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Journal Article

Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency


Kattge,  Jens
Interdepartmental Max Planck Fellow Group Functional Biogeography, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Madani, N., Kimball, J. S., Affleck, D. L., Kattge, J., Graham, J., van Bodegom, P. M., et al. (2014). Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency. Journal of Geophysical Research: Biogeosciences, 119(9), 1755-1769. doi:10.1002/2014JG002709.

Cite as: http://hdl.handle.net/11858/00-001M-0000-001A-2FCC-C
A common assumption of remote sensing-based light use efficiency (LUE) models for estimating vegetation gross primary productivity (GPP) is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed constant biomemaximum light use efficiency parameter (LUEmax) defines the maximum photosynthetic carbon conversion rate under these conditions and is a large source of model uncertainty. Here we used tower eddy covariance measurement-based carbon (CO2) fluxes for spatial estimation of optimal LUE (LUEopt) across North America. LUEopt was estimated at 62 Flux Network sites using tower daily carbon fluxes and meteorology, and satellite observed fractional photosynthetically active radiation fromthe Moderate Resolution Imaging Spectroradiometer. A geostatisticalmodel was fitted to 45 flux tower-derived LUEopt data points using independent geospatial environmental variables, including global plant traits, soil moisture, terrain aspect, land cover type, and percent tree cover, and validated at 17 independent tower sites. Estimated LUEopt shows large spatial variability within and among different land cover classes indicated from the sparse tower network. Leaf nitrogen content and soil moisture regime are major factors explaining LUEopt patterns. GPP derived from estimated LUEopt shows significant correlation improvement against tower GPP records (R2 = 76.9%; mean root-mean-square error (RMSE) = 257 g Cm2 yr1), relative to alternative GPP estimates derived using biome-specific LUEmax constants (R2 = 34.0%; RMSE= 439 g Cm2 yr1). GPP determined from the LUEopt map also explains a 49.4% greater proportion of tower GPP variability at the independent validation sites and shows promise for improving understanding of LUE patterns and environmental controls and enhancing regional GPP monitoring from satellite remote sensing.