Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index
2018
Abstract. Canopy nitrogen (N) concentration and content are linked to several
vegetation processes. Therefore, canopy N concentration is a state variable
in global vegetation models with coupled carbon (C) and N cycles. While there
are ample C data available to constrain the models, widespread N data are
lacking. Remotely sensed vegetation indices have been used to detect canopy N
concentration and canopy N content at the local scale in grasslands and
forests. Vegetation indices could be a valuable tool to detect canopy N
concentration and canopy N content at larger scale. In this paper, we
conducted a regional case-study analysis to investigate the relationship
between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial
Chlorophyll Index (MTCI) time series from European Space Agency (ESA) Envisat satellite at 1 km spatial
resolution and both canopy N concentration (%N) and canopy N content
(N g m −2 , of ground area) from a Mediterranean forest inventory in the
region of Catalonia, in the northeast of Spain. The relationships between the datasets were
studied after resampling both datasets to lower spatial resolutions (20, 15,
10 and 5 km) and at the original spatial resolution of 1 km. The results at
higher spatial resolution (1 km) yielded significant log–linear
relationships between MTCI and both canopy N concentration and content:
r 2 = 0.32 and r 2 = 0.17, respectively. We also investigated
these relationships per plant functional type. While the relationship between
MTCI and canopy N concentration was strongest for deciduous broadleaf and
mixed plots ( r 2 = 0.24 and r 2 = 0.44, respectively), the
relationship between MTCI and canopy N content was strongest for evergreen
needleleaf trees ( r 2 = 0.19). At the species level, canopy N
concentration was strongly related to MTCI for European beech plots
( r 2 = 0.69). These results present a new perspective on the
application of MTCI time series for canopy N detection.
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