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This study conducted field research in the summer and autumn seasons of 2023 and 2024 at a typical drainage ditch in the Yihe Canal, Hetao Irrigation District. Water quality samples (TN, TP, EC, pH) and UAV multispectral reflectance data were collected to build an inversion model based on statistical regression (SR) and extreme gradient boosting (XGBoost), exploring the correlation between water quality parameters and spectral indices for high-precision inversion. The results indicate that the SR-XGBoost model achieved average R² values of 0.872 and 0.880 in the training and validation sets, respectively, with RRMSE values of 3.46% and 3.23%, significantly outperforming single-band and other combined models. The autumn model exhibited better fitting performance than the summer model, with an average R² increase of 11.50% and an average RRMSE reduction of 1.76%. Spatial distribution results show that pollutant concentrations are higher upstream of the drainage ditch than downstream, with higher concentrations in summer than in autumn and more significant distribution differences. This study reveals the correlation between water quality parameters and spectral indices, validates the high accuracy and stability of the SR-XGBoost model, and provides scientific evidence for applying multispectral remote sensing technology in water quality monitoring.

, authors=Xin-lu LI, Xian-yue LI^*, Hai-bin SHI, Hong-yu MA, Ya-nan WANG, Yue LI, authorsList=Xin-lu LI, Xian-yue LI, Hai-bin SHI, Hong-yu MA, Ya-nan WANG, Yue LI, authorCompany=null, correspAuthors=Xian-yue LI, 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=1314917699382247707, articleId=1314917698602107161, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于SR-XGBoost模型的无人机多光谱水质参数反演——以干旱灌区农田排水沟为例, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

于2023~2024年夏、秋两季在河套灌区义和干渠典型排水沟开展实地调研．通过采集实测水质样本（TN、TP、EC、pH值）和无人机多光谱反射率数据，基于多参数统计回归（SR）与极限梯度提升（XGBoost）构建反演模型，探索水质参数与光谱指数之间的相关性，并进行高精度反演．研究结果表明，SR-XGBoost模型在训练集和验证集中R²的平均值分别为0.872和0.880，RRMSE分别为3.46%和3.23%，显著优于单波段及其他组合模型．秋季模型的拟合效果总体优于夏季，R²平均提高了11.50%，RRMSE平均降低1.76%．空间分布结果显示，排水沟上游污染物浓度高于下游，夏季污染物浓度较秋季更高，且分布差异更为显著．本研究揭示了水质参数与光谱指数的相关关系，验证了SR-XGBoost模型的高精度与稳定性，为多光谱遥感技术在水质监测中的应用提供了科学依据，同时为流域污染防治和水资源管理提供了参考方向．

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李欣璐（2001-），女，内蒙古包头人，内蒙古农业大学硕士研究生，研究方向为环境遥感．lixinlu0224@163.com.

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李欣璐（2001-），女，内蒙古包头人，内蒙古农业大学硕士研究生，研究方向为环境遥感．lixinlu0224@163.com.

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