Skip to main content

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

Buy Article:

$63.00 plus tax (Refund Policy)

Abstract:

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.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160902912061

Affiliations: 1: Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, PR China,Institute of Agricultural Remote Sensing & Information Application, Huajiachi Campus, Zhejiang University, Hangzhou, PR China 2: Institute of Agricultural Remote Sensing & Information Application, Huajiachi Campus, Zhejiang University, Hangzhou, PR China 3: Institute of Agricultural Remote Sensing & Information Application, Huajiachi Campus, Zhejiang University, Hangzhou, PR China,Zhejiang Meteorological Institute, Hangzhou, PR China

Publication date: 2010-04-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more