Estimation of Forest Gross Primary Productivity in North-East China by a Physiologically-based Model Driven with Remote Sensing Data

Forest gross primary productivity (GPP), the capacity of fixing CO 2 through photosynthesis, plays an important role in global changing and carbon cycle. In this study, we proposed a methodology to accurately and efficiently estimate GPP for six dominant forest types in north-east China using the remote sensing imagery driven Physiological Principles Predicting Growth model (3-PG). The GPP were accurately estimated and the results revealed that the largest GPP is obtained from the populus tremula (POTR) forest, with a range of 11.91 Mg C ha-1 year-1 and 40.11 Mg C ha-1 year-1. While the GPP of spruce (PIAS), dahurian larch (LAGM), and fir (ABFA) are similar, with an average of 14.47 Mg C ha-1 year-1, 14.85 Mg C ha-1 year-1, 13.39 Mg C ha-1 year-1, respectively. The GPP of korean pine (PIKO) and white birch (BEPL) are the smallest and with mean values of 7.38 Mg C ha-1 year1 and 3.70 Mg C ha-1 year-1, respectively. In addition, the comparison with previous researches indicated that the proposed module is reasonable and credible.