当前10米分辨率的互花米草大规模分布图难以精确划定群落边界、探测潮沟等内部特征及识别小型斑块。这些局限性进一步影响了空间分布提取与后续分析的准确性。为此，本研究采用\"对象化与亚米级增强的像元物候特征融合法（OSPPF）\"，首次研制了2020年中国大陆亚米级互花米草分布图（CM-SSM）。该方法融合了Sentinel-2卫星的物候特征与谷歌地球影像的空间纹理细节，显著提升了光谱可分性并有效缓解了混合像元效应。与现有10米分辨率中国大陆互花米草产品（CMSA）相比，CM-SSM的总体精度提升14.60%，F1分数提高0.21。尽管CM-SSM（59,371公顷）与CMSA（58,006公顷）的总面积仅相差1,365公顷，但空间分布存在显著差异。以CM-SSM为基准，CMSA的错分与漏分误差总量达34,273公顷（57.73%）。此外，CM-SSM识别的斑块数量（148,072个）超CMSA的17倍，展现出其对破碎化分布格局的卓越探测能力。进一步分析表明，基于CM-SSM估算的土壤有机碳（SOC）储量较最新国家SOC产品高出706.69吉克（23.09%），凸显了高精度制图对互花米草碳核算的重要贡献。这些进展深化了对互花米草入侵动态的理解，支撑了碳汇核算工作，为基于实证的滨海湿地管理与修复提供了科学依据。", "ds_source": "
数据来源于https://zenodo.org/records/16296823 。", "ds_process_way": "
对象化与亚米级增强的像元物候特征融合法（OSPPF）。", "ds_quality": "
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