State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Data. 2024 Jun 3;11(1):569. doi: 10.1038/s41597-024-03291-3.
Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.
利用涡度相关（EC）技术，如 FLUXNET2015，测量了大量的二氧化碳、能量和水通量数据集。这些数据集被广泛用于验证遥感产品和基准模型。利用 EC 通量数据的主要挑战之一是确定在单个 EC 塔上进行的测量反映模型网格或遥感像素的空间范围。为了最大限度地减少由足迹与目标区域不匹配引起的潜在偏差，使用具有足迹意识的通量数据集非常重要。本研究基于开源 FLUXNET2015 数据，使用已发布的通量足迹模型（SAFE-f），分析了全球 EC 测量的空间代表性。计算的年累积足迹气候图（ACFC）叠加在土地覆盖和植被指数图上，创建了全球通量塔的空间代表性数据集。该数据集包括以下组成部分：（1）ACFC 轮廓（ACFCC）数据和代表每个站点的 50%、60%、70%和 80%ACFCC 的区域，（2）加权 80%ACFC 的每种土地覆盖类型的比例（ACFCW），（3）考虑到 80%ACFCW 使用归一化差异植被指数（NDVI）计算的半变异函数，以及（4）传感器位置偏差（SLB），80%ACFCW 与由 NDVI 代理的指定区域（例如 80%ACFCC 和窗口大小）之间的差异。最后，我们从三个方面对每个站点的代表性进行了全面评估：（1）基础表面覆盖，（2）半变异函数，以及（3）80%ACFCW 与 80%ACFCC 之间的 SLB，并将它们分为 3 个级别。创建此数据集的目的是为国际研究人员提供数据质量指导，以便他们在未来有效利用 FLUXNET2015 数据集。
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