ICOS

FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2007

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X-BASE GPP (Gross Primary Productivity) is based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce this global product. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorlogical data, plant functional type classification as well as MODIS based vegitation indicies and land surface temperature. XGBoost was used as the machine learning algorithm. The GPP estimates from the eddy covariance data was based on the Nighttime Partitioning method.
2007-01-01 12:00:00
2007-12-31 12:00:00
daily
Gans, F., Duveiller, G., Hamdi, Z., Jung, M., Kraft, B., Nelson, J., Walther, S., Weber, U., Zhang, W. (2023). FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2007, Miscellaneous, https://hdl.handle.net/11676/IVaetrpkR1_WStxH7nfqaPZA
BibTex
@misc{https://hdl.handle.net/11676/IVaetrpkR1_WStxH7nfqaPZA,
  author={Gans, Fabian and Duveiller, Gregory and Hamdi, Zayd and Jung, Martin and Kraft, Basil and Nelson, Jacob A. and Walther, Sophia and Weber, Ulrich and Zhang, Weijie},
  title={FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2007},
  year={2023},
  note={X-BASE GPP (Gross Primary Productivity) is based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce this global product. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorlogical data, plant functional type classification as well as MODIS based vegitation indicies and land surface temperature. XGBoost was used as the machine learning algorithm. The GPP estimates from the eddy covariance data was based on the Nighttime Partitioning method.},
  keywords={BIOGEOCHEMICAL CYCLES, ECOSYSTEM FUNCTIONS, TERRESTRIAL ECOSYSTEMS, VEGETATION, CARBON, LAND SURFACE, FLUXCOM},
  url={https://hdl.handle.net/11676/IVaetrpkR1_WStxH7nfqaPZA},
  publisher={Carbon Portal},
  copyright={http://meta.icos-cp.eu/ontologies/cpmeta/icosLicence},
  pid={11676/IVaetrpkR1_WStxH7nfqaPZA}
}
RIS
TY - DATA
T1 - FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2007
ID - 11676/IVaetrpkR1_WStxH7nfqaPZA
PY - 2023
AB - X-BASE GPP (Gross Primary Productivity) is based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce this global product. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorlogical data, plant functional type classification as well as MODIS based vegitation indicies and land surface temperature. XGBoost was used as the machine learning algorithm. The GPP estimates from the eddy covariance data was based on the Nighttime Partitioning method.
UR - https://hdl.handle.net/11676/IVaetrpkR1_WStxH7nfqaPZA
PB - Carbon Portal
AU - Gans, Fabian
AU - Duveiller, Gregory
AU - Hamdi, Zayd
AU - Jung, Martin
AU - Kraft, Basil
AU - Nelson, Jacob A.
AU - Walther, Sophia
AU - Weber, Ulrich
AU - Zhang, Weijie
KW - BIOGEOCHEMICAL CYCLES
KW - ECOSYSTEM FUNCTIONS
KW - TERRESTRIAL ECOSYSTEMS
KW - VEGETATION
KW - CARBON
KW - LAND SURFACE
KW - FLUXCOM
ER - 
GPP_2007_025_daily.nc
350 MB (367161400 bytes)
3

Production

2023-06-21 00:00:00

Previewable variables

Name Value type Unit Quantity kind Preview
GPP gross primary productivity of carbon gC m-2 d-1 particle flux Preview

Statistics

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Submission

2023-07-25 11:23:22
2023-07-25 11:18:56

Technical information

21569eb6ba64475fd64adc47ee77ea68f6403386261c2f5095a30a795bfa47b8
IVaetrpkR1/WStxH7nfqaPZAM4YmHC9QlaMKeVv6R7g
S: -90, W: -180, N: 90, E: 180
BIOGEOCHEMICAL CYCLES CARBON ECOSYSTEM FUNCTIONS FLUXCOM LAND SURFACE TERRESTRIAL ECOSYSTEMS VEGETATION biosphere modeling carbon flux