evaluate the performances of univariate linear regression with various vegetation indices (VIs) and multivariate regression methods in estimating grass nutrients (i.e., nitrogen (N) and phosphorus (P)) with canopy hyperspectral reflectance. Synthetically considering predictive accuracy, simplicity, robustness and interpretation, the successive projections algorithm coupled with multiple linear regression (SPA-MLR) method was considered optimal for grass nutrient estimation at the canopy level, when compared with the performances of 12 statistical modeling methods, i.e., univariate linear regression with nine published VIs and three classical multivariate regression methods (stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector regression (SVR)). The simple ratio index model had comparable performance to SPA-MLR model for P estimation. SPA-MLR provided comparable prediction accuracies with only three first derivative spectral bands for N (715, 731 and 2283 nm) and P (714, 729 and 1319nm) estimations, compared with PLSR and SVR models, which used the full spectrum. Moreover, SPA-MLR provided robust prediction with the lowest bias values for N (-0.007%) and P (0.001%) estimations, and the fitting line between predicted and measured values was closer to the 1:1 line than the other models. Finally, most of the bands selected by SPA-MLR indirectly relate to foliar chlorophyll content, which suggests good physical interpretation.
入藏号：WOS:000357596900007
文献类型：Article
语种：English
扩展关键词：SUCCESSIVE PROJECTIONS ALGORITHM; LEAF NITROGEN CONCENTRATION; VEGETATION INDEXES; VARIABLE SELECTION; CHLOROPHYLL CONTENT; SPECTROSCOPY; PHOSPHORUS; REGRESSION; PASTURE; SPECTRA
通讯作者地址：Wu, Guofeng; Shenzhen Univ, Natl Adm Surveying Mapping & Geoinformat, Key Lab Geoenvironm Monitoring Coastal Zone, Shenzhen 518060, Peoples R China.
电子邮件地址：wjjlight@whu.edu.cn; t.wang@utwente.nl; a.k.skidmore@utwente.nl; tiezhushi@whu.edu.cn; guofeng.wu@szu.edu.cn
地址：
[Wang, Junjie; Shi, Tiezhu] Wuhan Univ, Minist Educ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
[Wang, Junjie; Shi, Tiezhu] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, Wuhan 430079, Peoples R China.
[Wang, Tiejun; Skidmore, Andrew K.] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 Enschede, Netherlands.
[Wu, Guofeng] Shenzhen Univ, Natl Adm Surveying Mapping & Geoinformat, Key Lab Geoenvironm Monitoring Coastal Zone, Shenzhen 518060, Peoples R China.
[Wu, Guofeng] Shenzhen Univ, Shenzhen Key Lab Spatial Temporal Smart Sensing &, Shenzhen 518060, Peoples R China.
[Wu, Guofeng] Shenzhen Univ, Coll Life Sci, Shenzhen 518060, Peoples R China.
研究方向：Remote Sensing
ISSN：2072-4292
