Articles | Volume 14, issue 4
Earth Syst. Sci. Data, 14, 1735–1794, 2022
Earth Syst. Sci. Data, 14, 1735–1794, 2022
Data description paper
13 Apr 2022
Data description paper | 13 Apr 2022

High-resolution land use and land cover dataset for regional climate modelling: a plant functional type map for Europe 2015

Vanessa Reinhart et al.

Related authors

High-resolution land-use land-cover change data for regional climate modelling applications over Europe – Part 2: Historical and future changes
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss.,,, 2021
Manuscript not accepted for further review
Short summary

Related subject area

Land Cover and Land Use
SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches
Femke van Geffen, Birgit Heim, Frederic Brieger, Rongwei Geng, Iuliia A. Shevtsova, Luise Schulte, Simone M. Stuenzi, Nadine Bernhardt, Elena I. Troeva, Luidmila A. Pestryakova, Evgenii S. Zakharov, Bringfried Pflug, Ulrike Herzschuh, and Stefan Kruse
Earth Syst. Sci. Data, 14, 4967–4994,,, 2022
Short summary
History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset from 1860 to 2019
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568,,, 2022
Short summary
LUCAS cover photos 2006–2018 over the EU: 874 646 spatially distributed geo-tagged close-up photos with land cover and plant species label
Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez, Peter Haub, Oliver Buck, Carsten Haub, Beatrice Eiselt, and Marijn van der Velde
Earth Syst. Sci. Data, 14, 4463–4472,,, 2022
Short summary
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
Han Su, Bárbara Willaarts, Diana Luna-Gonzalez, Maarten S. Krol, and Rick J. Hogeboom
Earth Syst. Sci. Data, 14, 4397–4418,,, 2022
Short summary
Vectorized dataset of roadside noise barriers in China using street view imagery
Zhen Qian, Min Chen, Yue Yang, Teng Zhong, Fan Zhang, Rui Zhu, Kai Zhang, Zhixin Zhang, Zhuo Sun, Peilong Ma, Guonian Lü, Yu Ye, and Jinyue Yan
Earth Syst. Sci. Data, 14, 4057–4076,,, 2022
Short summary

Cited articles

Alkama, R. and Cescatti, A.: Biophysical climate impacts of recent changes in global forest cover, Science, 351, 600–604, 2016. a
Anderegg, L. D. L., Griffith, D. M., Cavender-Bares, J., Riley, W. J., Berry, J. A., Dawson, T. E., and Still, C. J.: Representing plant diversity in land models: An evolutionary approach to make “Functional Types” more functional, Glob. Change Biol., 28​​​​​​​, 2541–2554,, 2021. a
Bégué, A., Arvor, D., Bellon, B., Betbeder, J., De Abelleyra, D., Ferraz, R. P. D., Lebourgeois, V., Lelong, C., Simões, M., and Verón, S. R.​​​​​​​: Remote sensing and cropping practices: A review, Remote Sensing, 10, 99​​​​​​​,, 2018. a
Belda, M., Halenka, T., Huszar, P., Karlicky, J., and Nováková, T.: Do we need urban parameterization in high resolution regional climate simulations?, in: AGU Fall Meeting Abstracts, 2018AGUFM.A21L2878B, 2018. a
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and Zemp, M.: The concept of essential climate variables in support of climate research, applications, and policy, B. Am. Meteorol. Soc., 95, 1431–1443, 2014. a
Short summary
The LANDMATE plant functional type (PFT) land cover dataset for Europe 2015 (Version 1.0) is a gridded, high-resolution dataset for use in regional climate models. LANDMATE PFT is prepared using the expertise of regional climate modellers all over Europe and is easily adjustable to fit into different climate model families. We provide comprehensive spatial quality information for LANDMATE PFT, which can be used to reduce uncertainty in regional climate model simulations.