土壤与作物2025，Vol.14Issue(4)：487-501,15.DOI:10.11689/sc.2025021202
基于联合算法的高光谱土壤有机质含量估测
Estimation of hyperspectral soil organic matter content based on joint algorithm
摘要
Abstract
In view of the large redundancy information of hyperspectral soil organic matter data and the uncertainty of the results of the single feature band selection algorithm,this paper proposes to establish a model by combining multiple single algorithms to screen the feature bands.Firstly,189 soil samples from 13 counties and urban areas of Guizhou Province are collected,and their spectral data are used as data source.Secondly,four feature band screening algorithms,i.e.kings of CARS(Competitive adaptive Reweighted Sampling),UVE(Uninformative Variable Elimination),SPA(Simultaneous Perturbation Algorithms)and IRIV(Itera-tively Retained Informative Variables)feature band screening algorithms are combined,and three optimization joint algorithms are selected among the multiple joint algorithms.Finally,soil organic matter content inversion model is constructed based on support vector machine and random forest.The results show that in the soil organic matter inversion model,the determination coefficient of UVE-SPA-SVM model reaches 0.87(R2=0.87),the root mean square error reaches 8.31(RMSE=8.31),and the relative analysis error is 2.72(RPD=2.72),which showes the best performance among the three optimization joint algorithms.The performance is better than four single screening algorithms(CARS,UVE,SPA,IRIV).It shows that the optimized joint algorithm has obvious advantages over the single algorithm in reducing hyperspectral data and improving model accuracy.By comparing the accuracy of different models,this study aims to verify the superiority of the optimized combined algorithm compared with a single algorithm in the feature band screening,so as to provide a more reliable and efficient algorithm support for the high-precision inversion of soil organic matter content in mountainous cultivated land.关键词
土壤有机质/特征波段/优化联合算法/支持向量机/数据降维Key words
soil organic matter/characteristic band/optimal association algorithm/support vector machine/data dimensionality reduction分类
农业科技引用本文复制引用
陈彦东,汪泓,肖玖军,杨隆姗,彭俊杰,吴建高,唐润润..基于联合算法的高光谱土壤有机质含量估测[J].土壤与作物,2025,14(4):487-501,15.基金项目
国家自然科学基金(42301440) （42301440）
贵州省科技支撑计划项目(黔科合支撑[2021]一般496号) （黔科合支撑[2021]一般496号）
贵州科学院青年基金(黔科院J字[2024]22号). （黔科院J字[2024]22号）
