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In this work, we present a strategy for obtaining the dynamics of forest above-ground biomass (AGB) at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering only one of the terms since the cumulative carbon flux should be consistent with AGB increments. We estimated forest AGB dynamics by combining two types of models driven by field, remote sensing, and auxiliary data. The strategy was successfully applied to the Qilian Mountains, a cold arid region located in northwest China.

In the first step, we improved the efficiency of existing non parametric methods for estimating forest AGB. We applied the Random Forest (RF) model in order to pre-select the most relevant remotely sensed features in Landsat Thematic Mapper 5 (TM) and ASTER GDEM V2 products (GDEM). These features were further used to construct an optimal configuration for the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one out (LOO) method indicated that the optimal k-NN configuration yielded satisfactory performance (R-2 = 0.70 and RMSE = 24.52 tones ha(-1)). Hence, the k-NN configuration was used to generate a regional forest AGB basis map for 2009. In the second step, we obtained one seasonal cycles (2011) of carbon fluxes using the MODIS MOD_17 GPP (MOD_17) model that was driven by meteorological fields of a numerical weather prediction model (WRF) and calibrated to Eddy Covariance (EC) flux tower data. The calibrated model for 2010_well predicted GPP for 2011 (R-2 = 0.88 and RMSE = 5.02 gC m(-2) 8d(-1)). In the third step, we calibrated the ecological process model (Biome-BioGeochemical Cycles (Biome-BGC)) to above GPP estimates (for 2011) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. The Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R-2 = 0.75 and RMSE = 1. 27 gC m(-2) d(-1) for GPP, and R-2 = 0.61 and RMSE = 1.17 gC m(-2) d(-1) for NEE). We used Biome-BGC to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments, were compared to dendrochronological measurements (R-2 = 0.73 and RMSE = 46.65 g m(-2) year(-1)). The calibrated Biome-BGC model provided estimates of forest carbon fluxes that were converted into interannual AGB increments according to site-calibrated coefficients. With combination of these increments with the AGB map of 2009, the modeling of forest AGB dynamics was accomplished.