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<p>Uncertainties in gross primary production (GPP) estimation by light-use efficiency (LUE) models are largely driven by fluctuations in photosynthetically active radiation inputs (<i>PAR</i><sub>in</sub>). To address this problem, the photosynthetic capacity model (PCM) reduces&#xa0;this sensitivity&#xa0;by replacing&#xa0;actual radiation with&#xa0;potential <i>PAR</i>, and estimates GPP from the MODIS-derived enhanced vegetation index (<i>EVI</i>), land surface water index (<i>LSWI</i>) and the ecological parameter, <i>PC</i><sub>max</sub> (maximum photosynthetic capacity). However, the PCM tends to underestimate GPP in cold temperate regions, primarily because it parameterizes <i>PC</i><sub>max</sub> using multi-year average nighttime land surface temperatures (<i>LST</i><sub>night</sub>),&#xa0;which incorporate&#xa0;dormant-season extremes unrelated to photosynthesis. We developed an improved variant (PCM<sub>NF</sub>), which employs a quadratic function linking <i>PC</i><sub>max</sub> to growing-season <i>LST</i><sub>night</sub> so as to better represent the nonlinear temperature-photosynthesis relationship. The PCM<sub>NF</sub> model was calibrated to forest ecosystems in Northeast China, with potential applicability to analogous temperate and cold-temperate forest biomes. When validated against eddy covariance observations from five ChinaFlux forest sites, PCM<sub>NF</sub> showed clear gains at multiple time scales over MODIS GPP products. Regional mean estimation error dropped from 21.8% (PCM) to 7.6% (PCM<sub>NF</sub>). The model also captured a clear north–south GPP gradient and a rising trend of 11.9&#xa0;g&#xa0;m<sup>−2</sup>&#xa0;a<sup>−1</sup> over 2001–2023, offering a more reliable basis for temperate forest carbon sink quantification and climate impact assessment.</p>

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Tower-based improvement of a photosynthetic capacity model for gross primary production estimation in temperate forest ecosystems of Northeast China

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Fengyuan Yu,
  • Zhi Chen,
  • Qiufeng Wang,
  • Jinxin Zhang,
  • Tian Gao,
  • Deliang Lu,
  • Zhongxu Yu,
  • Ryuichi Hirata,
  • Hyun Seok Kim,
  • Shirong Liu,
  • Guirui Yu

摘要

Uncertainties in gross primary production (GPP) estimation by light-use efficiency (LUE) models are largely driven by fluctuations in photosynthetically active radiation inputs (PARin). To address this problem, the photosynthetic capacity model (PCM) reduces this sensitivity by replacing actual radiation with potential PAR, and estimates GPP from the MODIS-derived enhanced vegetation index (EVI), land surface water index (LSWI) and the ecological parameter, PCmax (maximum photosynthetic capacity). However, the PCM tends to underestimate GPP in cold temperate regions, primarily because it parameterizes PCmax using multi-year average nighttime land surface temperatures (LSTnight), which incorporate dormant-season extremes unrelated to photosynthesis. We developed an improved variant (PCMNF), which employs a quadratic function linking PCmax to growing-season LSTnight so as to better represent the nonlinear temperature-photosynthesis relationship. The PCMNF model was calibrated to forest ecosystems in Northeast China, with potential applicability to analogous temperate and cold-temperate forest biomes. When validated against eddy covariance observations from five ChinaFlux forest sites, PCMNF showed clear gains at multiple time scales over MODIS GPP products. Regional mean estimation error dropped from 21.8% (PCM) to 7.6% (PCMNF). The model also captured a clear north–south GPP gradient and a rising trend of 11.9 g m−2 a−1 over 2001–2023, offering a more reliable basis for temperate forest carbon sink quantification and climate impact assessment.