A cost-efficient and robust approach to monitor ecosystem photosynthesis using near-infrared enabled cameras

This is the data and code to analyze the relationship between near-infrared reflectance and photosynthetically active radiation (NIRvP) and gross primary productivity (GPP) using near-infrared enabled cameras across eddy covariance sites. For more details on scientific implications, methodology and validation, please refer to the manuscript: Syahid, L. N., Luo, X., Zhao, R., Yu, L., Tan, L. M., Detto, M., Sonnentag, O. (2025). A cost-efficient and robust approach to monitor ecosystem photosynthesis using near-infrared enabled cameras. Journal of Geophysical Research: Biogeosciences. Under review The folders we provide include: 1. data Including the processed data from this study: Folder `weekly_NIRvP-GPP/`: contains 28 files, each representing weekly NIRvP and GPP data from one station. Used to generate Figure 1, 2, 5, and 6. File `Regression_Result.csv`: contains annual linear regression results for all stations. Used to generate Figure 3. File `predicted_conversion_factors.csv`: contains NIRvP, GPP and predicted conversion factor in the global scale resulted from machine learning. Used to generated Figure 4. 2. code `1_Processing_GPP.py`: to process GPP data from flux towers. `2_Processing_NIRvP.py`: to process phenocam data and calculate NIRvP. `3_Fig1_5_6.py`: to generate Figure 1, 5, and 6. `4_Fig2.py`: to generate Figure 2. `5_Fig3_4.py`: to generate Figure 3 and 4. For inquiries about the dataset and the method, please contact Luri Nurlaila Syahid at [e-mail] or [e-mail]