Article(id=1322559826882839296, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1738684800000, receivedDateStr=2025-02-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252434701, onlineDateStr=2026-10-29, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252434701, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252434701, creator=13701087609, updateTime=1793252434701, updator=13701087609, issue=Issue{id=1322559792544072145, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='1', pageStart='1', pageEnd='306', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252426515, creator='13701087609', updateTime=1793252675339, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322560836246286849, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322560836246286850, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=186, endPage=194, ext={EN=ArticleExt(id=1322559828564755201, articleId=1322559826882839296, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Study on Prediction Model of Tailings Organic Matter Content Based on Hyperspectral Technology, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Over-exploitation of mineral resources aggravates the ecological environmental pressure in mining areas. As solid wastes containing potential heavy metals, tailings cause damage to soil organic matter (SOM) through stockpiling, which has become a key issue in ecological restoration. The SOM monitoring system of tailings was established based on hyperspectral remote sensing technology. Taking a mining area in Tangshan, Hebei Province as the research object, vegetation canopy hyperspectral data were obtained using an ASD ground spectrometer and GF-5 satellite, and tailings samples at the corresponding points were collected for physicochemical property analysis. After screening sensitive bands with the Boruta algorithm, four types of models such as multiple stepwise linear regression (SMLR), partial least squares regression (PLSR), random forest (RF) and extreme gradient boosting (XGBoost) were constructed and verified for accuracy. The results show that the XGBoost model has the best prediction effect on the ground ASD spectral data (R²=0.92, RMSE=0.37), which is suitable for fine monitoring. The GF-5 satellite data perform best with the XGBoost model (R²=0.73, RMSE=0.53), which meets the needs of large-scale rapid assessment. The study confirms that ground-space multi-scale SOM monitoring can be realized through the collaborative optimization of spectral mathematical transformations and linear and nonlinear models, with efficiency significantly enhanced compared with traditional detection methods. The research results can provide a scientific basis for ecological restoration of mining areas, promoting environmental management of mining area and the sustainable development of mining industry.

, authors=Zijing WU¹, Haihong GU^{1, 2, 3, 4, 5}, Shaoping CHENG¹, Zheng ZHOU⁶, Yusong REN¹, Jiali SHAO¹, Yanjun AI¹, Guie TIAN^{1, 2, 3}, authorsList=Zijing WU, Haihong GU, Shaoping CHENG, Zheng ZHOU, Yusong REN, Jiali SHAO, Yanjun AI, Guie TIAN, authorCompany=null, correspAuthors=Haihong GU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322559828791247618, articleId=1322559826882839296, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于高光谱的尾矿有机质含量估测模型研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

矿产资源的过度开发加剧了矿区生态环境压力，尾矿作为含潜在重金属的固体废弃物，其堆存对土壤有机质（SOM）的破坏已成为生态修复的关键问题。基于高光谱遥感技术建立尾矿SOM监测体系，以河北唐山某矿区为研究对象，通过ASD地物光谱仪与GF-5卫星获取植被冠层高光谱数据，采集对应点位尾矿样本进行理化性质分析，结合Boruta算法筛选敏感波段，构建多元逐步线性回归（SMLR）、偏最小二乘回归（PLSR）、随机森林（RF）和极端梯度提升（XGBoost）等四类模型并进行精度验证。结果表明，ASD光谱数据采用XGBoost模型的预测效果最优（R²=0.92，RMSE=0.37），适用于精细化监测；GF-5卫星数据以XGBoost模型表现最佳（R²=0.73，RMSE=0.53），可满足大范围快速评估需求。研究证实，通过光谱数学变换与线性、非线性模型协同优化，可实现地面－空天多尺度SOM监测，较传统检测方法效率显著提升。研究结果可为矿区生态修复提供科学依据，促进矿区环境管理和矿业可持续发展。

, authors=吴梓敬¹, 谷海红^{1, 2, 3, 4, 5}, 成少平¹, 周征⁶, 任宇松¹, 邵佳丽¹, 艾艳君¹, 田桂娥^{1, 2, 3}, authorsList=吴梓敬, 谷海红, 成少平, 周征, 任宇松, 邵佳丽, 艾艳君, 田桂娥, authorCompany=null, correspAuthors=谷海红, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=9w0qQ4AgyV4wY6Pcp2MjgQ==, magXml=u2w/m8li2lI1mhZCtxyVYA==, pdfUrl=null, pdf=aOcNh6Uobp+YTRzc+Amy+w==, pdfFileSize=1866345, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=hXKcSHw1GnGXiMGpzrFycQ==, mapNumber=null, fund=null)}, authors=[Author(id=1322559830049538838, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559826882839296, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zijing1264294689@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322559830456386328, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559826882839296, authorId=1322559830049538838, language=EN, stringName=Zijing WU, firstName=Zijing, middleName=null, lastName=WU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322559830766764825, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559826882839296, authorId=1322559830049538838, language=CN, stringName=吴梓敬, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．华北理工大学 矿业工程学院，河北 唐山市　063210, bio={"content":"

吴梓敬（2000—），男，福建福州人，硕士研究生，研究方向为环境生态修复。E-mail:zijing1264294689@163.com

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吴梓敬（2000—），男，福建福州人，硕士研究生，研究方向为环境生态修复。E-mail:zijing1264294689@163.com

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