Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia

This paper describes a method of integrating remotely sensed data with an ecosystem model to estimate net primary productivity (NPP) in East Asia. We improved the Boreal Ecosystem Productivity Simulator (BEPS) model for global NPP estimation by incorporating a new land cover map and employed a robust Normalized Difference Vegetation Index-Leaf Area Index (NDVI-LAI) algorithm. Using this method, we produced a map showing the distribution of annual NPP in East Asia in 1998 and calculated that the mean NPP for that area in that year was 634 g C/m(2)/year. Comparing the estimated NPP obtained from model computation with the observed NPP obtained from an NPP database, we found that the estimated NPP closely approximates the observed NPP, with an average error of - 20%. We checked the accuracy of a six-biome land cover map using a Geographic Information System (GIS) data set for Japan [Data Sets for GIS on the Natural Environment, Japan (DS_GIS_NEJ), Japan Environment Agency, Ver. 2, 1999] and how the accuracy of the map affects NPP estimation. Results show that an accurate land cover map is essential if one is to accurately and reliably estimate NPP, and it is especially crucial if one is to estimate the NPP of an individual biome (e.g., for crop prediction). (C) 2002 Elsevier Science Inc. All rights reserved.