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基于谐波分析算法的干旱区绿洲土壤光谱特性研究

Spectral Characteristics of Oasis Soil in Arid Area Based on Harmonic Analysis Algorithm

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摘要

土壤有机质(SOM)含量是评价土壤肥力的重要指标。以新疆渭-库绿洲为研究区, 对室内获取的SOM含量及反射光谱数据进行Savitzky-Golay (S-G)平滑和一阶微分(FD)预处理。在此基础上, 为减小敏感波段遴选对建模精度的影响, 引入谐波分析(HA)算法对全波段光谱数据进行谐波分解。基于主成分分析(PCA)降维后的7个主分量对SOM含量进行基于反向传播(BP)神经网络、遗传算法(GA)-BP神经网络和多元线性回归(MLR)方法的定量估算, 并对估算精度进行比较。结果表明:HA预处理后的光谱数据与SOM含量的相关性相较于FD数据有了明显提高; 非线性模型BP神经网络的估算精度明显高于线性模型MLR; 在非线性模型中, GA-BP模型的估算精度最高, 其决定系数为0.92, 预测集的均方根误差为3.92×10-3, 相对分析误差为1.93。验证了HA算法深度挖掘光谱数据的有效性, 经过GA优化的BP神经网络模型可以提高SOM含量的估算精度, 为土壤属性的光谱定量估算提供借鉴。

Abstract

The soil organic matter (SOM) content is an important index for evaluating soil fertility. Weigan-Kuqa region in Xinjiang is selected as the study area, based on the laboratory-derived SOM content and reflectance data, the pretreatment of Savitzky-Golay (S-G) smoothing and first order derivative (FD) are carried out. In order to further reduce the influence of sensitive band selection on modeling accuracy, we introduce the harmonic analysis (HA) algorithm to conduct the harmonic decomposition of all wavelengths. Seven principal components are obtained using dimensional reduction treatment of the principal component analysis (PCA). Subsequently, the SOM contents of soil samples are quantified by means of three methods: back propagation (BP) neural network, Genetic Algorithm (GA)-BP, and multiple linear regression (MLR). The accuracy of these methods is compared here. The results show that the correlation coefficient between SOM content and HA pretreated spectral data is improved effectively compared with those of FD data. The estimate accuracy of the non-linear model, BP neural network, is better than that of the linear model, MLR. In terms of non-linear models, the estimate accuracy of the GA-BP model is the best, with the optimal determining coefficient of 0.92, root mean square error of prediction set of 3.92×10-3, and the relative analysis error of 1.93. This study validates the effectiveness of the HA algorithm for the depth mining of spectral data, and the BP neural network model optimized by GA can improve estimate accuracy of SOM content, which can further provide scientific reference for the quantitative estimation of multiple soil properties.

Newport宣传-MKS新实验室计划
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中图分类号:S127

DOI:10.3788/aos201939.0228003

所属栏目:遥感与传感器

基金项目:国家自然科学基金项目(41771470,U1303381,41661046)、自治区重点实验室专项基金项目(2016D03001)

收稿日期:2018-08-13

修改稿日期:2018-09-20

网络出版日期:2018-10-08

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张子鹏:新疆大学资源与环境科学学院, 新疆 乌鲁木齐 830046新疆大学绿洲生态教育部重点实验室, 新疆 乌鲁木齐 830046新疆大学智慧城市与环境建模自治区普通高校重点实验室, 新疆 乌鲁木齐 830046
丁建丽:新疆大学资源与环境科学学院, 新疆 乌鲁木齐 830046新疆大学绿洲生态教育部重点实验室, 新疆 乌鲁木齐 830046新疆大学智慧城市与环境建模自治区普通高校重点实验室, 新疆 乌鲁木齐 830046
王敬哲:新疆大学资源与环境科学学院, 新疆 乌鲁木齐 830046新疆大学绿洲生态教育部重点实验室, 新疆 乌鲁木齐 830046新疆大学智慧城市与环境建模自治区普通高校重点实验室, 新疆 乌鲁木齐 830046

联系人作者:丁建丽(watarid@xju.edu.cn)

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引用该论文

Zhang Zipeng,Ding Jianli,Wang Jingzhe. Spectral Characteristics of Oasis Soil in Arid Area Based on Harmonic Analysis Algorithm[J]. Acta Optica Sinica, 2019, 39(2): 0228003

张子鹏,丁建丽,王敬哲. 基于谐波分析算法的干旱区绿洲土壤光谱特性研究[J]. 光学学报, 2019, 39(2): 0228003

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