FLUXCOM-X monthly transpiration on global 0.5 degree grid for 2020
11676/2fKyUeU-lvscil3jguwc1j5u (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/
2020-01-01 00:00:00
2020-12-01 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_2020_050_monthly.nc
5 MB (5086747 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
23
0
Technical information
d9f2b251e53e96fb1c8a5de382ec1cd63e6e222fad15235b606702661aad6f19
2fKyUeU+lvscil3jguwc1j5uIi+tFSNbYGcCZhqtbxk
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