Published August 7, 2025
| Version v1
Dataset
Open
A cost-efficient and robust approach to monitor ecosystem photosynthesis using near-infrared enabled cameras
Authors/Creators
Description
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 lurinurlailasyahid@gmail.com or luri@nus.edu.sg