Canopy nitrogen (N) content plays important role in plant growth and other ecosystem processes. More specifically, Leaf nitrogen concentration links to several leaf traits associated with photosynthesis, including photosynthetic capacity, light use efficiency, specific leaf area and thus the primary productivity. Current Global Vegetation Models (GVMs) based prediction of carbon assimilation could be improved by accurate canopy N map.
Research group in Faculty of Geosciences, Utrecht University (UU) has published an approach to mapping canopy N content with a remote sensing (RS) way recently. A random forest (RF) approach was used with RS observation and environmental variables input to retrieve canopy N in Europe (Loozen et al., 2020). Result shows good consistency against field observation and reasonable spatial distribution (see Fig. 1).
Fig. 1. Predicted canopy nitrogen maps (%N) in Europe, refers to Loozen et al., (2020).
LPICEA has long been working with optimality theory. The universal gross primary productivity model (the P model) could predict carbon uptake based on plant adjustment to local environment (Wang et al., 2017). Our recent research about LMA (Leaf Mass per Area, Wang et al., in prep.), with our reasonable prediction of VCmax, makes it possible to further predict canopy N.
Collaboration with University Utrecht is thus being proposed. Two analysis branches, retrieving canopy N based on RS way and on optimality theory, would be finished by UU and LPICEA separately. Field observation from ICP-forest program would be employed in validation (http://icp-forests.net/).
Reference
Loozen Y, Rebel K T, de Jong S M, et al. Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method[J]. Remote Sensing of Environment, 2020, 247: 111933.
Wang H, Prentice I C, Keenan T F, et al. Towards a universal model for carbon dioxide uptake by plants[J]. Nature Plants, 2017, 3(9): 734-741.