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 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.
Highlights
As a key component of the global carbon cycle, the terrestrial ecosystem is the main force that can uptake free carbon from the atmosphere and convert it into organic compounds [1,2]
Where Rs(x, t) is the total solar radiation of pixel x in month t; the coefficient 0.5 is the approximate ratio of photosynthetic active radiation (0.4–0.7 μm) to total solar radiation; and FPAR(x, t) is the fraction of photosynthetic active radiation absorbed by the vegetation canopy, which is determined by the normalized difference vegetation index (NDVI) and vegetation types: FPAR(x, t) = min[(SR(x, t) − SRmin)/(SRmax − SRmin), 0.95], (4)
NDVI is one of the core parameters in modeling net primary productivity (NPP) at regional or larger scales, which decides the characteristics of the NPP result
Summary
As a key component of the global carbon cycle, the terrestrial ecosystem is the main force that can uptake free carbon from the atmosphere and convert it into organic compounds [1,2]. Net primary productivity (NPP), which is the residual amount of organic matter produced by vegetation photosynthesis minus its autotrophic respiration consumption, is an important ecological indicator for the status of a terrestrial ecosystem carbon budget [6,7]. NPP can be precisely acquired by field measurements at a site level, but it is not feasible for regional or larger scales, as it costs a lot and is unable to obtain spatially full coverage and continuously long time series. In this case, model-based estimation is an efficient approach, and a large number of models have been proposed in previous studies [8]. Satellite land-cover data and spectral vegetation index products (i.e., the normalized difference vegetation index, NDVI) are the most commonly used core data when modeling NPP of a large region [8]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call