Analysis of spatial and temporal patterns of aboveground net primary productivity in the Eurasian steppe region from 1982 to 2013

Abstract

To explore the importance of the Eurasian steppe region (EASR) in global carbon cycling, we analyzed the spatiotemporal dynamics of the aboveground net primary productivity (ANPP) of the entire EASR from 1982 to 2013. The ANPP in the EASR was estimated from the Integrated ANPPNDVI model, which is an empirical model developed based on field‐observed ANPP and long‐term normalized difference vegetation index (NDVI) data. The optimal composite period of NDVI data was identified by considering spatial heterogeneities across the study area in the Integrated ANPPNDVI model. EASR's ANPP had apparent zonal patterns along hydrothermal gradients, and the mean annual value was 43.78 g C m−2 yr−1, which was lower than the global grasslands average. Compared to other important natural grasslands, EASR's ANPP was lower than the North American, South American, and African grasslands. The total aboveground net primary productivity (TANPP) was found to be 378.97 Tg C yr−1, which accounted for 8.18%–36.03% of the TANPP for all grasslands. In addition, EASR's TANPP was higher than that of the grasslands in North America, South America, and Africa. The EASR's TANPP increased in a fluctuating manner throughout the entire period of 1982–2013. The increasing trend was greater than that for North American and South American and was lower than that for African grasslands over the same period. The years 1995 and 2007 were two turning points at which trends in EASR's TANPP significantly changed. Our analysis demonstrated that the EASR has been playing a substantial and progressively more important role in global carbon sequestration. In addition, in the development of empirical NDVI‐based ANPP models, the early–middle growing season averaged NDVI, the middle–late growing season averaged NDVI and the annual maximum NDVI are recommended for use for semi‐humid regions, semi‐arid regions, and desert vegetation in semi‐arid regions, respectively.

Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau

“…Uncertainties induced by sensors change and degradation can influence NDVI derived from GIMMS and SPOT, especially at the breakpoint due to sensors change (Tian et al., ). For instance, Zhang, Zhang, Dong, and Xiao () highlighted that GIMMS 2g ‐NDVI showed abnormal values across the Tibetan Plateau after 2000, which likely resulted in a negative trend of NPP A estimated based on GIMMS 3g ‐NDVI since 2000 (Jiao et al., ). To extend the study periods, some researchers used GIMMS 2g data with MODIS data to simulate NPP A , with taking the year 2000 (Huang et al., ; Zhang, Zhang, et al., ) or 2001 (Chen et al., ) as the time of turning point.…”

Surveying pasture communities in diachronic analyses by 3D models: the diachronic canopy variation model

“…Traditional AGB approaches could fail in monitoring such dynamics due to the limitations caused by biomass harvesting. Non-disturbing techniques based on AGB proxies derived from remote-sensed data are a consistent way to analyze temporal dynamics of primary productivity (Jiao et al 2017), but their efficacy could be limited for fine community-scale applications. Therefore, SfMs can supply a powerful way to investigate local AGB temporal patterns.…”

The impact of teleconnections on the temporal dynamics in aboveground net primary productivity of the Mongolian Plateau grasslands

“…The Integrated ANPP NDVI model developed by Jiao et al . (2017) was calibrated based on the previous NDVI3g.v0 data set, which only covered NDVI data spanning 1981 to 2013. Therefore, the NDVI3g.v1 data set was used in this study.…”

“…Annual ANPP data for 1982-2015 at a 0.083 spatial resolution simulated by the Integrated ANPP NDVI model were used in this study. The Integrated ANPP NDVI model developed by Jiao et al (2017) is an empirical model developed based on field annual ANPP observations and Normalized Difference Vegetation Index (NDVI) data. Spatial heterogeneities across the study area and composite periods of NDVI data are considered (Jiao et al, 2017).…”