Technical note

The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, AmeriFlux, and Terrestrial Ecosystem Research Network (TERN)/OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the benchmarking of terrestrial biosphere models. However, insufficient quality and frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices, and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40g off-nadir. Finally, we offer the community living data sets of pre-processed Earth observation data, where version 1.0 features the MCD43A4/A2 and MxD11A1 MODIS products and Landsat Collection 1 Tier 1 and Tier 2 products in a radius of 2 km around 338 flux sites. The data sets we provide can widely facilitate the integration of activities in the eddy-covariance, remote sensing, and modelling fields.

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Funding Information: Acknowledgements. We thank the team at the ICOS Carbon Portal for their support in publishing the FluxnetEO data sets, with great thanks in particular to Ute Karstens and Zois Zogopoulos. SW acknowledges funding from an ESA Living Planet Fellowship in the project Vad3e mecum. Alexey Vasilevich Panov acknowledges funding from the Max Planck Society (Germany), Russian Foundation for Basic Re- search, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project no. 20-45-242908. Frederik Schrader and Christian Brümmer acknowledge funds from the German Federal Ministry of Food and Agriculture (BMEL) received through Thünen Institute of Climate-Smart Agriculture. Simon Besnard acknowledges funding from the European Union through the BIOMAS-CAT (project code: 4000115192/18/I/NB) (https://eo4society.esa. int/projects/biomascat/, last access: 3 May 2022) and VERIFY (project code: BO-55-101-006) (https://cordis.europa.eu/project/id/ 776810, last access: 3 May 2022) projects. Funding Information: Financial support. This research has been supported by the Euro- Publisher Copyright: © 2022 Sophia Walther et al.