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As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Articles
Constrained Carbon Partitioning: A Self-Trained Physics-Informed Machine Learning Model Refines GPP Estimates From Eddy Covariance Measurements
1 Introduction It is critical to quantify ecosystem carbon inputs via gross primary productivity (GPP) to understand the role that ecosystems play in the climate system and opportunities to enhance natural climate solutions (Novick 2022; Novick et al. 2022). Eddy covariance has quickly become the standard methodology for measuring the net ecosystem-atmosphere flux of carbon dioxide (NEE) at the ecosystem scale (D. Baldocchi 2008, 2014; D. D.
Using Geostationary Satellite Observations and Machine Learning Models to Estimate Ecosystem Carbon Uptake and Respiration at Half Hourly Time Steps at Eddy Covariance Sites
1 Introduction Satellite remote sensing has revolutionized our understanding of the terrestrial carbon cycle (Xiao et al., 2019) and demonstrated the critical role of terrestrial carbon fluxes in determining the trends and variability of atmospheric carbon dioxide concentration (Friedlingstein et al., 2022; Z. Zhu et al., 2016).
Evaluation of the Effect of Sentinel-1 SAR and Environmental Factors in Alfalfa Yield and Quality Estimation
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