Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F23%3A00576230" target="_blank" >RIV/86652079:_____/23:00576230 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s41597-023-02473-9" target="_blank" >https://www.nature.com/articles/s41597-023-02473-9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41597-023-02473-9" target="_blank" >10.1038/s41597-023-02473-9</a>
Alternative languages
Result language
angličtina
Original language name
Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
Original language description
Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10509 - Meteorology and atmospheric sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Data
ISSN
2052-4463
e-ISSN
2052-4463
Volume of the periodical
10
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
18
Pages from-to
587
UT code for WoS article
001065045600002
EID of the result in the Scopus database
2-s2.0-85170167023