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Assimilating phenology datasets automatically across ICOS ecosystem stations

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Hufkens Koen, Filippa Gianluca, Cremonese Edoardo, Migliavacca Mirco, D’Odorico Petra, Peichl Matthias, Gielen Bert, Hörtnagl Lukas, Soudani Kamel, Papale Dario, Rebmann Corinna, Brown Tim, Wingate Lisa,
Project ICOS-CH Phase 2
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Original article (peer-reviewed)

Journal International Agrophysics
Volume (Issue) 32(4)
Page(s) 677 - 687
Title of proceedings International Agrophysics
DOI 10.1515/intag-2017-0050

Open Access

URL http://doi.org/10.1515/intag-2017-0050
Type of Open Access Publisher (Gold Open Access)

Abstract

AbstractThe presence or absence of leaves within plant canopies exert a strong influence on the carbon, water and energy balance of ecosystems. Identifying key changes in the timing of leaf elongation and senescence during the year can help to understand the sensitivity of different plant functional types to changes in temperature. When recorded over many years these data can provide information on the response of ecosystems to long-term changes in climate. The installation of digital cameras that take images at regular intervals of plant canopies across the Integrated Carbon Observation System ecosystem stations will provide a reliable and important record of variations in canopy state, colour and the timing of key phenological events. Here, we detail the procedure for the implementation of cameras on Integrated Carbon Observation System flux towers and how these images will help us understand the impact of leaf phenology and ecosystem function, distinguish changes in canopy structure from leaf physiology and at larger scales will assist in the validation of (future) remote sensing products. These data will help us improve the representation of phenological responses to climatic variability across Integrated Carbon Observation System stations and the terrestrial biosphere through the improvement of model algorithms and the provision of validation datasets.
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