Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Abstract

) from a Mediterranean forests inventory in the region of Catalonia, NE of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20 km, 15 km, 10 km and 5 km) and at the initial higher spatial resolution of 1 km. The results at the higher spatial resolution yielded significant 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 20 concentration was strongest for deciduous broadleaf and mixed plots (r 2 = 0.25 and r 2 = 0.47, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r 2 = 0.20). At the species level, canopy N concentration was strongly related to MTCI for European Beech plots (r 2 = 0.71). These results present a new perspective on the application of MTCI time series for canopy N detection, ultimately leading towards the generation of canopy N maps that can be used to constrain global vegetation models. capacity (Evans, 1989;Reich et al., 1995;Reich et al., 1997;Reich et al., 1999;Wright et al., 2004), specific leaf area, leaf life span (Reich et al., 1999;Wright et al., 2004) and light use efficiency (Kergoat et al., 2008). Leaf N concentration expressed on a leaf area basis, also called leaf N content (N g m -2 ) has also been linked with chlorophyll content, Rubisco content (Evans, 35 1989) and photosynthetic capacity (Evans, 1989;Reich et al., 1995). At stand scale, canopy nitrogen concentration, which represents the leaf N concentration averaged over the stand canopy, has also been found to correlate with above ground Net Primary Productivity (NPP) (Reich, 2012), while canopy N content has been linked with the canopy light use efficiency (Green et al., 2003).Given their links to many vegetation processes, leaf and canopy N variables could be used to constrain N cycle modules in 40 global vegetation models. At the global scale, ample data is available to constrain models for the C cycle; however, data to constrain the N cycle are limited. Currently, canopy N data is not widely available and canopy N sampling campaigns are timeconsuming and thus expensive tasks. Moreover, upscaling from local sampling campaign measurements represents an additional limitation. In this perspective, local, regional or even global remotely sensed canopy N estimates will be a valuable addition, enabling us to collect information in a less time intensive and expensive manner than traditional on-field sampling 45 campaigns. Such near global canopy N estimates will be beneficial as input in global vegetation models or to calibrate and validate these models.Currently, different remote sensing techniques have been applied to detect canopy N in terrestrial vegetation. Imaging spectrometry from either airborne or spaceborne sensors coupled with different analysis methods, including partial least squares regression (PLS), continuu...

Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

“…Results for this study concurs with other studies that evaluated the applicability of the multispectral red edge bands for mapping vegetation quality or health in the grasslands or savanna [16][17][18]45] subtropical forests [46] and Mediterranean forests (Loozen et al, 2017) [47]. In addition, the notable performance of the MTCI on the estimation of N content or concentrations has been further reported by Cho et al [46]; Ullah et al [45] and Loozen et al [47]. The leaf N model based on SR-RE3 was evaluated using independent leaf N and RapidEye data collected from the same area during peak productivity (March 2010) [16].…”

“…The latter band (705 nm) is placed at the onset of the high reflectivity portion of the vegetation response, and is crucial for plant health estimation (e.g., leaf N or chlorophyll) while the second red edge band (740 nm) is influenced by the concerted effects of plant health (e.g., leaf N or chlorophyll) [14] and the vegetation structure (e.g., leaf area index and biomass) [14,18]. Results for this study concurs with other studies that evaluated the applicability of the multispectral red edge bands for mapping vegetation quality or health in the grasslands or savanna [16][17][18]45] subtropical forests [46] and Mediterranean forests (Loozen et al, 2017) [47]. In addition, the notable performance of the MTCI on the estimation of N content or concentrations has been further reported by Cho et al [46]; Ullah et al [45] and Loozen et al [47].…”

Nitrogen Availability Dampens the Positive Impacts of CO2 Fertilization on Terrestrial Ecosystem Carbon and Water Cycles

“…The use of MTCI also has limitations because we have assumed that the trend of MTCI is linear and the V cmax, 25 , chlorophyll content and MTCI share the same trend. Although the correlations between V cmax, 25 and chlorophyll content (Croft et al, 2017;Houborg et al, 2013) and between chlorophyll content and MTCI (Nguy-Robertson et al, 2015;Peng et al, 2017) are strong, more studies that directly use of MTCI for GPP modeling, such as Alton (2017), Boyd et al (2012), Dong et al (2015), Harris &Dash (2011), andLoozen et al (2017), will further strengthen the conclusion of this study.…”

Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery

“…From Fig. 6 and Table 6, we observed that the estimation accuracy of leaf nitrogen was lower for each forest functional type than for the pooled plots, which have been shown in a number of previous studies (Dahlin et al, 2013;Lepine et al, 2016;Gokkaya et al, 2015;Singh et al, 2015;Loozen et al, 2017). The limited number of plots for each forest type (8-10) may make the indicators of model performance unsuitable for comparison.…”