Terrestrial gross primary production: Using NIR_V to scale from site to globe

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 147 Pg C/year (95% credible interval 131–163 Pg C/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 plant photosynthetic pathway.

Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops

“…We believe our study is the first one that explicitly investigated the relationships of f esc to canopy LUE P , which makes a comparison with other literature somewhat indirect. Nevertheless, a strong link to previous studies on the NIR V -GPP relationship (Badgley et al, 2019(Badgley et al, , 2017 can be established in the following way. APAR × f esc is calculated as the simple product of NIR V and PAR (Table 1).…”

“…For APAR × f esc , the approach of using f esc to correct for slope differences between SIF obs and GPP is 1) not possible as NIR V is already used to calculate APAR × f esc and 2) not desirable as f esc contains the LUE P -relevant information in addition to APAR. It seems, therefore, that for APAR × f esc -based GPP estimation, an ecosystem-dependent slope has to be applied as was partly done in Badgley et al (2019). This is particularly relevant for evergreen needleleaf forests (ENF) that have a much lower NIR reflectance despite rather high GPP during the growing period, as can be inferred also from previous studies investigating SIF obs -GPP relationships (Sun et al, 2018;Yao Zhang et al, 2018a).…”

“…In contrast to the Φ F -based reasoning above highlighting the role of leaf physiology, (Badgley et al, 2019(Badgley et al, , 2017 have argued that it is precisely the canopy structure that explains the SIF obs -GPP relationship. Their reasoning is based on the NIR reflectance of vegetation (NIR V ), a multi-spectral reflectance-based measurement that is strongly linear with both SIF obs and GPP at large spatial and long temporal scales.…”

A double peak in the seasonality of California's photosynthesis as observed from space

“…The advent of global remotesensing observations of solar-induced chlorophyll fluorescence (SIF) represents a breakthrough in our ability to constrain photosynthetic activity from space. This is because a number of studies have shown SIF to be a powerful proxy for photosynthesis both in laboratory environments (e.g., Baker, 2008) and at larger spatial scales (e.g., Frankenberg et al, 2011a;Parazoo et al, 2014;Yang et al, 2015Yang et al, , 2017Y. Sun et al, , 2018Magney et al, 2019a).…”

Estimating site-specific optimum air temperature and assessing its effect on the photosynthesis of grasslands in mid- to high-latitudes

“…The Photochemical Reflectance Index was found to be significantly correlated to LUE, and was effective in detecting seasonal carbon fluxes in evergreen ecosystems where FPAR and greenness-related vegetation indices change little (Garbulsky et al 2011;Middleton et al 2016). NIRv was better correlated to modeled MODIS FPAR than NDVI and significantly correlated to GPP (Badgley et al 2017), and has been used for GPP estimates globally in 0.5° (Badgley et al 2018). Also, significant linear relationships between GPP and OCO-2based SIF product (GOSIF) contributed to the work that estimated GPP in 0.05°using GOSIF (Li and Xiao 2019).…”