Dataset of the journal article "Does increased spatial replication above heterogeneous agroforestry improve the representativeness of eddy covariance measurements?". Preprint of the paper at https://doi.org/10.5194/egusphere-2025-810.

Dataset containing final processed and gap-filled eddy covariance and meteorological data corresponding to the publication "Does increased spatial replication above heterogeneous agroforestry improve the representativeness of eddy covariance measurements?", to be submitted soon to Biogeosciences from Copernicus. There is one file corresponding to each station. Quality flags were developped for gap-filled data, both for eddy covariance and meteorological measurements, as described in the article. The supporting file "variable_names_units" contains information on the units of each variable and describes the variable. Precipitation data are submitted separately because the time resolution is 1h, due to how the gap-filling was performed (please look at publication for more details on it). All variables necessary for footprint analysis are in the files. The folder footprint_by_seasons.zip contains the coordinates of the 80 % footprint contour lines, for all five seasons and all four stations, necessary for figure 3. Additionally, the Python code used to analyze the results and plot the figures for the paper (except for the maps of land cover and footprints) is published. In few months, the whole code used to gap fill both meteorological and flux data, as well as to partition carbon dioxide flux, will be available at a different repository, linked to a data publication.本数据集包含经最终处理与间隙填补后的涡度协方差（eddy covariance）及气象观测数据，对应即将投稿至哥白尼（Copernicus）旗下《Biogeosciences》期刊的论文《异质农林复合系统上方提升空间重复采样是否能改善涡度协方差观测的代表性？》。 每个站点对应一个专属数据文件。如论文所述，我们针对涡度协方差与气象观测的间隙填补数据开发了质量标记（quality flags）。配套文件“variable_names_units”收录了各变量的单位信息并对变量进行说明。降水数据单独提交，因其时间分辨率为1小时，这与间隙填补的实施逻辑相关，详细信息请参阅发表论文。 所有足迹分析（footprint analysis）所需变量均已包含在数据文件中。文件夹“footprint_by_seasons.zip”收录了5个季节、4个站点的80%足迹等值线坐标，可用于绘制论文图3。 此外，用于论文结果分析与图表绘制（土地覆盖与足迹地图除外）的Python代码已公开。数月后，用于气象与通量数据（flux data）间隙填补、以及二氧化碳通量（carbon dioxide flux）分区的完整代码将在另一个关联数据发表成果的代码仓库中上线。