FLUXCOM-X monthly transpiration on global 0.5 degree grid for 2016
11676/O_WPBBtgC_H5DrxOrn_oxHA5 (link)
The X-BASE products are global fluxes based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce these global products. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorological data, plant functional type classification as well as MODIS based vegetation indices and land surface temperature. XGBoost was used as the machine learning algorithm.
Published paper: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-165/
2016-01-01 00:00:00
2016-12-01 00:00:00
monthly
Nelson, J.A., Walther, S., Jung, M., Gans, F., Kraft, B., Weber, U., Hamdi, Z., Duveiller, G., Zhang, W., 2023. FLUXCOM-X-BASE. https://doi.org/10.18160/5NZG-JMJE
BibTex
@misc{https://doi.org/10.18160/5nzg-jmje, doi = {10.18160/5NZG-JMJE}, url = {https://meta.icos-cp.eu/collections/zfwf1Ak2I7OlziGDTX8Xl6_T}, author = {Nelson, Jacob A. and Walther, Sophia and Jung, Martin and Gans, Fabian and Kraft, Basil and Weber, Ulrich and Hamdi, Zayd and Duveiller, Gregory and Zhang, Weijie}, keywords = {BIOGEOCHEMICAL CYCLES, CARBON, ECOSYSTEM FUNCTIONS, FLUXCOM, LAND SURFACE, TERRESTRIAL ECOSYSTEMS, VEGETATION, biosphere modeling, carbon flux, FLUXCOM-X}, title = {FLUXCOM-X-BASE}, publisher = {ICOS ERIC -- Carbon Portal}, year = {2023}, copyright = {ICOS CCBY4 Data Licence} }
RIS
TY - DATA T1 - FLUXCOM-X-BASE AU - Nelson, Jacob A. AU - Walther, Sophia AU - Jung, Martin AU - Gans, Fabian AU - Kraft, Basil AU - Weber, Ulrich AU - Hamdi, Zayd AU - Duveiller, Gregory AU - Zhang, Weijie DO - 10.18160/5NZG-JMJE UR - https://meta.icos-cp.eu/collections/zfwf1Ak2I7OlziGDTX8Xl6_T AB - The X-BASE products are global fluxes based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce these global products. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorological data, plant functional type classification as well as MODIS based vegetation indices and land surface temperature. XGBoost was used as the machine learning algorithm. Published paper: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-165/ KW - BIOGEOCHEMICAL CYCLES KW - CARBON KW - ECOSYSTEM FUNCTIONS KW - FLUXCOM KW - LAND SURFACE KW - TERRESTRIAL ECOSYSTEMS KW - VEGETATION KW - biosphere modeling KW - carbon flux KW - FLUXCOM-X PY - 2023 PB - ICOS ERIC -- Carbon Portal ER -
ET_T_2016_050_monthly.nc
5 MB (5088146 bytes)
3
Production
2023-11-10 11:00:00
Gregory Duveiller,
Fabian Gans,
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
19
0
Technical information
3bf58f041b600bf1f90ebc4eae7fe8c4703995a67f7de2674323f31c84ee2f59
O/WPBBtgC/H5DrxOrn/oxHA5laZ/feJnQyPzHITuL1k
S: -90, W: -180, N: 90, E: 180
BIOGEOCHEMICAL CYCLES
CARBON
ECOSYSTEM FUNCTIONS
FLUXCOM
FLUXCOM-X
LAND SURFACE
TERRESTRIAL ECOSYSTEMS
VEGETATION
biosphere modeling
carbon flux