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Improving global gross primary productivity estimation by fusing multi-source data products.
1State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Heliyon
|March 29, 2022
Summary
This study introduces a new method to estimate global gross primary productivity (GPP) by merging multiple GPP datasets. The resulting weighted GPP data reduces uncertainty and improves carbon balance understanding.
Area of Science:
- Ecology
- Climate Science
- Remote Sensing
Background:
- Accurate terrestrial gross primary productivity (GPP) is crucial for climate change decisions and global carbon balance.
- Inconsistent GPP estimates from various products create significant uncertainty.
Purpose of the Study:
- To improve the quantification of global terrestrial GPP by integrating multiple GPP products.
- To develop a new weighted GPP dataset using the Bayesian-based Three-Cornered Hat (BTCH) method.
Main Methods:
- Utilized the Bayesian-based Three-Cornered Hat (BTCH) method for data fusion.
- Integrated multiple source GPP products without prior knowledge.
- Generated a new weighted GPP dataset.
Main Results:
- The weighted GPP dataset significantly reduces random errors compared to individual datasets.
- The fusion method effectively leverages characteristics of multi-source GPP data.
- The weighted dataset accurately reproduces interannual variations in regional GPP.
Conclusions:
- The BTCH-based merging scheme effectively generates a new GPP dataset integrating multi-source information.
- This approach offers novel insights for global GPP estimation.
- The improved GPP dataset enhances understanding of the global carbon cycle.
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