  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 33-year regional NPP study in
southwest China, at 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 respective advantages of the different remote
sensing datasets. As another key parameter for NPP modeling, the total solar
radiation was calculated by the improved Yang hybrid model (YHM), using
meteorological station data. The verification described in this paper proved
the feasibility of all the applied data processes, and a greatly improved
accuracy was obtained for the NPP calculated with the final processed NDVI. The
spatio-temporal analysis results indicated that 68.07% of the study area showed
an increasing NPP trend over the past three decades. Significant heterogeneity
was found in the correlation between NPP and precipitation at a monthly scale,
specifically, the negative correlation in the growing season and the positive
correlation in the dry season. The lagged positive correlation in the growing
season and no lag in the dry season indicated the important impact of
precipitation on NPP.
