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

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 biome maximum light use efficiency parameter (LUE_max) 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 (LUE_opt) across North America. LUE_opt was estimated at 62 Flux Network sites using tower daily carbon fluxes and meteorology, and satellite observed fractional photosynthetically active radiation from the Moderate Resolution Imaging Spectroradiometer. Ageostatistical model was fitted to 45 flux tower-derived LUE_opt 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 LUE_opt 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 LUE_opt patterns. GPP derived from estimated LUE_opt shows significant correlation improvement against tower GPP records (R² = 76.9%; mean root-mean-square error (RMSE) = 257gCm^-2yr^-1), relative to alternative GPP estimates derived using biome-specific LUE_max constants (R² = 34.0%; RMSE = 439gCm^-2yr^-1). GPP determined from the LUE_opt 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.