Data for "Improving agricultural carbon monitoring with Sentinel-2 and eddy-covariance-based plant productivity estimates"

This archive contains data for the manuscript "Improving agricultural carbon monitoring with Sentinel-2 and eddy-covariance-based plant productivity estimates" submitted for publication in Carbon Management and available as a preprint at https://doi.org/10.22541/essoar.173712580.08052217/v1 The archive consists of the following items: 1. The daily CO2 fluxes from five Eddy Covariance sites in Finland. The data are CSV files under the flux_data directory, with the following columns: - (nameless): Date as YYYY-MM-DD - NEE: Net Ecosystem Exchange (g CO2 m-2 day-1); negative values denote downwards flux - NEE_unc: uncertainty of the Net Ecosystem Exchange (g CO2 m-2 day-1) - GPP: Gross Primary Productivity (g CO2 m-2 day-1) - GPP_unc: uncertainty of the Gross Primary Productivity (g CO2 m-2 day-1) - TER: Total Ecosystem Respiration (g CO2 m-2 day-1) - TER_unc: uncertainty of the Total Ecosystem Respiration (g CO2 m-2 day-1) - Gapfill: fraction of the values that were gap-filled for that day. 2. The fitted GPP model parameters (1000 samples of the posterior distribution; example/params.csv) and an example script (example/example.py) for running the model. Running the script requires the numpyro, pandas and seaborn libraries to be installed.