FLUXCOM-X daily transpiration on global 0.25 degree grid for 2009
Deprecated data
Latest version(s):
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11676/HASRxmQjaSNR_BpHT1D2YZlh (link)
X-BASE ET_T (Transpiration) 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 transpiration estimates from the eddy covariance data was based on the Transpiration Estimation Algorithm (TEA).
2009-01-01 12:00:00
2009-12-31 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 transpiration on global 0.25 degree grid for 2009, Miscellaneous, https://hdl.handle.net/11676/HASRxmQjaSNR_BpHT1D2YZlh
BibTex
@misc{https://hdl.handle.net/11676/HASRxmQjaSNR_BpHT1D2YZlh, 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 transpiration on global 0.25 degree grid for 2009}, year={2023}, note={X-BASE ET_T (Transpiration) 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 transpiration estimates from the eddy covariance data was based on the Transpiration Estimation Algorithm (TEA).}, keywords={BIOGEOCHEMICAL CYCLES, ECOSYSTEM FUNCTIONS, TERRESTRIAL ECOSYSTEMS, VEGETATION, CARBON, LAND SURFACE, FLUXCOM}, url={https://hdl.handle.net/11676/HASRxmQjaSNR_BpHT1D2YZlh}, publisher={Carbon Portal}, copyright={http://meta.icos-cp.eu/ontologies/cpmeta/icosLicence}, pid={11676/HASRxmQjaSNR_BpHT1D2YZlh} }
RIS
TY - DATA T1 - FLUXCOM-X daily transpiration on global 0.25 degree grid for 2009 ID - 11676/HASRxmQjaSNR_BpHT1D2YZlh PY - 2023 AB - X-BASE ET_T (Transpiration) 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 transpiration estimates from the eddy covariance data was based on the Transpiration Estimation Algorithm (TEA). UR - https://hdl.handle.net/11676/HASRxmQjaSNR_BpHT1D2YZlh 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 -
ET_T_2009_025_daily.nc
353 MB (370451973 bytes)
3
Production
2023-06-21 00:00:00
Gregory Duveiller,
Zayd Hamdi,
Martin Jung,
Basil Kraft,
Jacob A. Nelson,
Sophia Walther,
Ulrich Weber,
Weijie Zhang
Previewable variables
Name | Value type | Unit | Quantity kind | Preview |
---|---|---|---|---|
ET_T | transpiration | mm h-1 | particle flux | Preview |
Statistics
0
0
Technical information
1c0491c66423692351fc1a474f50f6619961aae460b6b044c1365f897757e988
HASRxmQjaSNR/BpHT1D2YZlhquRgtrBEwTZfiXdX6Yg
S: -90, W: -180, N: 90, E: 180
BIOGEOCHEMICAL CYCLES
CARBON
ECOSYSTEM FUNCTIONS
FLUXCOM
LAND SURFACE
TERRESTRIAL ECOSYSTEMS
VEGETATION
biosphere modeling
carbon flux