Assessment of canopy photosynthetic capacity and estimation of GPP by using spectral vegetation indices and the light–response function in a larch forest

Integration of CO2 flux observations with remote sensing technique and ecosystem modeling is expected to be useful for estimation of gross primary production (GPP). We focused on the changes in the two main parameters for the canopy-scale light–response curve—Pmax (maximum GPP at light saturation) and ϕ (initial slope)—as indicators to represent canopy photosynthetic capacity. We hypothesized that Pmax and ϕ could be evaluated by using spectral reflectance related to the changes in the levels of canopy nitrogen and chlorophyll. We analyzed the relationships between Pmax and ϕ, derived from tower-based CO2 flux observations, and ground-based spectral vegetation indices (VIs) in a temperate deciduous coniferous forest. The canopy-scale Pmax and ϕ showed clear seasonal changes accompanying phenological stages. Both the variations in Pmax and ϕ were strongly correlated with VIs, especially with the ratio vegetation index (RVI) and enhanced vegetation index (EVI), independent of the growth stages. Moreover, day-to-day short-term variations of Pmax and ϕ were affected by meteorological conditions such as vapor pressure deficit (VPD) and relative solar radiation which was calculated as the ratio of monitored radiation per theoretical maximum radiation. Thus, seasonal changes of Pmax and ϕ were effectively assessed by RVI or EVI, and their short-term variations were evaluated by the empirical relationships with VPD and relative solar radiation. We propose a new simple method for estimating GPP with good precision; by fitting the light–response function with the evaluated parameters, the estimated GPP reflects 3 types of temporal variation: diurnal, day-to-day, and seasonal.