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



Grassland ecosystems play an important role in the terrestrial carbon cycles through carbon emission by ecosystem respiration (Re) and carbon uptake by plant photosynthesis (GPP). Surprisingly, given Re occupies a large component of annual carbon balance, rather less attention has been paid to developing the estimates of Re compared to GPP.


Based on 11 flux sites over the diverse grassland ecosystems in northern China, this study examined the amounts of carbon released by Re as well as the dominant environmental controls across temperate meadow steppe, typical steppe, desert steppe and alpine meadow, respectively. Multi-year mean Re revealed relatively less CO2 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 Re, which implied the great potential to derive Re using relevant remote sensing data. Then, these field-measured Re 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 (R2 and RMSE ranged from 0.45 to 0.88 and from 0.21 to 0.69 g C m−2 d−1, respectively).


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 Re observations and the remotely-derived products were usually within 20%. Finally, the regional Re 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 Re over large areas, which can greatly reduce the uncertainties in global carbon estimates and climate projections.


Although the terrestrial biosphere absorbs nearly a quarter of anthropogenic CO2 emissions and plays a critical role in mitigating global climate warming, the land C sequestration potential of different ecosystems remains highly uncertain, leading to large uncertainties in future climate projections [1, 2]. During the past several decades, substantial advances have allowed for spatially continuous, long-term estimation of terrestrial gross primary productivity (GPP) based on the satellite remote sensing data, climate data and ecosystem models [3,4,5,6]. However, net ecosystem C budget depends upon the balance of C fixation through vegetation photosynthesis and C loss from ecosystem respiration (Re) [7, 8], which requires accurate estimates of not only GPP but also Re across biomes.

Actually, terrestrial Re from the biosphere to the atmosphere represents a large component of annual carbon budget, which even exceeds the amount of GPP [9, 10]. It has excited much interest in evaluating the balance between GPP and Re on regional to global scales [11,12,13], because small fluctuations in either component caused by natural or human disturbances can ameliorate or exacerbate the buildup of CO2 in the atmosphere [14,15,16]. Particularly, Re is sensitive to the environmental factors and is highly spatio-temporally heterogeneous across scales [17, 18], which made it far poorly understood owing to the complicated interactions among physical, chemical, and biological variables in the respiration processes, including autotrophic respiration (Ra) from vegetation itself and heterotrophic respiration (Rh) from diverse soil microbiota [19]. Therefore, accurate quantification of the C emissions through Re is crucial to understand its effect on climate change and global carbon dynamics.

With the development of global FLUXNET community based the eddy covariance (EC) technique across terrestrial ecosystems, it has been possible to continuously monitor the seasonal and interannual variations of carbon fluxes between the biosphere and the atmosphere, which can be further divided into GPP and Re according to the nighttime based [20] and daytime based flux-partitioning methods [21]. However, the in situ observations are generally implemented at field scale with low areal coverage (< 1 km2) and high cost of constructing and maintaining flux towers [22,23,24]. The availability of spatially continuous data of ecosystem properties and environmental variables important for Re provides an alternative approach for the large-area estimates. Currently, how to upscale the field-measured data using remote sensing (RS) information is urgently needed for understanding regional and global patterns of ecosystem Re.

Several studies have recently been conducted to model the spatial distribution of soil respiration (Rs) at alpine grasslands [25] and forests [26, 27] using the satellite-based products including land surface temperature (LST) and spectral vegetation index (NDVI or EVI) or leaf area index (LAI). By incorporating the terrain information, Berryman et al. [28] estimated Rs in a typical of the Southern Rocky Mountains with a coefficient of determination (R2) of 0.45. Jägermeyr et al. [29] firstly developed the models of global Re according to forested and non-forested biomes. However, the classification system means that parameterization may not take into account the wide variety of ecosystems on the earth [31]. Ai et al. [17] also proposed an empirical yet physiologically based model with R2 and RMSE of 0.55 and 1.67 g C m−2 d−1 respectively, which is capable of retrieving the patterns in Re at the global scale. But the model cannot be transferred well to specific ecosystems, and may particularly be inaccurate in hydrologically sensitive areas owing to lack of water index affecting Re. Nevertheless, previous studies have implied the significant correlations between grassland respiration and vegetation growth status [25], as well as the environmental variables such as LST [15, 17, 30] from the time-series satellite data.

