Analysis of Spatiotemporal Dynamics of the Chinese Vegetation Net Primary Productivity from the 1960s to the 2000s

Erping Shang, Erqi Xu, Hongqi Zhang, Fang Liu
2018 Remote Sensing  
Field net primary productivity (NPP) is useful in research modeling of regional and global carbon cycles and for validating results by remote sensing or process-based models. In this study, we used multiple models of NPP estimation and vegetation classification methods to study Chinese vegetation NPP characteristics, trends, and drivers using 7618 field measurements from the 1960s, 1980s, and 2000s. The values of other relevant NPP models, as well as process-based simulation and remote sensing
more » ... and remote sensing models, were compared. Our results showed that NPP ranged from 3 to 12,407 gC·m −2 ·year −1 with a mean value of 571 gC·m −2 ·year −1 . Vegetation NPP gradually decreased from the southeast to the northwest. Forest, farmland, and grassland NPP was 1152, 294, and 518 gC·m −2 ·year −1 , respectively. Total NPP of grassland was higher than that of farmland. Total terrestrial NPP decreased from 3.58 to 3.41 Pg C·year −1 from the 1960s to the 2000s, a decadal decrease of 4.7%. Total NPP in forests and grasslands consistently showed a decreasing trend and decreased by 0.46 Pg C·year −1 and 0.16 Pg C·year −1 , respectively, whereas NPP for farmland showed an opposite trend, with a growth of 0.45 Pg C·year −1 . Our research findings filled gaps in the information regarding NPP for the entire landmass of China based on field data from a long-term time series and provide valuable information and a basis for validation analyses by remote sensing models, as well as a robust quantification of carbon estimation to anticipate future development at the national and global scale. Resolution Imaging Spectroradiometer (MODIS) data) [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] into an estimation model, such as the Carnegie-Ames-Stanford-Approach (CASA) model [19] [20] [21] , the Global Production Efficiency Model (GLO-PEM) [22, 23] , or the Eddy Covariance-Light Use Efficiency (EC-LUE) model [24] , the Atmosphere-Vegetation Interaction Model (AVIM2) [25, 26] , or spatial interpolation [27, 28] . The remote sensing images can provide continuous, dynamic, and comprehensive land-surface information for any region on Earth [29, 30] . Unfortunately, many researchers avoid validating models with independent observations when estimating NPP by data-based models or remote sensing models. The results obtained by these models have differences with uncertainty [31-34], which may influence our understanding of the whole-ecosystem carbon balance [3, [35] [36] [37] . On the one hand, a coarse resolution can lead to an obvious accuracy loss when modeling NPP based on the spatial heterogeneity of the data [38], especially for the AVHRR datasets with a spatial resolution of 8 km [39, 40] . On the other hand, the MODIS datasets [41, 42] and Satellite Pour l'Observation de la Terre (SPOT) vegetation products [43] also have their limitations, with no data available before the year 2000, and the possibly confounding influence of cloud cover. Reliance solely on data-based models or remote sensing models will not yield the true NPP value because of the uncertainty of these models [31] [32] [33] [34] . Reliable data is needed for robust assessments of resource availability. Thus, it is necessary for field measurements of regional NPP because remote sensing may yield great uncertainty or time discontinuity. It is possible to use the continuous accumulation of field data by different scholars over long periods of time to show regional NPP more accurately at a large scale to resolve the problems associated with different remote sensing data and models, and provide valuable information for validation analyses by remote sensing models. However, NPP based on field data for Chinese vegetation, which contains forest, grassland, and farmland ecosystems at the national scale, has rarely been reported. Previous studies typically focused on small scales or a single Chinese vegetation ecosystem at the large scale [44] [45] [46] and they also lack long-term time series. At the same time, robust quantification of carbon estimation is needed because, to anticipate future development, we must understand the historic growth conditions across all biomes. Thus, it is of great significance to obtain a long-term NPP series using NPP derived from field observations. The specific objectives of this study were: (1) to compile NPP data using a Chinese vegetation classification method based on 7618 field data points between 1952 and 2010; (2) analyze its trends and drivers; and (3) to compare this estimated NPP with other relevant NPP models, as well as process-based simulation and remote sensing models.
doi:10.3390/rs10060860 fatcat:tmn52s2zjfgmbgwh7qxyotow2u