PMID- 24822426
OWN - NLM
STAT- MEDLINE
DCOM- 20150817
LR  - 20181202
IS  - 1000-0593 (Print)
IS  - 1000-0593 (Linking)
VI  - 34
IP  - 2
DP  - 2014 Feb
TI  - [Research on universality of least squares support vector machine method for 
      estimating leaf area index of winter wheat].
PG  - 489-93
AB  - Leaf area index (LAI) is one of the most important parameters for evaluating 
      winter wheat growth status and forecasting its yield. Hyperspectral remote 
      sensing is a new technical approach that can be used to acquire the instant 
      information of vegetation LAI at large scale. This study aims to explore the 
      capability of least squares support vector machines (LS-SVM) method to winter 
      wheat LAI estimation with hyperspectral data. After the compression of PHI 
      airborne data with principal component analysis (PCA), the sample set based on 
      the measured LAI data and hyperspectral reflectance data was established. Then 
      the method of LS-SVM was developed respectively to estimate winter wheat LAI 
      under four different conditions, to be specific, different plant type cultivars, 
      different periods, different nitrogenous fertilizer and water conditions. 
      Compared with traditional NDVI model estimation results, each experiment of 
      LS-SVM model yielded higher determination coefficient as well as lower RMSE 
      value, which meant that the LS-SVM method performed better than the NDVI method. 
      In addition, NDVI model was unstable for winter wheat under the condition of 
      different plant type cultivars, different nitrogenous fertilizer and different 
      water, while the LS-SVM model showed good stability. Therefore, LS-SVM has high 
      accuracy for learning and considerable universality for estimation of LAI of 
      winter wheat under different conditions using hyperspectral data.
FAU - Xie, Qiao-Yun
AU  - Xie QY
FAU - Huang, Wen-Jiang
AU  - Huang WJ
FAU - Liang, Dong
AU  - Liang D
FAU - Peng, Dai-Liang
AU  - Peng DL
FAU - Huang, Lin-Sheng
AU  - Huang LS
FAU - Song, Xiao-Yu
AU  - Song XY
FAU - Zhang, Dong-Yan
AU  - Zhang DY
FAU - Yang, Gui-Jun
AU  - Yang GJ
LA  - chi
PT  - Journal Article
PL  - China
TA  - Guang Pu Xue Yu Guang Pu Fen Xi
JT  - Guang pu xue yu guang pu fen xi = Guang pu
JID - 9424805
RN  - 059QF0KO0R (Water)
RN  - N762921K75 (Nitrogen)
SB  - IM
MH  - Least-Squares Analysis
MH  - Models, Theoretical
MH  - Nitrogen
MH  - Plant Leaves/*growth & development
MH  - Plants
MH  - Principal Component Analysis
MH  - Support Vector Machine
MH  - Telemetry
MH  - Triticum/*growth & development
MH  - Water
EDAT- 2014/05/16 06:00
MHDA- 2015/08/19 06:00
CRDT- 2014/05/15 06:00
PHST- 2014/05/15 06:00 [entrez]
PHST- 2014/05/16 06:00 [pubmed]
PHST- 2015/08/19 06:00 [medline]
PST - ppublish
SO  - Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Feb;34(2):489-93.