Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu dataset, časopisecké články
PubMed
37679357
PubMed Central
PMC10485062
DOI
10.1038/s41597-023-02473-9
PII: 10.1038/s41597-023-02473-9
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
- dataset MeSH
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.
Agrosphere Institute IBG 3 Forschungszentrum Jülich Jülich 52425 Germany
CESBIO Université de Toulouse CNES CNRS INRAE IRD UPS Toulouse France
Climate Change Dept Environmental Protection Agency of Aosta Valley Saint Christophe 1 11020 Italy
Climate System Research Finnish Meteorological Institute P O Box 503 FI 00101 Helsinki Finland
College of Resources and Environment University of Chinese Academy of Sciences Beijing 100049 China
Department of Agroecology Aarhus University Tjele Denmark
Department of Biology University of Antwerp Wilrijk 2610 Belgium
Department of Computer Vision and Remote Sensing Technische Universität Berlin 10587 Berlin Germany
Department of Environmental Engineering Technical University of Denmark Kgs Lyngby 2800 Denmark
Department of Geography Ghent University Ghent 9000 Belgium
Earth and Life Institute Université Catholique de Louvain 1348 Louvain la Neuve Belgium
ECOSYS INRAE AgroParisTech Université Paris Saclay 22 place de l'agronomie 91120 Palaiseau France
Eurac research Institute for Alpine Environment Viale Druso 1 39100 Bolzano Italy
European Commission Joint Research Centre Ispra Italy
Fundación CEAM Parque Tecnológico C Charles R Darwin 14 Paterna 46980 Spain
GFZ German Research Centre for Geosciences Telegrafenberg 14473 Potsdam Germany
Institut für Ökologie Universität Innsbruck Innrain 52 6020 Innsbruck Austria
Laboratory of Forest Hydrology Graduate School of Agriculture Kyoto University 606 8502 Kyoto Japan
National Agriculture and Food Research Organization Tsukuba Ibaraki 305 8517 Japan
Natural Resources Institute Finland Bioeconomy and environment 00790 Helsinki Finland
Natural Resources Institute Finland Joensuu Yliopistokatu 6 FI 80130 Joensuu Finland
Northwest Institute of Plateau Biology Chinese Academy of Sciences Qinghai Xining 810008 China
Research Faculty of Agriculture Hokkaido University Sapporo Hokkaido 060 8589 Japan
Research Institute for Nature and Forest Geraardsbergen 9500 Belgium
School of Earth Sciences and Engineering Hohai University Nanjing 211100 China
School of Forest Sciences University of Eastern Finland P O Box 111 FIN 80100 Joensuu Finland
School of Life Science Shanxi University Taiyuan 030006 China
School of life sciences Qufu Normal University 57 Jingxuan West Road Qufu 273165 Shandong China
School of Natural Sciences Botany Discipline Trinity College Dublin D2 Dublin Ireland
Sino Belgian Joint Laboratory for Geo Information Ghent Belgium
Sino Belgian Joint Laboratory for Geo Information Urumqi China
Sol Agro et hydrosystèmes Spatialisation UMR 1069 INRAE Institut Agro 35000 Rennes France
Teagasc Johnstown Castle Wexford Ireland
Terra Teaching and Research Center University of Liège Gembloux Agro Bio Tech 5030 Gembloux Belgium
Thünen Institute of Climate Smart Agriculture 38116 Braunschweig Germany
UK Centre for Ecology and Hydrology Bush Estate Penicuik EH26 0QB UK
University of Padova DAFNAE Viale dell'Università 16 1 35020 Padova Legnaro Italy
Wageningen Univertsity Water Systems and Global change group PO bx 47 7700AA Wageningen Netherlands
Zobrazit více v PubMed
Jung M, et al. Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences. 2020;17:1343–1365.
Ciais P, et al. Five decades of northern land carbon uptake revealed by the interhemispheric CO2 gradient. Nature. 2019;568:221–225. PubMed
Jung M, et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data. 2019;6:1–14. PubMed PMC
Jung M, et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature. 2017;541:516–520. PubMed
Wang R, et al. Recent increase in the observation-derived land evapotranspiration due to global warming. Environ. Res. Lett. 2022;17:024020.
Reichstein M, et al. Deep learning and process understanding for data-driven Earth system science. Nature. 2019;566:195–204. PubMed
Li X, et al. Intercomparison of six upscaling evapotranspiration methods: From site to the satellite pixel. J. Geophys. Res.-Atmos. 2018;123:6777–6803.
