Evaluating the performance of PC-ANN for the estimation of rice nitrogen concentration from canopy hyperspectral reflectance

被引:15
|
作者
Yi, Qiuxiang [1 ,2 ]
Huang, Jingfeng [2 ]
Wang, Fumin [2 ]
Wang, Xiuzhen [2 ,3 ]
机构
[1] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Urumqi 830011, Peoples R China
[2] Zhejiang Univ, Inst Agr Remote Sensing & Informat Applicat, Hangzhou 310029, Zhejiang, Peoples R China
[3] Zhejiang Meteorol Inst, Hangzhou 310004, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
ARTIFICIAL NEURAL-NETWORKS;
D O I
10.1080/01431160902912061
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
In this study, a wide range of leaf nitrogen concentration levels was established in field-grown rice with the application of three fertilizer levels. Hyperspectral reflectance data of the rice canopy through rice whole growth stages were acquired over the 350 nm to 2500 nm range. Comparisons of prediction power of two statistical methods (linear regression technique (LR) and artificial neural network (ANN)), for rice N estimation (nitrogen concentration, mg nitrogen g(-1) leaf dry weight) were performed using two different input variables (nitrogen sensitive hyperspectral reflectance and principal component scores). The results indicted very good agreement between the observed and the predicted N with all model methods, which was especially true for the PC-ANN model (artificial neural network based on principal component scores), with an RMSE 0.347 and REP 13.14%. Compared to the LR algorithm, the ANN increased accuracy by lowering the RMSE by 17.6% and 25.8% for models based on spectral reflectance and PCs, respectively.
引用
收藏
页码:931 / 940
页数:10
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