Mapping average GPP, RE, and NEP for 2000 to 2013 using satellite data integrated into regression-tree models in the conterminous United States
Dates
Publication Date
2017-08-28
Start Date
2000-01-01
End Date
2013-12-31
Citation
Wylie, B.K., Howard, D.M., Dahal, Devendra, Boyte, S.P., and Gilmanov, Tagir, 2017, Mapping average GPP, RE, and NEP for 2000 to 2013 using satellite data integrated into regression-tree models in the conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/F7CR5S8M.
Summary
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 [...]
Summary
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.
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Related External Resources
Type: Related Primary Publication
Boyte, S.P., Wylie, B.K., Howard, D.M., Dahal, Devendra, and Gilmanov, Tagir, 2018, Estimating carbon and showing impacts of drought using satellite data in regression-tree models: International Journal of Remote Sensing, v. 39, n. 2, p. 374-398, https://doi.org/10.1080/01431161.2017.1384592.
