Abstract

Net primary production (NPP) serves as the primary carbon source in terrestrial ecosystems, which is the key regulator of ecological processes. Remote sensing data play an irreplaceable role in the simulation of regional vegetation production because they can provide continuous spatial and temporal variations in vegetation. In this chapter, the Carnegie Ames Stanford approach (CASA) model is used to estimate NPP in Northeast China, which is a model based on light use efficiency. This chapter discussed the influence of five calculated fractions of photosynthetically active radiation (FPAR) based on global inventory modeling and mapping studies (GIMMS) and normalized difference vegetation index (NDVI) datasets on NPP estimation. The results revealed that the calculation method of FPAR has an important influence on the regional estimation of NPP. The NPP values are diverse for various FPAR-estimated cases. The NPP values from five estimation cases in this study are all greater than those of MOD17A3 NPP products. The estimation results revealed that the average annual NPP is 760 gC/m²·a in Northeast China from 1982 to 2015. In general, the interannual fluctuations of the simulation results of the five cases are similar. The pixels with a significant increase are much greater than those with a significant decrease during the study period. The significant increase and decrease pixels are located in southern and eastern the study area, respectively.