Remotely monitoring ecosystem respiration from various grasslands along a large-scale east–west transect across northern China

Abstract

Background: Grassland ecosystems play an important role in the terrestrial carbon cycles through carbon emission by ecosystem respiration (R e) and carbon uptake by plant photosynthesis (GPP). Surprisingly, given R e occupies a large component of annual carbon balance, rather less attention has been paid to developing the estimates of R e compared to GPP. Results: Based on 11 flux sites over the diverse grassland ecosystems in northern China, this study examined the amounts of carbon released by R e as well as the dominant environmental controls across temperate meadow steppe, typical steppe, desert steppe and alpine meadow, respectively. Multi-year mean R e revealed relatively less CO 2 emitted by the desert steppe in comparison with other grassland ecosystems. Meanwhile, C emissions of all grasslands were mainly controlled by the growing period. Correlation analysis revealed that apart from air and soil temperature, soil water content exerted a strong effect on the variability in R e , which implied the great potential to derive R e using relevant remote sensing data. Then, these field-measured R e data were up-scaled to large areas using time-series MODIS information and remote sensing-based piecewise regression models. These semi-empirical models appeared to work well with a small margin of error (R 2 and RMSE ranged from 0.45 to 0.88 and from 0.21 to 0.69 g C m −2 d −1 , respectively). Conclusions: Generally, the piecewise models from the growth period and dormant season performed better than model developed directly from the entire year. Moreover, the biases between annual mean R e observations and the remotely-derived products were usually within 20%. Finally, the regional R e emissions across northern China's grasslands was approximately 100.66 Tg C in 2010, about 1/3 of carbon fixed from the MODIS GPP product. Specially, the desert steppe exhibited the highest ratio, followed by the temperate meadow steppe, typical steppe and alpine meadow. Therefore, this work provides a novel framework to accurately predict the spatio-temporal patterns of R e over large areas, which can greatly reduce the uncertainties in global carbon estimates and climate projections.

Terrestrial carbon cycle: tipping edge of climate change between the atmosphere and biosphere ecosystems

“…101 Besides the primary productions, phenology and VIs, RS is integrated to estimate the canopy level N, CH 4 and other biophysical parameters to estimate the key carbon flux drivers at variable scales. 16,45 Various studies such as that by Turner et al , 83 Potter et al 118 and Tang et al 136 highlighted the prevailing correlation between VIs and key flux drivers (NPP, NEE, and ER). This has been further diversified by developing the light use efficiency (LUE) models, 15,47,137 which are purely based on RS datasets and the integration of meteorological parameters.…”

“…This cascades to the quantification accuracy, as the point error sources with higher frame in terms of other estimation sources. Various such sources of errors result in the spatial and temporal variability in the terrestrial carbon dynamics 118,136 , which obscure the magnitude and trend of carbon flux estimations and potentially alter the interannual variability and long-term trend of primary productivities 13,76 . As RS -an inevitable source for terrestrial carbon estimation, plummeting such errors will help in erudite the carbon dynamics, both spatially and temporally.…”

“…The terrestrial carbon dynamics is controlled by ER and GPP, which are highly disturbed by various associating factors both in the positive and negative aspect. Studies state that about 270 Pg C year -1 is involved in the biotic terrestrial flux, which exhibits intra-and inter-annual variability at a significant level 35,76,136,150 . Such uncertainties are highly associated with various carbon responders, i.e., the intra-annual associates like climate variables and inter-annual associates like hydrology, LUC and soil N etc.…”

Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model

“…Moreover, the absence of Rh in the selected predictive variables could be because of its correlation with Rs (r=0.55, p<0.01). However, it's worth to note that both Rs and Rh were only part of ecosystem respiration and highaccuracy estimation of global ecosystem respiration will improve the performance of data-driven models for estimating global NEE [46].…”

Plant diversity is coupled with soil fungal diversity in a natural temperate steppe of northeastern China

“…Temperate steppes are one of the largest terrestrial ecosystems worldwide, and they play an important role in regional climate change and global carbon cycling (Guo et al, 2018). The temperate steppe in Inner Mongolia comprises approximately 22% of the total grassland area of China (Tang et al, 2020). The plant community in Inner Mongolia varies sharply from west to east, which makes Inner Mongolia a natural laboratory for research on the relationship between plant diversity and soil fungi diversity (Liu et al, 2008;Ma et al, 2015).…”