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This study develops an innovative framework for estimating forest carbon stocks in Sanjiangyuan National Nature Reserve through synergistic inversion of multi-source remote sensing data. We pioneer the comprehensive fusion of Sentinel-2red-edge spectral features with Sentinel-1SAR datasets. Comparative analysis of machine learning algorithms-Random Forest (RF), Gradient Boosting Regression Trees (GBRT), and K-Nearest Neighbors (KNN)-was conducted, with Recursive Feature Elimination (RFE) optimizing feature selection. Key findings reveal: The GBRT-RFE integration achieved optimal performance (R²=0.84, RMSE=3.50Mg/hm²). Multi-source fusion significantly enhanced estimation accuracy across forest types (broadleaf: R²=0.88; coniferous: R²=0.92). Red-edge indices (e.g., EVI, CI) demonstrated highest feature importance, while SAR textures effectively compensated for optical data limitations in canopy penetration through structural information. The 30-m resolution carbon stock map generated by the optimized model showed values ranging from 6.06 to 39.73Mg/hm² (mean=20.85Mg/hm²), exhibiting distinct topographic and moisture gradients. Methodological breakthroughs include: A topography-adaptive feature selection framework for alpine regions, improving steep-slope (>25°) estimation accuracy by 18%~22%; First successful integration of Sentinel-2red-edge bands (B5-B7, B8A) with Sentinel-1dual-polarization (VV/VH) textures; Development of a regional-scale high-precision estimation model (validated against 126 field plots, RMSE < 15%). This research delivers robust technical support for carbon sink quantification, with direct applications in carbon trading and ecological compensation programs, advancing China's "Dual Carbon" goals (2030/2060).

, authors=Yan-he WANG^{1, 2}, Ying-mei XING^{2, *}, authorsList=Yan-he WANG, Ying-mei XING, authorCompany=null, correspAuthors=Ying-mei XING, 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=1314918363701281757, articleId=1314918362954695643, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=SAR与多光谱协同反演的三江源高寒森林碳储量优化, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

以三江源自然保护区为研究区，创新性地提出了一套基于多源遥感数据协同反演的森林碳储量估测方法．研究首次将Sentinel-2红边波段与Sentinel-1 SAR数据进行深度融合．在模型构建方面，采用随机森林（RF）、梯度提升决策树（GBRT）、K最近邻（KNN）等算法进行对比分析，并引入递归特征消除（RFE）方法优化特征选择．研究结果表明：GBRT结合RFE特征选择的模型组合表现最优，其决定系数（R²）达0.84，均方根误差（RMSE）为3.50Mg/hm²；多源数据融合显著提升了不同森林类型的估测精度，在阔叶林和针叶林碳储量估测中，决定系数分别达到0.88和0.92；特征重要性分析揭示红边波段特征（如EVI、CI）对模型贡献最大，而SAR纹理特征通过提供林分结构信息，有效补充了光学特征在冠层穿透方面的不足；基于优化模型生成的30m分辨率碳储量分布图显示，碳储量范围为6.06~39.73Mg/hm²，平均值为20.85Mg/hm²，空间分布呈现出明显的地形效应和水分梯度特征．首次提出适用于高寒地区的地形自适应特征优选框架，显著提升了陡坡区估测精度；实现了红边波段与SAR数据的深度融合，克服了传统单一数据源的局限性；并建立了高精度的区域尺度森林碳储量估测模型，为碳汇评估提供了可靠的技术支撑．研究成果可直接应用于碳交易和生态补偿工作，对推动“双碳”目标实现具有重要的实践价值．

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王雁鹤（1983-），男，河南叶县人，高级工程师，兰州大学博士研究生，主要从事遥感生态地质研究．发表论文10余篇．wangyanh0227@163.com.

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王雁鹤（1983-），男，河南叶县人，高级工程师，兰州大学博士研究生，主要从事遥感生态地质研究．发表论文10余篇．wangyanh0227@163.com.

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