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

Abstract

Uncertainties in gross primary production (GPP) estimation by light-use efficiency (LUE) models are largely driven by fluctuations in photosynthetically active radiation inputs (PAR_in). 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, PC_max (maximum photosynthetic capacity). However, the PCM tends to underestimate GPP in cold temperate regions, primarily because it parameterizes PC_max using multi-year average nighttime land surface temperatures (LST_night), which incorporate dormant-season extremes unrelated to photosynthesis. We developed an improved variant (PCM_NF), which employs a quadratic function linking PC_max to growing-season LST_night so as to better represent the nonlinear temperature-photosynthesis relationship. The PCM_NF 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_NF showed clear gains at multiple time scales over MODIS GPP products. Regional mean estimation error dropped from 21.8% (PCM) to 7.6% (PCM_NF). 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.

Keywords

Light utilization efficiency model / Gross primary production / Eddy covariance flux / Remote sensing / Northeast China forest ecosystems

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