Proceedings Article | 28 October 2006
KEYWORDS: Vegetation, Data modeling, Solar radiation models, Remote sensing, Atmospheric modeling, Image processing, Temperature metrology, Ecosystems, Meteorology, Satellites
Terrestrial net primary production (NPP), as an important component of carbon cycle on land, not only indicates directly the production level of vegetation community on land, but also shows the status of terrestrial ecosystem. What's more, NPP is also a determinant of carbon sinks on land and a key regulator of ecological processes, including interactions among tropic levels. In the study, three existing models are combined with each other to assess net primary production in Haihe Basin, China. The photosynthetically active radiation (PAR) model of Monteith is used for the calculation of absorbed photosynthetically active radiation (APAR), the light utilization efficiency model of Potter et al. is used for determining the light utilization efficiency, and the surface energy balance system (SEBS) of Su is used into Potter's model to describe water stress in land wetness conditions. To assess NPP, We use NOAA-AVHRR data from November 2003 to September 2004 and the corresponding daily data of temperature and hours of sunshine obtained from meteorological stations in Haihe Basin, China. After atmospheric, geometrical and radiant corrections, every ten days NOAA data are processed to become an image of NDVI by means of the maximal value composition method (MVC) in order to eliminate some noises. Using these data, we compute NPP in spring season and spring season of 2004 in Haihe Basin, China. The result shows, in Haihe Basin, NPP for spring season is averaged to 336.10gC•m-2, and 709.16 gC•m-2 for autumn season. In spatial distribution, NPP is greater in both ends than in middle for spring season, and decrease increasingly from north to south for autumn season. Future work should rely on the integration of high and low resolution images to assess net primary production, which will probably have more accurately estimation.
