Regional-scale data assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM) over Siberia

Progress in Earth and Planetary Science

Table 2 Data used for cross-comparisons

Data	Period	Description	References
LAI (MODIS)	2003–2012	LAI data assimilated in this study. Estimation using a radiative transfer model and satellite-observed data.	Knyazikhin et al. (1999)
Overstory LAI	2003–2012	Estimation using a radiative transfer model and satellite-observed data (SPOT-VEGETATION).	Delbart et al. (2005); Kobayashi et al. (2010)
GPP (FLUXCOM)	2003–2012	Estimation with three machine learning methods: the artificial neural network (ANN), multivariate regression splines (MARS), and random forest (RF) using the flux partitioning method of Reichstein et al. (2005). These machine learning methods make use of in-situ flux measurements and diverse explanatory variables, such as meteorological data and satellite-based vegetation indices.	Tramontana et al. (2016); Jung et al. (2017)
Aboveground biomass	2003–2012	Estimation using the relationships between satellite-observed vegetation optical depth and the aboveground biomass.	Liu et al. (2015)