Improvements of Vis-NIRS Model in The Prediction of Soil Organic Matter Content Using Wavelength Optimization

LIN Zhi-dan; WANG Yu-bing; WANG Ru-jing; WANG Liu-san; LU Cui-ping; ZHANG Zheng-yong; SONG

doi:10.3788/fgxb20163711.1428

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Improvements of Vis-NIRS Model in The Prediction of Soil Organic Matter Content Using Wavelength Optimization

更新时间：2020-08-12

- Improvements of Vis-NIRS Model in The Prediction of Soil Organic Matter Content Using Wavelength Optimization
- Chinese Journal of Luminescence Vol. 37, Issue 11, Pages: 1428-1435(2016)
- 作者机构：
  
  1. 中国科学院合肥智能机械研究所, 安徽合肥 230031
  2. 中国科技大学自动化系, 安徽合肥 230026
  3. 合肥电子工程学院, 安徽合肥 230037
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20163711.1428
  CLC： O235
- Received：30 May 2015，
  
  Revised：25 June 2016，
  
  Published：05 November 2016
- 稿件说明：
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林志丹, 汪玉冰, 王儒敬等. 波长优选对土壤有机质含量可见光/近红外光谱模型的优化[J]. 发光学报, 2016,37(11): 1428-1435

LIN Zhi-dan, WANG Yu-bing, WANG Ru-jing etc. Improvements of Vis-NIRS Model in The Prediction of Soil Organic Matter Content Using Wavelength Optimization[J]. Chinese Journal of Luminescence, 2016,37(11): 1428-1435
林志丹, 汪玉冰, 王儒敬等. 波长优选对土壤有机质含量可见光/近红外光谱模型的优化[J]. 发光学报, 2016,37(11): 1428-1435 DOI： 10.3788/fgxb20163711.1428.

LIN Zhi-dan, WANG Yu-bing, WANG Ru-jing etc. Improvements of Vis-NIRS Model in The Prediction of Soil Organic Matter Content Using Wavelength Optimization[J]. Chinese Journal of Luminescence, 2016,37(11): 1428-1435 DOI： 10.3788/fgxb20163711.1428.

摘要

可见光/近红外光谱模型是土壤属性预测的有效工具。波长优选在光谱建模过程中起着重要作用。文中首先利用从安徽省涡阳县采集的130个砂姜黑土土壤样本获得可见光/近红外光谱，然后利用平滑与多重散射校正联合的光谱预处理方式消除光谱中的无关变量和冗余信息以提高模型预测结果的相关性，再利用SPXY方法挑选建模集样本，分别利用连续投影算法和遗传算法进行波长优选，最后利用留一法进行交互验证建立有机质含量的主成分回归模型。研究结果显示：连续投影算法和遗传算法都可以有效地减少参与建模的波长数并提高模型的准确度，尤其是遗传算法能够更好地提高土壤有机质含量预测精度，其相关系数、预测均方根误差和相对分析误差分别达到0.9316，0.2142和2.3195。通过合适的特征波长选取，不仅计算量可以大大减少，预测精度也会有效提高。

Abstract

Visible-near infrared spectroscopy (Vis/NIRS) is proved to be an effective tool in the prediction of soil properties. Wavelength optimization plays an important role in the construction of Vis-NIRS prediction model. In this article

a total of 130 topsoil samples collected from Guoyang County

Anhui Province

China

were used to establish a Vis-NIRS model for the prediction of organic matter content (OMC) in line concretion black soils. Through comparison

the combined spectral pretreatments of smooth and multiplicative scatter correlation (MSC) were applied to minimize the irrelevant and useless information of the spectra and increase the correlation between spectra and the measured values

and subsequently

SPXY methods were used to select the representative training set. Successive projection algorithm (SPA) and genetic algorithm (GA) were then conducted for wavelength optimization. Finally

the principal component regression (PCR) model was constructed

in which the optimal number of principal components was determined using leave-one-out cross validation technique. Results show that: both SPA and GA can significantly reduce the wavelength and favorably increase the accuracy

especially

GA can greatly improve the prediction accuracy of soil OMC

with

RMSEP and RPD up to 0.9316

0.2142

2.3195

respectively. Conclusively

using appropriate wavelength optimization methods

not only the computational load can be significantly reduced but also the prediction precision can be improved.

关键词

Keywords

references

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