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| Titel |
Remote sensing of leaf N to improve carbon assimilation prediction |
| VerfasserIn |
Yasmina Loozen, Karin Rebel, Derek Karssenberg, Steven de Jong, Martin Wassen |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250128017
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| Publikation (Nr.) |
 EGU/EGU2016-7957.pdf |
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| Zusammenfassung |
| Predicting and understanding carbon assimilation by terrestrial vegetation remains
fundamental in the context of climate change. Carbon and nitrogen cycles are linked
as nitrogen is an essential nutrient for plant growth. In this respect the N cycle is
integrated into vegetation models predicting vegetation carbon uptake. However plant
traits within the N cycle, such as leaf nitrogen, are lacking at large scales, which
complicates the calibration and optimization of the N cycling modelling modules.
Remote sensing techniques could offer the possibility to detect leaf N concentration at
continental scales. In fact, it has already been used to sense leaf N at local, e.g. in
agricultural oriented applications, as well as at regional scales. The objective of this
study is to enhance the availability of leaf N estimates in forested ecosystems at
European scale using remote sensing products. European forest leaf N data were
obtained from the TRY database. The MERIS Terrestrial chlorophyll Index (MTCI)
Level 3 product as well as two reflectance bands in the NIR region (band centers at
865 and 885nm) both from MERIS aboard ENVISAT (ESA) were used to study
statistical relationship with leaf N data. In a first step, we analyzed 1892 Catalonian
(NE Spain) forest plots using a linear regression method. The regressions results
between leaf N and either MTCI or NIR bands were significant (p< 0.001). The
R-square for the regression between leaf N and MTCI was equal to 0.13. The method
performed better for broadleaves deciduous plots (R-square = 0.11) than for needleleaves
or broadleaves evergreen plots. The relationship between leaf N and MTCI was
also higher for the plots sampled during summer (R-square = 0.28 in July) than
for the plots sampled during the rest of the year. In a second step the method will
be applied on and will include more diverse forest types at the European level. |
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