Global Upscaling of Gross Primary Productivity Using a Simple and Robust Modeling Scheme

Gross primary productivity (GPP) through photosynthesis is a crucial ecosystem function that significantly influences food security, carbon cycle, and climate change. Current remote sensing estimates of GPP rely on look-up tables containing biome-specific parameters to model light-use efficiency (ε) using coarse resolution interpolated meteorology data, resulting in significant uncertainties in global GPP estimates. To address this challenge, we propose a simple yet effective ecosystem light-use efficiency (eLUE) model to GPP from FLUXNET tower sites to a global scale. Defined as GPP/PAR, eLUE differs from the traditional LUE (GPP/APAR, or ε) in that eLUE essentially integrates canopy light absorption (fAPAR) and the physiological efficiency of photosynthesis (ε), thus eliminating the need for a separate estimate of ε. eLUE was calibrated as a function of MODIS Enhanced Vegetation Index (EVI), and then GPP can be modelled directly as eLUE × PAR. To quantify the carbon cycle error budget, we analytically derived GPP uncertainty based on the law of error propagation. Cross-validation against 120 global FLUXNET sites, encompassing 11 plant functional types (PFTs), demonstrated satisfactory performance of the eLUE model (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.74, RMSE = 2.05 g C m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> d<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>, NSE = 0.74), outperforming or performing comparably to more sophisticated models. Our estimate of global total terrestrial GPP, averaged between 2001 and 2024, is 135.12±11.02 Pg C yr<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>. Meanwhile, we found a significant increasing trend in global total GPP at a rate of 0.26±0.06 Pg C yr<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> (p < 0.001) from 2001 to 2024, primarily driven by the enhanced CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sequestration in terrestrial ecosystems across the Northern Hemisphere. We suggest that our eLUE model, with its robust performance and clear error representation, will help constrain the global carbon budget and improve the diagnostic analysis of carbon cycle dynamics and climate change feedback. The eLUE-GPP product, available at both global scale and FLUXNET sites, can be accessed for free at: https://doi.org/10.5061/dryad.v9s4mw74h.

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Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/tgrs.2025.3618410
Citations
2
Primary Topic
Global Energy and Sustainability Research
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article
Field-Weighted Citation Impact
0.73
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article

Global Upscaling of Gross Primary Productivity Using a Simple and Robust Modeling Scheme

Xuanlong Ma, Cao Chun-yan, Alfredo Huete, Kai Yan et al.
2 citations
IEEE Transactions on Geoscience and Remote Sensing
Global Energy and Sustainability Research
0.73
article

Global Upscaling of Gross Primary Productivity Using a Simple and Robust Modeling Scheme

Xuanlong Ma, Cao Chun-yan, Alfredo Huete, Kai Yan, Wei Yang, Feng Liu, Yuanyuan Wang
article en
2 citations

Abstract

Gross primary productivity (GPP) through photosynthesis is a crucial ecosystem function that significantly influences food security, carbon cycle, and climate change. Current remote sensing estimates of GPP rely on look-up tables containing biome-specific parameters to model light-use efficiency (ε) using coarse resolution interpolated meteorology data, resulting in significant uncertainties in global GPP estimates. To address this challenge, we propose a simple yet effective ecosystem light-use efficiency (eLUE) model to GPP from FLUXNET tower sites to a global scale. Defined as GPP/PAR, eLUE differs from the traditional LUE (GPP/APAR, or ε) in that eLUE essentially integrates canopy light absorption (fAPAR) and the physiological efficiency of photosynthesis (ε), thus eliminating the need for a separate estimate of ε. eLUE was calibrated as a function of MODIS Enhanced Vegetation Index (EVI), and then GPP can be modelled directly as eLUE × PAR. To quantify the carbon cycle error budget, we analytically derived GPP uncertainty based on the law of error propagation. Cross-validation against 120 global FLUXNET sites, encompassing 11 plant functional types (PFTs), demonstrated satisfactory performance of the eLUE model (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.74, RMSE = 2.05 g C m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> d<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>, NSE = 0.74), outperforming or performing comparably to more sophisticated models. Our estimate of global total terrestrial GPP, averaged between 2001 and 2024, is 135.12±11.02 Pg C yr<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>. Meanwhile, we found a significant increasing trend in global total GPP at a rate of 0.26±0.06 Pg C yr<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> (p < 0.001) from 2001 to 2024, primarily driven by the enhanced CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sequestration in terrestrial ecosystems across the Northern Hemisphere. We suggest that our eLUE model, with its robust performance and clear error representation, will help constrain the global carbon budget and improve the diagnostic analysis of carbon cycle dynamics and climate change feedback. The eLUE-GPP product, available at both global scale and FLUXNET sites, can be accessed for free at: https://doi.org/10.5061/dryad.v9s4mw74h.

IEEE Transactions on Geoscience and Remote SensingVol. 63
University of Technology Sydney (AU), China Meteorological Administration (CN), Chiba University (JP), Chinese Academy of Sciences (CN), Beijing Normal University (CN), Northwest Institute of Eco-Environment and Resources (CN), State Key Laboratory of Remote Sensing Science (CN), Lanzhou University (CN)
Openalex Percentile: Top 28%
Global Energy and Sustainability Research
0.73
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