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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10509 - Meteorology and atmospheric sciences

Result continuities

  • Project

  • 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