A 33-Year NPP Monitoring Study in Southwest China by the Fusion of Multi-Source Remote Sensing and Station Data.

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a regional NPP study in southwest China, with a 33-year time span and a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the advantages of different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated utilizing the improved Yang hybrid model (YHM), based on meteorological station data. The accuracy of the data processes is proved reliable by verification experiments. Moreover, NPP estimated by fused NDVI shows an obvious improved accuracy than that based on the original data. The spatio-temporal analysis results indicated that 67% of the study area showed an increasing NPP trend over the past three decades. The correlation between NPP and precipitation was significant heterogeneous at the monthly scale; specifically, the correlation is negative in the growing season and positive in the dry season. Meanwhile, the lagged positive correlation in the growing season and no lag in the dry season indicated the important impacts of precipitation on NPP. What is more, we found that there are three distinct stages during the variation of NPP, which were driven by different climatic factors. Significant climate warming led to a great increase of NPP from 1992 to 2002, while NPP clearly decreased during 1982-1992 and 2002-2014 due to the frequent droughts caused by the precipitation decrease.