Abstract
In this study, the net primary productivity (NPP) in China from 2001 to 2012 was estimated based on the Carnegie-Ames-Stanford Approach (CASA) model using Moderate Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, and the accuracy was verified by a ChinaFLUX dataset. It was found that the spatiotemporal variations in NPP present a downward trend with the increase of latitude and longitude. Moreover, the influence of climate change on the evolution of NPP shows that NPP has had different impact factors in different regions and periods over the 12 years. The eastern region has shown the largest increase in gross regional product (GRP) and a significant fluctuation in NPP over the 12 years. Meanwhile, NPP in the eastern and central regions is significantly positively correlated with annual solar radiation, while NPP in these two regions is significantly negatively correlated with the growth rate of GRP. It is concluded that both the development of the economy and climate change have influenced NPP evolution in China. In addition, NPP has shown a steadily rising trend over the 12 years as a result of the great importance attributed to ecological issues when developing the economy.
Highlights
As one of the key components of the terrestrial carbon cycle, net primary productivity (NPP) accounts for most of the carbon flux between the atmosphere and biosphere among the pools and fluxes that make up the cycle[7]
The ChinaFlux dataset provides the daily GPP of the observation sites from 2003 to 2005, which we used as annual GPP
We verified the model NPP precision by the use of a ChinaFLUX dataset, and the average relative error was less than 20% for five of the eight sites
Summary
As one of the key components of the terrestrial carbon cycle, NPP accounts for most of the carbon flux between the atmosphere and biosphere among the pools and fluxes that make up the cycle[7]. Temperature/water availability is commonly incorporated into the LUE models[10] One such technique is the Carnegie-Ames-Stanford Approach (CASA) model for estimating NPP from remote sensing data[11]. Zhang et al.[17] obtained the mean ratio of the NPP/GPP of different vegetation types using 10 years of global remote sensing data from 2000 to 2009, which we used in this study to calculate the NPP from the observed GPP. The objectives of this study were as follows: (1) to estimate the NPP of the main territories in China during the period from 2001 to 2012, based on MODIS and meteorological data under the CASA model; (2) to apply the flux data from the ChinaFLUX network to verify the accuracy of the model; (3) to assess the spatiotemporal variation of NPP over the study area and explore the influence of climate change on the evolution of NPP; and 4) to analyze the relationship between the dynamics of NPP and human factors such as GRP and population, alongside economic regionalization
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