Shi H, et al. Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis. Hydrol. Earth Syst. Sci. 2022;26:4603–4618.
Shi H, et al. Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation. Biogeosciences. 2022;19:3739–3756.
Xu T, et al. Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. J. Geophys. Res.-Atmos. 2018;123:8674–8690.
Bzdok D, Nichols TE, Smith SM. Towards algorithmic analytics for large-scale datasets. Nat. Mach. Intell. 2019;1:296–306. PubMed PMC
Fatima M, Pasha M. Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 2017;9:1.
Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature. 2015;521:452–459. PubMed
Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349:255–260. PubMed
Zhu AX, Lu G, Liu J, Qin CZ, Zhou C. Spatial prediction based on Third Law of Geography. Ann. GIS. 2018;24:225–240.
Xie M. 2023. Flux station information. figshare. DOI
Che T, Xu Z, Ren Z, Tan J, Zhang Y. 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Zhangye wetland station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Huazhaizi desert steppe station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of mixed forest station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Huazhaizi station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Sidaoqiao superstation, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Zhangye wetland station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of mixed forest station, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Sidaoqiao superstation, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Zhangye wetland station, 2020) National Tibetan Plateau Data Center. DOI
Liu S, Che T, Xu Z, Ren Z, Zhang Y. 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Zhangye wetland station, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of Meteorological elements gradient of A’rou Superstation, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of A’rou Superstation, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Dashalong station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Dashalong station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Jingyangling station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Jingyangling station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Yakou station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Yakou station, 2019) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of Meteorological elements gradient of A’rou Superstation, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of A’rou Superstation, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Jingyangling station, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Jingyangling station, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Yakou station, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2021. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Yakou station, 2020) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Dashalong station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2013) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of Hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2013) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydro-meteorological observation network (automatic weather station of Huazhaizi Desert Steppe Station, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Huazhaizi desert steppe station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Huazhaizi desert Station, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Huazhaizi desert station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of Sidaoqiao superstation, 2013) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of Sidaoqiao Superstation, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2013) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2013) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland station, 2013) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland Station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Huazhaizi desert steppe station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Huazhaizi desert station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of Sidaoqiao Superstation, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland Station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao mixed forest station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Huazhaizi desert steppe station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Huazhaizi desert station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of Hydrometeorological observation network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Sidaoqiao superstation, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Zhangye wetland station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland Station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of mixed forest station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (automatic weather station of Huazhaizi desert steppe station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Huazhaizi station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (automatic weather station of Jingyangling station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of meteorological elements gradient of Sidaoqiao superstation, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Sidaoqiao superstation, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Zhangye wetland station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Zhangye wetland station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of A’rou Superstation, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of A’rou superstation, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of A’rou Superstation, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of A’rou Superstation, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (Dashalong automatic meteorological station, 2013) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Dashalong station, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Dashalong station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of Hydrometeorological observation network (eddy covariance system of Dashalong station, 2013) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2014) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Yakou station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Yakou station, 2015) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydro-meteorological observation network (automatic weather station of Dashalong station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Yakou station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2017. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Yakou station, 2016) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (an observation system of meteorological elements gradient of A’rou Superstation, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of A’rou Superstation, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydro-meteorological observation network (automatic weather station of Dashalong station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Yakou station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2018. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Yakou station, 2017) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of meteorological elements gradient of A’rou Superstation, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of A’rou superstation, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Dashalong station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Jingyangling station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of Yakou station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, 2019. Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Yakou station, 2018) National Tibetan Plateau Data Center. DOI
Liu S, Li X, Xu Z. 2016. HiWATER: Dataset of flux observation matrix (automatic meteorological station of No.1) of the MUlti-Scale Observation EXperiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12) National Tibetan Plateau Data Center. DOI
Liu S, Li X, Xu Z. 2016. HiWATER: The multi-scale observation experiment on evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12)-dataset of flux observation matrix (No.1 eddy covariance system) National Tibetan Plateau Data Center. DOI
Liu S, Li X, Xu Z. 2016. HiWATER: Dataset of flux observation matrix (automatic meteorological station of No.17) of the MUlti-Scale Observation EXperiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12) National Tibetan Plateau Data Center. DOI
Liu S, Li X, Xu Z. 2016. HiWATER: The multi-scale observation experiment on evapotranspiration over heterogeneous land surfaces (MUSOEXE-12)-dataset of flux observation matrix (No.17 eddy covariance system) from Mar to Sep, 2012. National Tibetan Plateau Data Center. DOI
Liu S, 2016. HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Zhangye wetland Station, 2014) National Tibetan Plateau Data Center. DOI
Liu S, Xu Z. 2016. Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Daxing site-automatic weather station) (2008-2010) National Tibetan Plateau Data Center. DOI
Liu S, Xu Z. 2016. Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Daxing site - eddy covariance system) (2008-2010) National Tibetan Plateau Data Center. DOI
Che T, et al. Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China. Earth Syst. Sci. Data. 2019;11:1483–1499.
