Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models

IF 2.8 3区 农林科学 Q3 ENVIRONMENTAL SCIENCES Journal of Soils and Sediments Pub Date : 2024-08-25 DOI:10.1007/s11368-024-03886-8
Xiao Wang, Jianli Ding, Lijing Han, Jiao Tan, Xiangyu Ge
{"title":"Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models","authors":"Xiao Wang, Jianli Ding, Lijing Han, Jiao Tan, Xiangyu Ge","doi":"10.1007/s11368-024-03886-8","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Prediction of soil particle content is essential for soil texture classification, soil management and agricultural production. This study aimed to achieve high-accuracy predictions of soil particle content in the Ogan-Kucha River Oasis using hyperspectral data and environmental variables.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>We collected 62 representative surface soil samples (depth: 0–10 cm), and conducting indoor soil particle content and spectral measurements. The relationship between environmental variables and soil particle content was analyzed using the Boruta algorithm, and seven three-band spectral indices (TBIs) were constructed using an optimal band algorithm. By integrating environmental covariates and TBIs, soil particle inversion models were developed using the extreme learning machine (ELM), backpropagation neural networks (BP), neural networks optimized with the sparrow search algorithm (SSA-BP), and neural networks optimized with the sparrow search algorithm enhanced by Sine chaos mapping (Sine-SSA-BP).</p><h3 data-test=\"abstract-sub-heading\">Results and discussion</h3><p>The results demonstrated that (1) the Boruta algorithm identified key environmental covariates that affect specific soil particle components; (2) there was significant variation in the correlation between different TBIs and soil particle content, with absolute correlation coefficients ranging from 0.225 to 0.852; (3) the estimation models established by the four machine learning algorithms performed well in predicting soil particle content, particularly for silt (<i>R</i><sup><i>2</i></sup>: 0.664–0.858, RMSE: 11.107–17.128) and clay (<i>R</i><sup><i>2</i></sup>: 0.444–0.857, RMSE: 0.550–1.405), for which higher accuracy was achieved; and (4) compared with the traditional ELM (<i>R</i><sup><i>2</i></sup>: 0.422–0.664), BP (<i>R</i><sup><i>2</i></sup>: 0.487–0.673) and SSA-BP models (<i>R</i><sup><i>2</i></sup>: 0.625–0.777), the Sine-SSA-BP model showed a significant improvement in prediction accuracy, with the highest <i>R</i><sup><i>2</i></sup> reaching 0.858.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Compared to the traditional ELM, BP and SSA-BP models, the Sine-SSA-BP model significantly excelled in predicting soil particle content, offering innovative insights and robust support for soil texture classification and management.</p>","PeriodicalId":17139,"journal":{"name":"Journal of Soils and Sediments","volume":"78 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Soils and Sediments","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11368-024-03886-8","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose

Prediction of soil particle content is essential for soil texture classification, soil management and agricultural production. This study aimed to achieve high-accuracy predictions of soil particle content in the Ogan-Kucha River Oasis using hyperspectral data and environmental variables.

Materials and methods

We collected 62 representative surface soil samples (depth: 0–10 cm), and conducting indoor soil particle content and spectral measurements. The relationship between environmental variables and soil particle content was analyzed using the Boruta algorithm, and seven three-band spectral indices (TBIs) were constructed using an optimal band algorithm. By integrating environmental covariates and TBIs, soil particle inversion models were developed using the extreme learning machine (ELM), backpropagation neural networks (BP), neural networks optimized with the sparrow search algorithm (SSA-BP), and neural networks optimized with the sparrow search algorithm enhanced by Sine chaos mapping (Sine-SSA-BP).

Results and discussion

The results demonstrated that (1) the Boruta algorithm identified key environmental covariates that affect specific soil particle components; (2) there was significant variation in the correlation between different TBIs and soil particle content, with absolute correlation coefficients ranging from 0.225 to 0.852; (3) the estimation models established by the four machine learning algorithms performed well in predicting soil particle content, particularly for silt (R2: 0.664–0.858, RMSE: 11.107–17.128) and clay (R2: 0.444–0.857, RMSE: 0.550–1.405), for which higher accuracy was achieved; and (4) compared with the traditional ELM (R2: 0.422–0.664), BP (R2: 0.487–0.673) and SSA-BP models (R2: 0.625–0.777), the Sine-SSA-BP model showed a significant improvement in prediction accuracy, with the highest R2 reaching 0.858.

Conclusion

Compared to the traditional ELM, BP and SSA-BP models, the Sine-SSA-BP model significantly excelled in predicting soil particle content, offering innovative insights and robust support for soil texture classification and management.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
提高土壤颗粒含量预测精度:先进的高光谱分析和机器学习模型
目的预测土壤颗粒含量对于土壤质地分类、土壤管理和农业生产至关重要。本研究旨在利用高光谱数据和环境变量对奥干-库车河绿洲的土壤颗粒含量进行高精度预测。材料与方法 我们采集了 62 个具有代表性的表层土壤样本(深度:0-10 厘米),并进行了室内土壤颗粒含量和光谱测量。使用 Boruta 算法分析了环境变量与土壤颗粒含量之间的关系,并使用最优波段算法构建了七个三波段光谱指数(TBI)。通过整合环境协变量和 TBI,利用极端学习机(ELM)、反向传播神经网络(BP)、利用麻雀搜索算法优化的神经网络(SSA-BP)以及利用正弦混沌映射增强的麻雀搜索算法优化的神经网络(Sine-SSA-BP)建立了土壤颗粒反演模型。结果与讨论结果表明:(1)Boruta 算法确定了影响特定土壤颗粒成分的关键环境协变量;(2)不同 TBI 与土壤颗粒含量之间的相关性存在显著差异,绝对相关系数从 0.225 到 0.852;(3)四种机器学习算法建立的估算模型在预测土壤颗粒含量方面表现良好,尤其是对淤泥(R2:0.664-0.858,RMSE:11.107-17.128)和粘土(R2:0.444-0.857,RMSE:0.550-1.405)的预测精度较高;(4)与传统的 ELM(R2:0.422-0.664)、BP(R2:0.487-0.结论与传统的 ELM、BP 和 SSA-BP 模型相比,Sine-SSA-BP 模型在预测土壤颗粒含量方面表现突出,为土壤质地分类和管理提供了创新见解和有力支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Soils and Sediments
Journal of Soils and Sediments 环境科学-土壤科学
CiteScore
7.00
自引率
5.60%
发文量
256
审稿时长
3.5 months
期刊介绍: The Journal of Soils and Sediments (JSS) is devoted to soils and sediments; it deals with contaminated, intact and disturbed soils and sediments. JSS explores both the common aspects and the differences between these two environmental compartments. Inter-linkages at the catchment scale and with the Earth’s system (inter-compartment) are an important topic in JSS. The range of research coverage includes the effects of disturbances and contamination; research, strategies and technologies for prediction, prevention, and protection; identification and characterization; treatment, remediation and reuse; risk assessment and management; creation and implementation of quality standards; international regulation and legislation.
期刊最新文献
Enhancing pyromorphite formation through hydroxyapatite application in lead-contaminated, water-unsaturated soils: influence of low percolation velocity and high soil porosity Effect of peanut straw mulching on the soil nitrogen change and functional genes in the Camellia oleifera intercropping system Microbial metabolism strengths carbon sequestration and crop yield in upland red soil after long-term ex situ incorporation of straw “Once upon a time… a beach sand grain”: a bed-time story and scientific outreach activity for young children to increase sediment literacy Desalination of dredged sediments for beneficial use: a case of study for raising agricultural peatlands
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1