The comparisons of carbon sink/source of Chinese grassland ecosystem using different approaches

Grassland ecosystem, one of the widely distributed vegetation type, is of great significance in estimation of global carbon cycle. The grassland net primary productivity (NPP) plays an important role in global change and carbon balance; it is always used as an index of C cycle in terrestrial ecosystems at landscape and regional scales. This paper compared grassland net primary productivity (NPP) estimation methods, including filed observation and models (mainly climate productivity model, process model and light energy use efficiency model). As every method has its limited, it was convenient and prompt to estimate NPP using remote sensing data and could achieve fast monitoring of all kinds of vegetation. Meanwhile, these researches, such as influence on the terrestrial ecosystem carbon source/sink caused by climate change and atmospheric CO₂ increase and effects on carbon cycle caused by terrestrial ecosystems type conversion, are not studied enough; either scene simulation based on models or contrast studies, there is even larger gap in this research in China. Therefore, we studied the NPP of the main grassland in China( Sinkiang, Inner Mongolia, grassplot in Southern China) using the Remote Sensing(RS), Geography Information System(GIS), modified Cellular Automata(CA) and integrated model with high precision testified by MODIS data and meteorological data, which is much more suitable for grassland in China, and extend to the Europe and North America, then grassland ecosystem carbon source/sink estimation using remote sensing data is proposed. The results will provide the scientific basis for the government to account grassland carbon balance and carbon storage in the future. Under the condition of increasing measuring precision and region density, it is superior to use remote sensing in grassland NPP measurement, which provides baseline for the terrestrial carbon cycle mechanism and global carbon balance research.