A continental benchmark dataset for evaluating ecosystem gradient-flux approaches across 47 NEON flux towers

作者：Sparkle L. Malone, Jaclyn H. Matthes, Cove S. Sturtevant, Angel Chen, R. Commane, Kyle Delwiche, Ankur R. Desai, Christopher R. Florian, Jonathan Gewirtzman, Samuel A. Jurado, David E. Reed, Jinshu Chi, Hiroki Iwata, Erik Lundin, Ivan Mammarella, Matthias Peichl, Masahito Ueyama, Camilo Rey-Sanchez · 年份：2026 · DOI：10.5194/essd-2026-413 · 研究领域：Plant Water Relations and Carbon Dynamics、Atmospheric and Environmental Gas Dynamics、Remote Sensing in Agriculture

Abstract. Benchmark datasets for evaluating profile-based ecosystem flux methods across environmental gradients are currently lacking. This limits method evaluation, cross-site intercomparison, and the expansion of tower-based monitoring for gases not routinely measured by the eddy covariance (EC) method. Here, we present a benchmark dataset derived from 47 terrestrial towers in the National Ecological Observatory Network (NEON), integrating co-located EC, concentration profiles, tower geometry, and canopy structural metrics. We developed a dataset to evaluate the performance of three widely used gradient flux approaches – the modified Bowen ratio (MBR), aerodynamic (AE), and wind-profile (WP) methods – against co-located EC measurements of CO₂ and H₂O. We evaluate how canopy structure, sensor height configuration, and data filtering influence agreement with EC across ecosystems, with canopy heights ranging from 0.15 to 53 m. Performance varied strongly among approaches and measurement configurations. Across all ecosystems, 11% of height-pair combinations for CO₂ and 19% for H₂O achieved moderate-to-strong concordance with EC, with the MBR approach providing the most consistent performance. Reliable estimates most often occurred when both sampling heights were above the canopy (AA) or when one level was above the canopy and the other within sparse canopies (AW). Concordance declined as canopy complexity increased, highlighting persistent challenges in tall, complex forests. S...