%0 Journal Article %1 Dechant_2024 %A Dechant, Benjamin %A Kattge, Jens %A Pavlick, Ryan %A Schneider, Fabian D. %A Sabatini, Francesco M. %A Moreno-Martínez, Álvaro %A Butler, Ethan E. %A van Bodegom, Peter M. %A Vallicrosa, Helena %A Kattenborn, Teja %A Boonman, Coline C.F. %A Madani, Nima %A Wright, Ian J. %A Dong, Ning %A Feilhauer, Hannes %A Peñuelas, Josep %A Sardans, Jordi %A Aguirre-Gutiérrez, Jesús %A Reich, Peter B. %A Leitão, Pedro J. %A Cavender-Bares, Jeannine %A Myers-Smith, Isla H. %A Durán, Sandra M. %A Croft, Holly %A Prentice, I. Colin %A Huth, Andreas %A Rebel, Karin %A Zaehle, Sönke %A Šímová, Irena %A Díaz, Sandra %A Reichstein, Markus %A Schiller, Christopher %A Bruelheide, Helge %A Mahecha, Miguel %A Wirth, Christian %A Malhi, Yadvinder %A Townsend, Philip A. %D 2024 %I Elsevier BV %J Remote Sensing of Environment %K imported zno %P 114276 %R 10.1016/j.rse.2024.114276 %T Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches %U http://dx.doi.org/10.1016/j.rse.2024.114276 %V 311 %X 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, but considerably smaller than that of the PFT and land cover information. The maps using PFT and land cover information better reproduce the between-PFT trait differences of sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation. Our findings highlight the importance of explicitly accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts. Remote sensing information has great potential to reduce uncertainties related to scaling from in-situ observations to grid cells and the regression-based mapping steps involved in the upscaling.

@article{Dechant_2024, abstract = {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, but considerably smaller than that of the PFT and land cover information. The maps using PFT and land cover information better reproduce the between-PFT trait differences of sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation. Our findings highlight the importance of explicitly accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts. Remote sensing information has great potential to reduce uncertainties related to scaling from in-situ observations to grid cells and the regression-based mapping steps involved in the upscaling.}, added-at = {2024-12-10T12:34:47.000+0100}, author = {Dechant, Benjamin and Kattge, Jens and Pavlick, Ryan and Schneider, Fabian D. and Sabatini, Francesco M. and Moreno-Martínez, Álvaro and Butler, Ethan E. and van Bodegom, Peter M. and Vallicrosa, Helena and Kattenborn, Teja and Boonman, Coline C.F. and Madani, Nima and Wright, Ian J. and Dong, Ning and Feilhauer, Hannes and Peñuelas, Josep and Sardans, Jordi and Aguirre-Gutiérrez, Jesús and Reich, Peter B. and Leitão, Pedro J. and Cavender-Bares, Jeannine and Myers-Smith, Isla H. and Durán, Sandra M. and Croft, Holly and Prentice, I. Colin and Huth, Andreas and Rebel, Karin and Zaehle, Sönke and Šímová, Irena and Díaz, Sandra and Reichstein, Markus and Schiller, Christopher and Bruelheide, Helge and Mahecha, Miguel and Wirth, Christian and Malhi, Yadvinder and Townsend, Philip A.}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/269a33fcd4d023a07e0c3b31779e0ee6c/scadsfct}, doi = {10.1016/j.rse.2024.114276}, interhash = {8aac0216b7e0785fe79ef45adb9db64d}, intrahash = {69a33fcd4d023a07e0c3b31779e0ee6c}, issn = {0034-4257}, journal = {Remote Sensing of Environment}, keywords = {imported zno}, month = sep, pages = 114276, publisher = {Elsevier BV}, timestamp = {2025-03-07T12:56:48.000+0100}, 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 }