Dong et al. (2026) Improving vegetation photosynthesis model (VPM) by incorporating CO2 and atmospheric aerosols: A case study using urban and rural flux data from Shenzhen, China

Identification

• Journal: Physics and Chemistry of the Earth Parts A/B/C
• Year: 2026
• Date: 2026-04-08
• Authors: Guannan Dong, Weimin Wang, Kai Liu, Ziyan Yan, Long Gao, Furong Zhang, Shaohui Chen
• DOI: 10.1016/j.pce.2026.104430

Research Groups

• Academy of Forestry Inventory and Planning, National Forestry and Grassland Administration, Beijing, China
• Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
• University of Chinese Academy of Sciences, Beijing, China
• Shenzhen Ecological and Environmental Monitoring Center of Guangdong Province, Shenzhen, China
• Guangdong Greater Bay Area, Change and Comprehensive Treatment of Regional Ecology and Environment, National Observation and Research Station, Shenzhen, China
• State Environmental Protection Scientific Observation and Research Station for Ecology and Environment of Rapid Urbanization Region, Shenzhen, China
• Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
• Systems Engineering Research Institute, China State Shipbuilding Corporation, Beijing, China

Short Summary

This study enhances the Vegetation Photosynthesis Model (VPM) by integrating atmospheric CO2 and aerosol optical depth (AOD) to improve gross primary production (GPP) estimation for urban evergreen broadleaf forests. The modified VPM (VPM_urban) significantly increased GPP simulation accuracy in urban areas, offering better tools for urban ecological management.

Objective

• To improve the Vegetation Photosynthesis Model (VPM) by incorporating atmospheric CO2 concentration and Aerosol Optical Depth (AOD) as stress factors.
• To assess the enhanced VPM's (VPM_urban) ability to accurately estimate Gross Primary Production (GPP) for urban and rural evergreen broadleaf forests, using Shenzhen, China, as a case study.

Study Configuration

• Spatial Scale: Urban and rural evergreen broadleaf forests in Shenzhen, China, specifically Tianxinshan (TXS) (urban) and Yangmeikeng (YMK) (rural) research forests.
• Temporal Scale: The study utilizes flux data, implying continuous measurements over a period; however, specific start and end dates for the data collection are not provided in the text.

Methodology and Data

• Models used: Vegetation Photosynthesis Model (VPM), modified to VPM_urban by incorporating CO2 and AOD stress factors using regression algorithms.
• Data sources:
  • Flux data (urban and rural) from Shenzhen, China.
  • Remote sensing data: Landsat 8 and MODIS (used for data fusion).
  • Atmospheric CO2 concentration.
  • Aerosol Optical Depth (AOD).

Main Results

• The improved VPM (VPM_urban) demonstrated enhanced simulation accuracy for Gross Primary Production (GPP).
• For urban areas (VPM_urban), the simulation accuracy indices were: R² = 0.51, Root Mean Square Error (RMSE) = 0.11, Mean Absolute Error (MAE) = 0.85.
• For rural areas, the simulation accuracy indices were: R² = 0.65, RMSE = 1.65, MAE = 1.30.
• The Pearson correlation coefficient (r) for GPP simulated by VPM_urban in the urban area (TXS) increased from 0.62 to 0.71, indicating a significant improvement in accuracy.
• VPM_urban provides a more accurate assessment of evergreen broadleaf forest growth status in urban areas.

Contributions

• Developed an improved Vegetation Photosynthesis Model (VPM_urban) that explicitly incorporates CO2 concentration and Aerosol Optical Depth (AOD) as atmospheric stress factors, addressing a critical gap in existing models for urban environments.
• Demonstrated a significant improvement in GPP estimation accuracy for urban evergreen broadleaf forests by integrating these human-influenced atmospheric factors.
• Utilized data fusion techniques (Landsat 8 and MODIS) to acquire high-resolution remote sensing data, effectively addressing challenges posed by high spatial heterogeneity in urban settings.
• Provides valuable insights and a more accurate tool for urban planning and management, particularly for supporting carbon neutrality strategies and sustainable urban development.

Funding

• Not specified in the provided text.

Citation

@article{Dong2026Improving,
  author = {Dong, Guannan and Wang, Weimin and Liu, Kai and Yan, Ziyan and Gao, Long and Zhang, Furong and Chen, Shaohui},
  title = {Improving vegetation photosynthesis model (VPM) by incorporating CO2 and atmospheric aerosols: A case study using urban and rural flux data from Shenzhen, China},
  journal = {Physics and Chemistry of the Earth Parts A/B/C},
  year = {2026},
  doi = {10.1016/j.pce.2026.104430},
  url = {https://doi.org/10.1016/j.pce.2026.104430}
}