Characterizing ecosystem functional type patterns based on subtractive fuzzy cluster means using Sentinel-2 time-series data

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.

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