Skip to main content

Table 2 Data used for cross-comparisons

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

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)