Abstract. The characteristics of ecosystem functions are of great significance for biodiversity conservation and ecosystem services. Ecosystem functional types (EFTs) are land surface areas similar in carbon dynamics that are not defined by the structure and composition of vegetation and represent the spatial heterogeneity of ecosystem functions. However, identification of EFTs based on low-resolution remote sensing data cannot satisfy the needs of fine-scale characterization of regional ecosystem functionality patterns, and a more accurate and optimized method of identifying EFTs also deserves attention. Here, we characterize EFTs at a county scale based on subtractive fuzzy cluster means (SUBFCM) and Sentinel-2 time-series data. The normalized difference vegetation index, the fraction of absorbed photosynthetically active radiation, and canopy water content and their derived variables in the growing season were selected as ecosystem functional indicators to characterize regional EFT diversity patterns. The correspondence analysis method was used to reveal relationships between the EFTs and land cover structure information, and further analysis of EFTs was performed with soil type data. Our results showed that the selected variables indicating carbon and water flux of the regional ecosystems could be adopted in ecosystem functional classification. The SUBFCM algorithm can automatically divide EFTs with faster convergence speed and reduced subjectivity. The obtained EFTs based on Sentinel-2 images reflected the internal structure of carbon balance well and the distribution pattern of ecosystem functional diversity at a fine scale. A reference for further optimization of the EFT identification algorithm and development of the understanding of spatial heterogeneity of temperate terrestrial ecosystem functions was provided.

中文翻译：

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使用 Sentinel-2 时间序列数据基于减法模糊聚类表征生态系统功能类型模式

摘要。生态系统功能特征对生物多样性保护和生态系统服务具有重要意义。生态系统功能类型 (EFT) 是碳动态相似的陆地表面区域，不受植被结构和组成的限制，代表生态系统功能的空间异质性。然而，基于低分辨率遥感数据的 EFT 识别不能满足区域生态系统功能模式精细刻画的需要，更准确和优化的 EFT 识别方法也值得关注。在这里，我们基于减法模糊聚类均值 (SUBFCM) 和 Sentinel-2 时间序列数据在县级范围内表征 EFT。归一化差异植被指数，吸收光合有效辐射的分数，选择生长季节的冠层含水量及其衍生变量作为生态系统功能指标来表征区域 EFT 多样性模式。采用对应分析法揭示EFTs与土地覆盖结构信息之间的关系，并结合土壤类型数据对EFTs进行进一步分析。我们的结果表明，在生态系统功能分类中可以采用表明区域生态系统碳和水通量的选定变量。SUBFCM 算法可以自动划分 EFT，收敛速度更快，主观性降低。基于 Sentinel-2 图像获得的 EFT 很好地反映了碳平衡的内部结构和生态系统功能多样性在精细尺度上的分布格局。