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Abstract:
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.
