Utility of spectral vegetation index for estimation of gross CO2 flux under varied sky conditions

To estimate the gross CO2 flux (FCO2) of deciduous coniferous forest from canopy spectral reflectance, we introduced spectral vegetation indices (VIs) into a light use efficiency (LUE) model of mature Japanese larch (Larix kaempferi) forest. We measured the eddy covariance CO2 flux and spectral reflectance of larch canopy at half-hourly intervals during one growing season, and investigated the relationships between the parameters of the LUE model (FAPAR, ɛ) and 3 types of VIs (NDVI, PRI, EVI) in both clear sky and cloudy conditions. (fraction of absorbed photosynthetically active radiation) had a positive linear relationship with both NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index), and the sky condition had little effect on the relationships. The relative RMSE (root mean square error) of the APAR (absorbed photosynthetically active radiation) based on the incoming PAR and estimated FAPAR from a linear function of NDVI was less than 10.5%, irrespective of sky condition. ourly values of ɛ (conversion efficiency of absorbed energy) showed both seasonal variation related to leaf phenology and short-term variation related to light intensity due to varied sun position and sky condition. Both EVI and PRI (photochemical reflectance index) were significantly correlated with ɛ. EVI showed a positive linear relationship with ɛ as a result of their similar seasonal variation. However, since EVI did not detect short-term variation of ɛ, their relationship differed among sky conditions. On the other hand, although PRI could trace the short-term variation of ɛ in green needles, the relationship became non-linear due to drastic reduction of PRI in the senescent needles. RI/PRImin), a combined index based on a 6-day moving minimum value of PRI (PRImin), showed a linear relationship with half-hourly values of ɛ throughout the seasons irrespective of sky condition. This index allow us to estimate ɛ in all sky conditions with a smaller error (rRMSE = 35.2%) than using EVI or PRI alone (38.7%–48.7%). Consequently, this combined index-derived ɛ and NDVI-based FAPAR gave a low estimation error of FCO2 (rRMSE = 36.4%, RMSE = 8.3 μmol m− 2 s− 1). Although there are still various issues to resolve, including adaptive limit and combination of vegetation index type, we conclude that the combination of PRI and EVI increased the accuracy of estimation of CO2 uptake in deciduous forest even though sky conditions varied.