Abstract
Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R2 = 0.6427, p < 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31 C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.
Highlights
Gross primary productivity (GPP) is an important component of the terrestrial carbon cycle and exhibits significant spatial and temporal variations
For half-hourly comparison with light use efficiency (LUE), the top-canopy photochemical reflectance index (PRI) was calculated as the mean value of the multi-angle PRI, which could reduce the angular effects to some extent with hundreds of samples evenly distributed at different angles
For half-hourly comparison with LUE, the top-canopy PRI was calculated as the mean value of the multi-angle PRI, which could reduce the angular effects to some extent with hundreds of samples ReevmeontelySendsi.s2t0r1ib5,u7t,e1d693a8t–d16i9ff6e2rent angles
Summary
Gross primary productivity (GPP) is an important component of the terrestrial carbon cycle and exhibits significant spatial and temporal variations. Reliable estimation of GPP is a prerequisite for quantifying terrestrial carbon sinks and sources. Remote sensing data have the potential for estimating the regional distributions of GPP, as they can provide the needed spatial and temporal coverages. They have been widely used to calculate GPP in combination with light use efficiency (LUE) models, which commonly express GPP as the product of the amount of absorbed photosynthetically active radiation (APAR) and a LUE term (GPP = LUE APAR) [1,2,3,4]. The incident PAR can normally be obtained from meteorological observation while FPAR can be retrieved from remote sensing data [5,6,7,8]
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