The estimation and analysis of NPP from 1982 to 2014 in Yunnan province based on multi-source remote sensing data

Net primary productivity (NPP), as an important indicator for the carbon sequestration capacity of vegetation, has become one of the hotspots in global climate change research under the background of continuous increasing of CO
2
concentration. Remote sensing data based model is an effective and widely used method to obtain NPP at regional and global scales. While the spatial and temporal resolution of estimated result is limited by the resolution of inputted NDVI data. In this paper, a new framework was proposed to simulate long time series and fine scale NPP based on multi-source remote sending data in Yunnan province, China. GIMMS3g NDVI datasets and MODIS NDVI products were integrated to construct the consistent and high quality monthly NDVI data from 1982 to 2014 with 1km spatial resolution. There processing steps were designed to reach this target, successively are reconstruction, normalization and multi-sensor fusion. Then the long term NPP were calculated by Carnegie-Ames-Stanford approach (CASA) model with the carefully interpolated meteorological data. The results showed a gradually decreased NPP from the southwest to the northeast in study area. Furthermore, the annual NPP presents a volatile upward trend in the past 33 years, consistent with the trend of temperature in.