BibTex format
@article{Dechant:2024:10.1016/j.rse.2024.114276,
author = {Dechant, B and Kattge, J and Pavlick, R and Schneider, FD and Sabatini, FM and Moreno-Martínez, Á and Butler, EE and van, Bodegom PM and Vallicrosa, H and Kattenborn, T and Boonman, CCF and Madani, N and Wright, IJ and Dong, N and Feilhauer, H and Peñuelas, J and Sardans, J and Aguirre-Gutiérrez, J and Reich, PB and Leitão, PJ and Cavender-Bares, J and Myers-Smith, IH and Durán, SM and Croft, H and Prentice, IC and Huth, A and Rebel, K and Zaehle, S and ímová, I and Díaz, S and Reichstein, M and Schiller, C and Bruelheide, H and Mahecha, M and Wirth, C and Malhi, Y and Townsend, PA},
doi = {10.1016/j.rse.2024.114276},
journal = {Remote Sensing of Environment},
title = {Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches},
url = {http://dx.doi.org/10.1016/j.rse.2024.114276},
volume = {311},
year = {2024}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0.2–0.6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was relevant
AU - Dechant,B
AU - Kattge,J
AU - Pavlick,R
AU - Schneider,FD
AU - Sabatini,FM
AU - Moreno-Martínez,Á
AU - Butler,EE
AU - van,Bodegom PM
AU - Vallicrosa,H
AU - Kattenborn,T
AU - Boonman,CCF
AU - Madani,N
AU - Wright,IJ
AU - Dong,N
AU - Feilhauer,H
AU - Peñuelas,J
AU - Sardans,J
AU - Aguirre-Gutiérrez,J
AU - Reich,PB
AU - Leitão,PJ
AU - Cavender-Bares,J
AU - Myers-Smith,IH
AU - Durán,SM
AU - Croft,H
AU - Prentice,IC
AU - Huth,A
AU - Rebel,K
AU - Zaehle,S
AU - ímová,I
AU - Díaz,S
AU - Reichstein,M
AU - Schiller,C
AU - Bruelheide,H
AU - Mahecha,M
AU - Wirth,C
AU - Malhi,Y
AU - Townsend,PA
DO - 10.1016/j.rse.2024.114276
PY - 2024///
SN - 0034-4257
TI - Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches
T2 - Remote Sensing of Environment
UR - http://dx.doi.org/10.1016/j.rse.2024.114276
UR - http://hdl.handle.net/10044/1/114801
VL - 311
ER -