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Terrestrial gross primary production: Using NIRV to scale from site to globe
Grayson Badgley1,2, Leander D L Anderegg1,3, Joseph A Berry1
1Department of Global Ecology, Carnegie Institution for Science, Stanford, California.
Global Change Biology
|June 15, 2019
Summary
Terrestrial photosynthesis, a major carbon cycle component, is now accurately predicted using near-infrared reflectance of vegetation (NIRv). This remote sensing approach provides a global estimate of 147 Pg C/year, improving carbon cycle understanding.
Area of Science:
- Earth System Science
- Global Ecology
- Remote Sensing
Background:
- Terrestrial photosynthesis is a critical yet uncertain component of the global carbon cycle.
- Accurate estimation of gross primary production (GPP) is essential for understanding carbon dynamics.
Purpose of the Study:
- To develop a remote sensing-based method for estimating global terrestrial photosynthesis.
- To refine estimates of global gross primary production (GPP) using vegetation canopy structure.
Main Methods:
- Utilized near-infrared reflectance of vegetation (NIRv), a measure of canopy structure, to predict photosynthesis.
- Validated the NIRv-GPP relationship against FLUXNET eddy covariance data.
- Scaled the validated relationship globally to estimate annual terrestrial photosynthesis.
Main Results:
- NIRv accurately predicted photosynthesis at monthly to annual scales (R² = 0.68) without environmental data.
- Global annual terrestrial photosynthesis was estimated at 147 Pg C/year (95% CI: 131–163 Pg C/year).
- NIRv-derived GPP estimates were systematically higher than existing bottom-up estimates, particularly in midlatitudes.
Conclusions:
- Near-infrared reflectance of vegetation (NIRv) offers a robust method for estimating global GPP.
- The NIRv approach provides a valuable estimate that bridges bottom-up and top-down carbon cycle constraints.
- Future improvements can be achieved through enhanced satellite data processing and higher resolution vegetation characteristic data.
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