Jia Z, Liu S, Xu Z, Chen Y, Zhu M. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. J. Geophys. Res.-Atmos. 2012;117:D13113.
Liu S, et al. The heihe integrated observatory network: a basin‐scale land surface processes observatory in China. Vadose Zone J. 2018;17:1–21.
Liu S, et al. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agric. For. Meteorol. 2016;230-231:97–113.
Liu S, et al. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrol. Earth Syst. Sci. 2011;15:1291–1306.
Liu S, Xu Z, Zhu Z, Jia Z, Zhu M. Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China. J. Hydrol. 2013;487:24–38.
Xu Z, et al. Intercomparison of surface energy flux measurement systems used during the HiWATER‐MUSOEXE. J. Geophys. Res.-Atmos. 2013;118:13140–13157.
Dušek J, Faußer A, Stellner S, Kazda M. Stems of Phragmites australis are buffering methane and carbon dioxide emissions. Sci. Total Environ. 2023;882:163493. PubMed
Foltýnová L, Fischer M, McGloin RP. Recommendations for gap-filling eddy covariance latent heat flux measurements using marginal distribution sampling. Theor. Appl. Climatol. 2020;139:677–688.
Granier A, Bréda N, Longdoz B, Gross P, Ngao J. Ten years of fluxes and stand growth in a young beech forest at Hesse, North-eastern France. Ann. For. Sci. 2008;65:704.
Kivalov SN, et al. Addressing effects of environment on eddy-covariance flux estimates at a Temperate Sedge-Grass Marsh. Bound.-Layer Meteor. 2023;186:217–250.
Wutzler T, et al. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences. 2018;15:5015–5030.
Aurela M, Laurila T, Hatakka J, Tuovinen J-P, Rainne J. 2016. FLUXNET2015 RU-Tks Tiksi. FLUXNET. DOI
Bernhofer C, 2016. FLUXNET2015 DE-Spw Spreewald. FLUXNET. DOI
Bernhofer C, 2016. FLUXNET2015 DE-Tha Tharandt. FLUXNET. DOI
Chen S. 2016. FLUXNET2015 CN-Du2 Duolun_grassland. FLUXNET. DOI
Dong G. 2016. FLUXNET2015 CN-Cng Changling. FLUXNET. DOI
Ibrom A, Pilegaard K. 2016. FLUXNET2015 DK-Sor Soroe. FLUXNET. DOI
Klatt J, Schmid H, Mauder M, Steinbrecher R. 2016. FLUXNET2015 DE-SfN Schechenfilz Nord. FLUXNET. DOI
Kosugi Y, Takanashi S. 2016. FLUXNET2015 MY-PSO Pasoh Forest Reserve (PSO) FLUXNET. DOI
Kotani A. 2016. FLUXNET2015 JP-MBF Moshiri Birch Forest Site. FLUXNET. DOI
Kotani A. 2016. FLUXNET2015 JP-SMF Seto Mixed Forest Site. FLUXNET. DOI
Li Y. 2016. FLUXNET2015 CN-Ha2 Haibei Shrubland. FLUXNET. DOI
Maximov T. 2016. FLUXNET2015 RU-SkP Yakutsk Spasskaya Pad larch. FLUXNET. DOI
Olesen J. 2016. FLUXNET2015 DK-Fou Foulum. FLUXNET. DOI
Pastorello G, et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data. 2020;7:225. doi: 10.1038/s41597-020-0534-3. PubMed DOI PMC
Pilegaard K, Ibrom A. 2016. FLUXNET2015 DK-Eng Enghave. FLUXNET. DOI
Sachs T, Wille C, Larmanou E, Franz D. 2016. FLUXNET2015 DE-Zrk Zarnekow. FLUXNET. DOI
Schneider K, Schmidt M. 2016. FLUXNET2015 DE-Seh Selhausen. FLUXNET. DOI
Shao C. 2016. FLUXNET2015 CN-Du3 Duolun Degraded Meadow. FLUXNET. DOI
Shao C. 2016. FLUXNET2015 CN-Sw2 Siziwang Grazed (SZWG) FLUXNET. DOI
Shi P, Zhang X, He Y. 2016. FLUXNET2015 CN-Dan Dangxiong. FLUXNET. DOI
Tang Y, Kato T, Du M. 2016. FLUXNET2015 CN-HaM Haibei Alpine Tibet site. FLUXNET. DOI
Wang H, Fu X. 2016. FLUXNET2015 CN-Qia Qianyanzhou. FLUXNET. DOI
Zhang J, Han S. 2016. FLUXNET2015 CN-Cha Changbaishan. FLUXNET. DOI
Zhou G, Yan J. 2016. FLUXNET2015 CN-Din Dinghushan. FLUXNET. DOI
Anapalli SS, et al. Quantifying soybean evapotranspiration using an eddy covariance approach. Agric. Water Manage. 2018;209:228–239.
