Purpose

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Design/methodology/approach

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

Findings

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

Social implications

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

Originality/value

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

中文翻译：

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正反灰色关联度土壤有机质高光谱估计修正模型

目的

为了提高土壤有机质的估算精度，本文旨在建立基于正、逆灰色关联度的土壤有机质含量高光谱估算修正模型。

设计/方法论/途径

基于山东省泰安市岱岳区采集的82个土壤样品数据，首先对土壤样品的光谱数据进行一阶微分、对数倒数一阶微分等变换，得到变换后的相关系数计算光谱数据和土壤有机质含量，并按照最大相关性原则选择估算因子。其次，利用正逆灰色关联度模型对待识别样品进行识别，得到有机质含量的初始估计值。最后，根据待识别样品与其对应的已知模式之间的差异信息，建立了土壤有机质含量初始估计的修正模型，并利用平均相对误差和决定系数。

发现

结果表明，对数倒数一阶微分和平方根一阶微分方法对原始光谱数据进行变换较为有效，可显着提高土壤有机质含量与光谱数据的相关性。修正的土壤有机质高光谱估算模型估算精度较高，11个测试样品的平均相对误差（MRE）为4.091%，判定系数（R ²）为0.936。估计精度高于线性回归模型、BP神经网络和支持向量机模型。应用实例表明，本文提出的基于正反灰色关联度的土壤有机质含量高光谱估计修正模型是可行和有效的。

社会影响

本文模型数学物理意义明确、计算简单、易于编程。该模型不仅充分挖掘和利用了“信息不足、不完全”的已知模式样本的内部信息，而且有效克服了土壤有机质光谱估计中的随机性和灰色不确定性。研究成果不仅丰富了灰色系统理论和方法，也为土壤有机质含量、含水量等土壤性质的高光谱估算提供了新途径。

原创性/价值

论文成功实现了基于正、逆灰色关联度的土壤有机质高光谱估计修正模型，有效处理了光谱估计中的随机性和灰色不确定性。