A 33-year NPP monitoring study in southwest China by the fusion of multi-source remote sensing and station data

TL;DR

This study presents a 33-year regional NPP analysis in southwest China using multi-source remote sensing and station data, revealing spatial and temporal trends and the influence of precipitation on NPP.

Contribution

It introduces a multi-sensor fusion framework for creating a long-term NDVI time series and improves NPP estimation accuracy using combined remote sensing and meteorological data.

Findings

68.07% of the area showed increasing NPP over 33 years

Significant seasonal heterogeneity in NPP-precipitation correlation

Precipitation impacts NPP with lag effects in the growing season

Abstract

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.