Xie M, et al. Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China. Hydrol. Process. 2021;35:e14444.
Zhang C, et al. A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach. J. Hydrol. 2021;603:127047.
Liang S, et al. The global land surface satellite (GLASS) product suite. Bull. Amer. Meteorol. Soc. 2020;102:1–37.
Zhang X, Liang S, Zhou G, Wu H, Zhao X. Generating Global Land Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data. Remote Sens. Environ. 2014;152:318–332.
Tang W. 2019. Dataset of high-resolution (3 hour, 10 km) global surface solar radiation (1983–2018) National Tibetan Plateau Data Center. https://cstr.cn/18406.11.Meteoro.tpdc.270112
Tang W, Yang K, Qin J, Li X, Niu X. A 16-year dataset (2000-2015) of high-resolution (3 h, 10 km) global surface solar radiation. Earth Syst. Sci. Data. 2019;11:1905–1915.
Myneni R, Knyazikhin Y, Park T. 2015. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. DOI
Vermote E, Wolfe R. 2015. MOD09GA MODIS/Terra Surface Reflectance Daily L2G Global 1kmand 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. DOI
Fang B, Lei H, Zhang Y, Quan Q, Yang D. Spatio-temporal patterns of evapotranspiration based on upscaling eddy covariance measurements in the dryland of the North China Plain. Agric. For. Meteorol. 2020;281:107844.
Yamazaki D, et al. A high-accuracy map of global terrain elevations. Geophys. Res. Lett. 2017;44:5844–5853.
FAO/IIASA/ISRIC/ISSCAS/JRC. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austriahttps://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML (2012).
Friedl M, Sulla-Menashe D. 2019. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. DOI
Sorensen, L. A spatial analysis approach to the global delineation of dryland areas of relevance to the CBD Programme of Work on Dry and Subhumid Lands. UNEP-WCMChttps://www2.unep-wcmc.org/resources-and-data/a-spatial-analysis-approach-to-the-global-delineation-of-dryland-areas-of-relevance-to-the-cbd-programme-of-work-on-dry-and-subhumid-lands (2007).
Xie M. 2023. Division of flux stations. figshare. DOI
Lopatin J, Dolos K, Hernández H, Galleguillos M, Fassnacht F. Comparing generalized linear models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sens. Environ. 2016;173:200–210.
Biau G. Analysis of a random forests model. J. Mach. Learn. Res. 2012;13:1063–1095.
Bergstra J, Komer B, Eliasmith C, Yamins D, Cox DD. Hyperopt: a python library for model selection and hyperparameter optimization. Comput. Sci. Discov. 2015;8:014008.
Ploton P, et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 2020;11:1–11. PubMed PMC
Patel SP, Upadhyay SH. Euclidean distance based feature ranking and subset selection for bearing fault diagnosis. Expert Syst. Appl. 2020;154:113400.
Lever J, Krzywinski M, Altman N. Points of significance: Principal component analysis. Nat. Methods. 2017;14:641–643.
Xie M. 2022. Carbon-water flux datasets of Eurasian meteorological stations. figshare. DOI
Xie M. 2023. Meteorological station information. figshare. DOI
Xie M. 2023. RSMs information. figshare. DOI
Jiang F, et al. A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO 2 retrievals (GCAS2021) Earth Syst. Sci. Data. 2022;14:3013–3037.
Running S, Mu Q, Zhao M. 2017. MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. DOI
Xie M. 2022. Code for mining carbon-water flux information at Eurasian meteorological stations. figshare. DOI