Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

摘要

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?msup?2/sup, 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: rsup2/sup = 0.32 and rsup2/sup = 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 (rsup2/sup = 0.24 and rsup2/sup = 0.44, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (rsup2/sup = 0.19). At the species level, canopy N concentration was strongly related to MTCI for European beech plots (rsup2/sup = 0.69). These results present a new perspective on the application of MTCI time series for canopy N detection.