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1. Machine Learning-Based Prediction of Ecosystem-Scale CO2 Flux Measurements

   https://doi.org/10.3390/land14010124  

   Uyekawa, Jeffrey; Leland, John; Bergl, Darby; Liu, Yujie; Richardson, Andrew D; Lucas, Benjamin (January 2025, Land)

   AmeriFlux is a network of hundreds of sites across the contiguous United States providing tower-based ecosystem-scale carbon dioxide flux measurements at 30 min temporal resolution. While geographically wide-ranging, over its existence the network has suffered from multiple issues including towers regularly ceasing operation for extended periods and a lack of standardization of measurements between sites. In this study, we use machine learning algorithms to predict CO2 flux measurements at NEON sites (a subset of Ameriflux sites), creating a model to gap-fill measurements when sites are down or replace measurements when they are incorrect. Machine learning algorithms also have the ability to generalize to new sites, potentially even those without a flux tower. We compared the performance of seven machine learning algorithms using 35 environmental drivers and site-specific variables as predictors. We found that Extreme Gradient Boosting (XGBoost) consistently produced the most accurate predictions (Root Mean Squared Error of 1.81 μmolm−2s−1, R2 of 0.86). The model showed excellent performance testing on sites that are ecologically similar to other sites (the Mid Atlantic, New England, and the Rocky Mountains), but poorer performance at sites with fewer ecological similarities to other sites in the data (Pacific Northwest, Florida, and Puerto Rico). The results show strong potential for machine learning-based models to make more skillful predictions than state-of-the-art process-based models, being able to estimate the multi-year mean carbon balance to within an error ±50 gCm−2y−1 for 29 of our 44 test sites. These results have significant implications for being able to accurately predict the carbon flux or gap-fill an extended outage at any AmeriFlux site, and for being able to quantify carbon flux in support of natural climate solutions. 

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2. Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

   https://doi.org/10.1029/2024MS004342  

   Xia, Yushu; Sanderman, Jonathan; Watts, Jennifer D; Machmuller, Megan B; Mullen, Andrew L; Rivard, Charlotte; Endsley, Arthur; Hernandez, Haydee; Kimball, John; Ewing, Stephanie A; et al (March 2025, Journal of Advances in Modeling Earth Systems)

   Abstract Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R² > 0.6, RMSE <390 g C m⁻²) relative to net ecosystem exchange of CO₂(NEE) (R² > 0.4, RMSE <180 g C m⁻²). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks withR² = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks. 

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3. A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites

   https://doi.org/10.1038/s41597-022-01570-5  

   Moon, Minkyu; Richardson, Andrew D.; Milliman, Thomas; Friedl, Mark A. (December 2022, Scientific Data)

   Abstract Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products. 

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4. Terrestrial carbon, water and energy fluxes measured by eddy covariance, and associated biomet variables, at three adjacent tundra ecosystems at Imnavait Creek, Alaska, 2022

   https://doi.org/10.18739/A22Z12R2G  

   Bret-Harte, Syndonia; Euskirchen, Eugenie; Edgar, Colin (January 2023, NSF Arctic Data Center)

   This Arctic Observing Network (AON) project focuses on maintaining and expanding our long-term network of measurements of carbon, water, and energy exchange in terrestrial systems in Alaska. These exchanges help regulate the Arctic System and its feedbacks to global climate. Thus, extending long-term observations is a key science priority for the observing-change component of the Study of Environmental Arctic Change (SEARCH). Detecting and interpreting change in arctic carbon (C), water, and energy fluxes requires a continuous year-round record over multiple years. Recent data syntheses and modeling studies of Arctic Carbon balance suggest that tundra is either a carbon dioxide (CO2) sink, a source, or neutral (e.g., McGuire et al., 2009, McGuire et al., 2012) . This uncertainty arises mainly from a lack of data on winter CO2 flux and how tundra responds to recent warming. Because of harsh, remote environments and the lack of line power, long-term measurements of arctic CO2 fluxes over the full year are rare. We have been measuring year-round C, water, and energy fluxes for eleven years in two broadly representative flagship observatories with long-term histories of research, at Imnavait Creek near Toolik Lake, Alaska, and near Cherskiy, Siberia. Similar versions of these eddy covariance and biomet data are available from Ameriflux as sites US-ICx. https://ameriflux.lbl.gov/data/download-data/ Our three Imnavait Creek Alaska sites retained multiple names over the years. The following clarification is needed. The 'official' site name is followed by the technical station name (IC_xxxx), the positional name (Ridge), and the Ameriflux site name (US-ICx), and finally the site coordinates. Wet Sedge tundra (IC_1523, Fen, US-ICs) 68.6058 -149.3110 Tussock tundra (IC_1993, Tussock, US-ICt) 68.6063 -149.3041 Dry Heath tundra (IC_1991, Ridge, US-ICh) 68.6068 -149.2958 

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5. Terrestrial carbon, water and energy fluxes measured by eddy covariance, and associated biomet variables, at three adjacent tundra ecosystems at Imnavait Creek, Alaska, 2023

   https://doi.org/10.18739/A2VM4301C  

   Bret-Harte, Syndonia; Euskirchen, Eugenie; Edgar, Colin (January 2024, NSF Arctic Data Center)

   This Arctic Observing Network (AON) project focuses on maintaining and expanding our long-term network of measurements of carbon, water, and energy exchange in terrestrial systems in Alaska. These exchanges help regulate the Arctic System and its feedbacks to global climate. Thus, extending long-term observations is a key science priority for the observing-change component of the Study of Environmental Arctic Change (SEARCH). Detecting and interpreting change in arctic carbon (C), water, and energy fluxes requires a continuous year-round record over multiple years. Recent data syntheses and modeling studies of Arctic Carbon balance suggest that tundra is either a carbon dioxide (CO2) sink, a source, or neutral (e.g., McGuire et al., 2009, McGuire et al., 2012) . This uncertainty arises mainly from a lack of data on winter CO2 flux and how tundra responds to recent warming. Because of harsh, remote environments and the lack of line power, long-term measurements of arctic CO2 fluxes over the full year are rare. We have been measuring year-round C, water, and energy fluxes for eleven years in two broadly representative flagship observatories with long-term histories of research, at Imnavait Creek near Toolik Lake, Alaska, and near Cherskiy, Siberia. Similar versions of these eddy covariance and biomet data are available from Ameriflux as sites US-ICx. https://ameriflux.lbl.gov/data/download-data/ Our three Imnavait Creek Alaska sites retained multiple names over the years. The following clarification is needed. The 'official' site name is followed by the technical station name (IC_xxxx), the positional name (Ridge), and the Ameriflux site name (US-ICx), and finally the site coordinates. Wet Sedge tundra (IC_1523, Fen, US-ICs) 68.6058 -149.3110 Tussock tundra (IC_1993, Tussock, US-ICt) 68.6063 -149.3041 Dry Heath tundra (IC_1991, Ridge, US-ICh) 68.6068 -149.2958 

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