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FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2021

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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.
2021-01-01 12:00:00
2021-12-31 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 2021, Miscellaneous, https://hdl.handle.net/11676/KTbxZpEAqivQVKyNf9Ec8fCv
BibTex
@misc{https://hdl.handle.net/11676/KTbxZpEAqivQVKyNf9Ec8fCv,
  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 2021},
  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/KTbxZpEAqivQVKyNf9Ec8fCv},
  publisher={Carbon Portal},
  copyright={http://meta.icos-cp.eu/ontologies/cpmeta/icosLicence},
  pid={11676/KTbxZpEAqivQVKyNf9Ec8fCv}
}
RIS
TY - DATA
T1 - FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2021
ID - 11676/KTbxZpEAqivQVKyNf9Ec8fCv
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/KTbxZpEAqivQVKyNf9Ec8fCv
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_2021_025_daily.nc
355 MB (372209638 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

2
1

Submission

2023-07-25 11:32:33
2023-07-25 11:19:02

Technical information

2936f1669100aa2bd054ac8d7fd11cf1f0af2832ee5621d0de9520df2acb2208
KTbxZpEAqivQVKyNf9Ec8fCvKDLuViHQ3pUg3yrLIgg
S: -90, W: -180, N: 90, E: 180
BIOGEOCHEMICAL CYCLES CARBON ECOSYSTEM FUNCTIONS FLUXCOM LAND SURFACE TERRESTRIAL ECOSYSTEMS VEGETATION biosphere modeling carbon flux