Estimating carbon fluxes using satellite data integrated into regression-tree models in the conterminous United States

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, enhancing a more complete understanding of broad-scale ecosystem processes. This data release presents maps of estimates of annual gross primary production (GPP) and annual ecosystem respiration (RE) that were derived from weekly summaries of gross photosynthesis (Pg) and ecosytem respiration (Re). To conduct this study we used carbon data from flux towers that are scattered strategically across the conterminous United States (CONUS). We also calculate and present a map of average annual net ecosystem production (NEP). We present and analyze carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Our study experienced correlation coefficients (r) greater than or equal to 0.94 between training and estimated data for both GPP and RE. We conclude that this modeling method effectively measures carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.