Methodology for spatial scaling in NPP under the influence of variable topography and vegetation

Both surface topography and vegetation heterogeneity are important factors introducing biases in regional ecological modeling, especially when the modeling is made at large grids. Several studies have demonstrated that gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. These biases result from overlooking sub-pixel variability of land surface characteristics. This study suggests a simple algorithm that uses sub-pixel information on the spatial variability of vegetation and surface topography to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. A spatial scaling algorithm is developed to correct biases in coarse-resolution NPP estimation. This algorithm considers the effect of sub-pixel heterogeneities of land cover, leaf area index (LAI), slope and elevation. Its application to a carbon-hydrology coupled model estimates made at a 1-km resolution over a watershed (named Baohe River Basin) located in the southwestern part of Qinling Mountains, in China, Shaanxi Province, China, improved estimates of average NPP as well as its temporal and spatial variability.