Articles | Volume 19, issue 11
https://doi.org/10.5194/bg-19-2805-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-2805-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
08 Jun 2022
Technical note |
| 08 Jun 2022
| 08 Jun 2022
Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set
Sophia Walther
CORRESPONDING AUTHOR
Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, Jena, Germany
Simon Besnard
Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, Jena, Germany
South Pole, Digital Innovation, Fred. Roeskestraat 115, Amsterdam, the Netherlands
Jacob Allen Nelson
Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, Jena, Germany
Tarek Sebastian El-Madany
Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, Jena, Germany
Mirco Migliavacca
Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, Jena, Germany
European Commission, Joint Research Centre, Via Fermi 2749, Ispra (VA), Italy
Ulrich Weber
Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, Jena, Germany
Nuno Carvalhais
Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, Jena, Germany
Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
Sofia Lorena Ermida
Departamento de Ciências e Engenharia do Ambiente (DCEA), Faculdade de Ciências e Tecnologia (FCT), Universidade Nova de Lisboa, Lisbon, Portugal
Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, Campo Grande Edifício C1, Piso 1, 1749-016 Lisbon, Portugal
Christian Brümmer
Thünen Institute of Climate-Smart Agriculture, Bundesallee 65, Braunschweig, Germany
Frederik Schrader
Thünen Institute of Climate-Smart Agriculture, Bundesallee 65, Braunschweig, Germany
Anatoly Stanislavovich Prokushkin
V.N. Sukachev Institute of Forest of the Siberian Branch of Russian Academy of Sciences – separated department of the KSC SB RAS, Akademgorodok 50/28, Krasnoyarsk, Russia
Alexey Vasilevich Panov
V.N. Sukachev Institute of Forest of the Siberian Branch of Russian Academy of Sciences – separated department of the KSC SB RAS, Akademgorodok 50/28, Krasnoyarsk, Russia
Martin Jung
Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, Jena, Germany
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Cited
13 citations as recorded by crossref.
- X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X J. Nelson et al. 10.5194/bg-21-5079-2024
- Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest X. Yu et al. 10.5194/bg-19-4315-2022
- Global dryland aridity changes indicated by atmospheric, hydrological, and vegetation observations at meteorological stations H. Shi et al. 10.5194/hess-27-4551-2023
- Super resolution of historic Landsat imagery using a dual generative adversarial network (GAN) model with CubeSat constellation imagery for spatially enhanced long-term vegetation monitoring J. Kong et al. 10.1016/j.isprsjprs.2023.04.013
- Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation H. Shi et al. 10.5194/bg-19-3739-2022
- Using automated machine learning for the upscaling of gross primary productivity M. Gaber et al. 10.5194/bg-21-2447-2024
- The Forest Resistance to Droughts Differentiated by Tree Height in Central Europe T. Li et al. 10.1029/2021JG006668
- Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models S. Bao et al. 10.1029/2022MS003464
- Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems L. Liu et al. 10.1038/s41467-023-43860-5
- Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. 10.5194/npg-31-535-2024
- Assessing the effectiveness of a central flux tower in representing the spatial variations in gross primary productivity in a semi-arid pine forest H. Wang et al. 10.1016/j.agrformet.2023.109415
- Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network U. Gomarasca et al. 10.1088/2752-664X/ad87f9
- Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set S. Walther et al. 10.5194/bg-19-2805-2022
12 citations as recorded by crossref.
- X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X J. Nelson et al. 10.5194/bg-21-5079-2024
- Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest X. Yu et al. 10.5194/bg-19-4315-2022
- Global dryland aridity changes indicated by atmospheric, hydrological, and vegetation observations at meteorological stations H. Shi et al. 10.5194/hess-27-4551-2023
- Super resolution of historic Landsat imagery using a dual generative adversarial network (GAN) model with CubeSat constellation imagery for spatially enhanced long-term vegetation monitoring J. Kong et al. 10.1016/j.isprsjprs.2023.04.013
- Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation H. Shi et al. 10.5194/bg-19-3739-2022
- Using automated machine learning for the upscaling of gross primary productivity M. Gaber et al. 10.5194/bg-21-2447-2024
- The Forest Resistance to Droughts Differentiated by Tree Height in Central Europe T. Li et al. 10.1029/2021JG006668
- Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models S. Bao et al. 10.1029/2022MS003464
- Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems L. Liu et al. 10.1038/s41467-023-43860-5
- Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. 10.5194/npg-31-535-2024
- Assessing the effectiveness of a central flux tower in representing the spatial variations in gross primary productivity in a semi-arid pine forest H. Wang et al. 10.1016/j.agrformet.2023.109415
- Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network U. Gomarasca et al. 10.1088/2752-664X/ad87f9
1 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
Satellite observations help interpret station measurements of local carbon, water, and energy exchange between the land surface and the atmosphere and are indispensable for simulations of the same in land surface models and their evaluation. We propose generalisable and efficient approaches to systematically ensure high quality and to estimate values in data gaps. We apply them to satellite data of surface reflectance and temperature with different resolutions at the stations.
Satellite observations help interpret station measurements of local carbon, water, and energy...
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