Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China

Net ecosystem productivity (NEP) plays an important role in understanding function and the global carbon cycle. In this paper, key parameters of Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (εmax), was optimized by using vegetation classification data. Then, NEP estimated coupling CASA geostatistical model soil respiration (GSMSR) respiration–soil heterotrophic (Rs-Rh) relationship model. The ground observations from ChinaFLUX were used to verify estimation accuracy. results showed that R2 increased 0.411 0.774, RMSE decreased 21.425 gC·m−2·month−1 12.045 gC·m−2·month−1, indicating optimizing data effective method improve accuracy NEP. On basis, spatial temporal distribution China analyzed. research indicated monthly variation a single peak curve with summer as peak, which generally presented pattern southern region > northern Qinghai–Tibet northwest region. Furthermore, 2001 2016, most regions non-significant level upward trend, but main cropland (e.g., North Plain Northeast Plain) some grassland Ngari Plateau Xilin Gol League Inner Mongolia) non-significant-level downward trend. study can deepen sources/sinks China, provide reference for regional cycle research.