Terrestrial gross primary production: Using NIRV to scale from site to globe.

Grayson Badgley,Christopher B Field,Leander D L Anderegg,Joseph A Berry

doi:10.1111/gcb.14729

Abstract

Terrestrial photosynthesis is the largest and one of the most uncertain fluxes in the global carbon cycle. We find that near-infrared reflectance of vegetation (NIRV ), a remotely sensed measure of canopy structure, accurately predicts photosynthesis at FLUXNET validation sites at monthly to annual timescales (R2 =0.68), without the need for difficult to acquire information about environmental factors that constrain photosynthesis at short timescales. Scaling the relationship between gross primary production (GPP) and NIRV from FLUXNET eddy covariance sites, we estimate global annual terrestrial photosynthesis to be 147PgC/year (95% credible interval 131-163PgC/year), which falls between bottom-up GPP estimates and the top-down global constraint on GPP from oxygen isotopes. NIRV -derived estimates of GPP are systematically higher than existing bottom-up estimates, especially throughout the midlatitudes. Progress in improving estimated GPP from NIRV can come from improved cloud screening in satellite data and increased resolution of vegetation characteristics, especially details about plantphotosynthetic pathway.

Highlights

Terrestrial photosynthesis (or gross primary production (GPP)) is responsible for fixing somewhere between 119 and 169 Pg C y-1, making GPP both the largest and most uncertain component of the global carbon cycle (Anav et al, 2015)
We find that near-infrared reflectance of vegetation (NIRV), a remotely sensed measure of canopy structure, accurately predicts photosynthesis at FLUXNET validation sites at monthly to annual timescales (R2 = 0.68), without the need for difficult to acquire information about environmental factors that constrain photosynthesis at short timescales
Scaling the relationship between GPP and NIRV from FLUXNET eddy covariance sites, we estimate global annual terrestrial photosynthesis to be 147 Pg C y-1 (95% credible interval 131-163 Pg C y-1), which falls between bottom-up GPP estimates and the top-down global constraint on GPP from oxygen isotopes

Summary

Introduction

Terrestrial photosynthesis (or gross primary production (GPP)) is responsible for fixing somewhere between 119 and 169 Pg C y-1, making GPP both the largest and most uncertain component of the global carbon cycle (Anav et al, 2015). Though many upscaling schemes exist, two approaches are by far the most widely used: machine learning (Beer et al, 2010; Tramontana et al, 2016) and satellite-driven mechanistic models (Running et al, 2004; Ryu et al, 2011) Both approaches integrate some combination of site-level abiotic characteristics, plant traits, and meteorology to estimate photosynthesis, using in situ fluxes from eddy covariance installations to calculate scaling factors that allow estimation of photosynthesis beyond tower footprints. To the extent plants allocate resources efficiently (Bloom et al, 1985; Field et al, 1995), this integrated measure of investment in light capture should scale with the capacity to fix CO2, providing a strong basis for new, satellite-derived estimates of GPP To test this hypothesis, we use the relationship between NIRV and in situ measurements of GPP derived from eddy covariance. We evaluate some of the limitations in the global dataset of NIRV and discuss options for refining the approach

Materials and Methods

Calibration

Upscaling

Site-level Validation

Global Upscaling

Uncertainty Analysis