Sasai, T; Saigusa, N; Nasahara, KN; Ito, A; Hashimoto, H; Nemani, R; Hirata, R; Ichii, K; Takagi, K; Saitoh, TM; Ohta, T; Murakami, K; Yamaguchi, Y; Oikawa, T (2011). Satellite-driven estimation of terrestrial carbon flux over Far East Asia with 1-km grid resolution. REMOTE SENSING OF ENVIRONMENT, 115(7), 1758-1771.
The terrestrial carbon cycle is strongly affected by natural phenomena, terrain heterogeneity, and human-induced activities that alter carbon exchange via vegetation and soil activities. In order to accurately understand terrestrial carbon cycle mechanisms, it is necessary to estimate spatial and temporal variations in carbon flux and storage using process-based models with the highest possible resolution. We estimated terrestrial carbon fluxes using a biosphere model integrating eco-physiological and mechanistic approaches based on satellite data (BEAMS) and observations with 1-km grid resolution. The study area is the central Far East Asia region, which lies between 30 degrees and 50 degrees north latitude and 125 and 150 east longitude. Aiming to simulate terrestrial carbon exchanges under realistic land surface conditions, we used as many satellite-observation datasets as possible, such as the standard MODIS, TRMM, and SRTM high-level land products. Validated using gross primary productivity (GPP), net ecosystem production (NEP), net radiation and latent heat with ground measurements at six flux sites, the model estimations showed reasonable seasonal and annual patterns. In extensive analysis, the total GPP and NPP were determined to be 2.1 and 0.9 PgC/year, respectively. The total NEP estimation was + 5.6 TgC/year, meaning that the land area played a role as a carbon sink from 2001 to 2006. In analyses of areas with complicated topography, the 1-km grid estimation could prove to be effective in evaluating the effect of landscape on the terrestrial carbon cycle. The method presented here is an appropriate approach for gaining a better understanding of terrestrial carbon exchange, both spatially and temporally. (C) 2011 Elsevier Inc. All rights reserved.