MODIS Based Estimation of Forest Aboveground Biomass in China

Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventorybased timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha(-1), with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y(-1) and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y(-1). During the 2000s, the forests in China sequestered C by 61.9 Tg C y(-1), and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests.National Natural Science Foundation of China [41125004]; Chinese Ministry of Environmental Protection Grant [201209031]; National Youth Top-notch Talent Support Program in ChinaSCI(E)PubMedARTICLEslpiao@pku.edu.cn6e01301431