干旱区研究

Arid Zone Research >

2014 , Vol. 31 >Issue 6: 1005 - 1009

DOI: https://doi.org/10.13866/j.azr.2014.06.04

Land and Water Resources

PLSR-BP Complex Model-Based Hyper-Spectrum Retrieval of Oasis Soil pH

  • WANG Kai-long ,
  • XIONG Hei-gang ,
  • ZHANG Fang
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  • 1. College of Resources & Environmental Science, Xinjiang University, Urumqi 830046, Xinjiang, China;
    2. College of Art & Science, Beijing Union University, Beijing 100083, China;
    3. Key Laboratory of Oasis Ecology (Xinjiang University), Ministry of Education, Urumqi 830046, Xinjiang, China

Received date: 2013-04-11

  Revised date: 2013-06-13

  Online published: 2014-11-25

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Abstract

Based on soil pH data, measured VIS-NIR reflectance and the data pretreated by multiplicative scatter correction(MSC) at the given spots, soil pH prediction models were established by using principle components analysis-PCA, partial least squares regression -PLSR and back propagation -BP. The results showed that soil pH had a good correlation with both the original reflectance and the spectral data pretreated by MSC. The correction between the soil pH and the spectral data pretreated by MSC was more obvious. PCA and PLSR soil pH prediction models both have good predictability on soil alkalinization. BP neural network model had a lower forecasting precision because of the amount of input variables. However, using the principal components obtained from PCA and PLSR models as input variables, the predictability and the stability of the BP neural network model can be significantly improved. Compared with PLSR, BP and PCA, the prediction results of PLSR-BP model is the best.

Key words： hyper-spectrum; soil alkalinization; multiplicative scatter correction; prediction model; Qitai; Xinjiang

Cite this article

WANG Kai-long , XIONG Hei-gang , ZHANG Fang . PLSR-BP Complex Model-Based Hyper-Spectrum Retrieval of Oasis Soil pH[J]. Arid Zone Research, 2014 , 31(6) : 1005 -1009 . DOI: 10.13866/j.azr.2014.06.04

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