Spatial scaling of net primary productivity using subpixel information

Spatial scaling is of particular importance in remote sensing applications to terrestrial ecosystems where spatial heterogeneity is the norm. Surface parameters derived at different resolutions can be considerably different even though they are derived using the same algorithms. This article addresses issues related to spatial scaling of net primary productivity (NPP). The main objective is to develop algorithms for spatial scaling of NPP using subpixel information. NPP calculations were performed using the Boreal Ecosystem Productivity Simulator (BEPS). The area of interest is near Fraserdale, Ontario, Canada. It is found from this investigation that lumped (coarse resolution) calculations can be considerably biased (by +14.9% on average) from the distributed (fine resolution) case. Based on these results, algorithms for removing these biases in lumped NPP are developed using subpixel land cover type information. The correlation between the distributed NPP and lumped NPP is improved from r2=0.16 to r2=0.59 after the correction. In addition, subpixel leaf area index (LAI) information is used to reduce the remaining biases. After the LAI correction, the correlation is further improved to r2=0.90.