AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

Summary: A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or ‘proximal’ remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site‐level eddy‐covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high‐spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions. We provide current best practices for data availability and metadata for proximal remote sensing: spectral reflectance, solar‐induced fluorescence, thermal infrared radiation, microwave backscatter, and LiDAR. Our paper outlines the steps necessary for making these data streams more widespread, accessible, interoperable, and information‐rich, enabling us to address key ecological questions unanswerable from space‐based observations alone and, ultimately, to demonstrate the feasibility of these technologies to address critical questions in local and global ecology.

This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles in the American Geophysical Union Biogeosciences section, and discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (a) Global collaboration, technology transfer, and application (Section 2), (b) Community engagement, community science, education, and stakeholder involvement (Section 3), and (c) Field, experimental, remote sensing, and real‐time data research and application (Section 4). We discuss needs and strategies for implementing ICON and outline short‐ and long‐term goals. The inclusion of global data and international community engagement are key to tackling grand challenges in biogeosciences. Although recent technological advances and growing open‐access information across the world have enabled global collaborations to some extent, several barriers, ranging from technical to organizational to cultural, have remained in advancing interoperability and tangible scientific progress in biogeosciences. Overcoming these hurdles is necessary to address pressing large‐scale research questions and applications in the biogeosciences, where ICON principles are essential. Here, we list several opportunities for ICON, including coordinated experimentation and field observations across global sites, that are ripe for implementation in biogeosciences as a means to scientific advancements and social progress.

Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land-cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution thanks to the use of Landsat imagery; and (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Orphaned wells, meaning unplugged and non-producing wells lacking responsible owners, pose a significant and undersampled environmental challenge due to their vast number and unknown associated emissions. We propose, develop and test an alternative method for estimating emissions from orphaned wells using a forced advection sampling technique (FAST) that can overcome many of the limitations in current methods (cost, accuracy, safety). In contrast to existing ambient Gaussian plume methods, our approach uses a fan-generated flow to force advection between the emission source and a point methane (CH₄) sensor. The fan flow field is characterized using a colocated sonic anemometer to measure the 3D wind profile generated by the fan. Using time-series measurements of CH₄ concentration and wind, a simple estimate of the CH₄ emission rate of the source can be inferred. The method was calibrated using outdoor controlled-release experiments and then tested on four orphaned wells in Lufkin, TX, and Osage County, OK. Our results suggest that the FAST method can provide a low-cost, portable, fast and safe alternative to existing methods with reasonable estimates of orphaned well emissions over a range of leak rates below 40 g h⁻¹ and within certain geometric and atmospheric constraints.

In this work we present results from an intercomparison study between two CS500 temperature-relative humidity sensors and a brand new sonic anemometer/gas analyzer (Campbell Scientific Instruments – CSI IRGASON). The objective of the intercomparison was an upcoming micrometeorological experiment. For the intercomparison, all sensors were positioned at the same height over the water surface, at the Rio Verde reservoir, in the vicinity of Curitiba, PR, Brazil. The compared variables were air temperature and relative humidity. The CS500 air temperatures were compared to those from a CSI 107 temperature sensor operates with the IRGASON; for the relative humidity comparisons, water vapor concentration measured by the IRGASON was converted to an equivalent relative humidity. Standard linear correlations were calculated. The CS500s displayed good agreement with the IRGASON-derived values.