Purpose

This study aims to improve the accuracy of hyperspectral estimation of soil organic matter content.

Design/methodology/approach

Based on the uncertainty in spectral estimation, 76 soil samples collected in Zhangqiu District, Jinan City, Shandong Province, were studied in this paper. First, the spectral transformation of the spectral data after denoising was carried out by means of 11 transformation methods such as reciprocal and square, and the estimation factor was selected according to the principle of maximum correlation. Secondly, the grey weighted distance was used to calculate the grey relational degree between the samples to be estimated and the known patterns, and the local linear regression estimation model of soil organic matter content was established by using the pattern samples closest to the samples to be identified. Thirdly, the models were optimized by gradually increasing the number of modeling samples and adjusting the decision coefficient, and a comprehensive index was constructed to determine the optimal predicted value. Finally, the determination coefficient and average relative error are used to evaluate the validity of the model.

Findings

The results show that the maximum correlation coefficient of the seven estimated factors selected is 0.82; the estimation results of 14 test samples are of high accuracy, among which the determination coefficient R² = 0.924, and the average relative error is 6.608%.

Practical implications

Studies have shown that it is feasible and effective to estimate the content of soil organic matter by using grey correlation local linear regression model.

Originality/value

The paper succeeds in realizing both the soil organic matter hyperspectral grey relation estimating pattern based on the grey relational theory and the estimating pattern by using the local linear regression.

中文翻译：

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基于灰色关联局部回归模型的土壤有机质含量高光谱估计

目的

本研究旨在提高土壤有机质含量高光谱估计的准确性。

设计/方法/方法

基于光谱估计的不确定性，本文对山东省济南市章丘区采集的76个土壤样品进行了研究。首先，通过倒数、平方等11种变换方法对去噪后的光谱数据进行光谱变换，并根据最大相关原则选择估计因子。其次，利用灰度加权距离计算待测样本与已知模式的灰色关联度，利用距离待测样本最近的模式样本建立土壤有机质含量的局部线性回归估计模型。确定。第三，通过逐步增加建模样本数和调整决策系数来优化模型，并构建综合指标确定最优预测值。最后用决定系数和平均相对误差来评价模型的有效性。

发现

结果表明，选取的7个估计因子的最大相关系数为0.82；14个测试样本的估计结果精度较高，其中决定系数R ²  = 0.924，平均相对误差为6.608%。

实际影响

研究表明，利用灰色相关局部线性回归模型估算土壤有机质含量是可行和有效的。

原创性/价值

论文成功地实现了基于灰色关联理论的土壤有机质高光谱灰色关联估计模式和利用局部线性回归的估计模式。