Grasslands are the dominant landscape in China and account for 40% of the national land area. Geographically, approximately 78% of the grasslands in China exist in the northern temperate and alpine zones, constituting an integral part of the Eurasian grassland ecosystem [32, 33]. Due to their large carbon content, grasslands account for one-third of the global terrestrial carbon stock, second only to the forest ecosystem [34, 35], and play a key role in China’s terrestrial carbon cycle. Zhang et al. [5] found that temperate grasslands in northern China have the potential to sequester carbon, but the capacity of carbon sequestration relies on grassland types and local environmental conditions. Further analysis revealed that the water availability is the dominant environmental factor regulating the annual carbon budget [36]. Extreme climate events such as drought can significantly reduce the net carbon uptake of grasslands. Moreover, it is predicted that heat waves and droughts will become more frequent in the 21st century [37], which may lead to a general decrease in vegetation productivity in these grassland systems of northern China. Although several studies suggested that grasslands might be weak C sinks or near equilibrium [5, 38], as the main constraint on C budget, a deep understanding of Re is helpful to project climate change-terrestrial C feedback over different grassland ecosystems. Specially, the goals of this study were: (1) to analyze the differences of carbon released by Re as well as the dominant environmental variables across the temperate meadow steppe, typical steppe, desert steppe and alpine meadow, respectively; (2) to develop rule-based piecewise regression models to map Re of diverse grasslands by integrating time-series MODIS products and tower-based observations; and (3) to map the spatial patterns of annual mean Re for the grassland ecosystems in northern China.

Methods and materials

Description of the study area

The study was conducted in the grassland ecosystems of northern China, which is characterized by the arid and semi-arid continental monsoon climate with the highest temperature and rainfall period in summer. Following the east-to-west precipitation gradient, the temperate grasslands in northern China alter longitudinally from meadow steppes in the northeast, through typical steppes in the middle, and to desert steppes in the dry northwest. Alpine meadows are the dominant vegetation type in the Tibetan Plateau zone. These grasslands usually start to grow in early May and wither in late September with the peak biomass in July or August, which provide an important resource for livestock production and global carbon sequestration [5, 33].

During recent years, a series of EC-based flux towers have been installed by the Coordinated Observation and Synthesis in Arid and Semi-arid China (COSAS), as a part of China Flux Observation and Research Network (ChinaFLUX), which can be used to observe the carbon and water exchanges between the atmosphere and these grasslands in the ecologically fragile areas of northern China (Fig. 1). In total of 11 flux sites are used in the study, which represent the most prevalent types of grassland ecosystems and a wide range of spatial, ecological, and climatic conditions, including two meadow steppe sites, two typical steppe sites, two desert steppe sites and five alpine meadow sites. Detailed descriptions of these flux sites can be found in the associated literature (Table 1).

Fig. 1
figure 1

Spatial location and distribution of the grassland flux sites used in the present study. The base map is derived from MODIS land cover product (MCD12Q1 C5, 500 m resolution) based on the International Geosphere Biosphere Programme (IGBP) global vegetation classification scheme in 2010. The detailed descriptions of these flux sites including CN-Cng, CN-Nmg, CN-Du2, CN-Hzz, CN-Sw2, CN-Ham, CN-Ar, CN-Ds, CN-Dan, CN-Ty and CN-Mq can be seen in Table 1

Table 1 Characteristics of the flux sites over grassland ecosystems in northern China

Processing of the EC-based flux data

Both the EC system and the automatic meteorological station were mounted at these grassland sites, which acquired the continuous observations of site-level carbon fluxes (NEE), as well as the relevant climate data, including solar radiation (Rg), air and soil temperatures (Ta and Ts), relative humidity, soil water content (SWC), precipitation (P) and vapor pressure deficit (VPD). Each EC system was comprised of a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, UT, USA) and a Li-7500 open path CO2 and H2O gas analyzer (LI-COR Inc., NE, USA). Raw data were continuously recorded at a frequency of 10 Hz on a CR5000 (Campbell Scientific) data logger. The processing procedures including spike detection and despiking, two-dimensional coordinate rotation, time delay removal of H2O and CO2, virtual temperature correction, density effects (WPL correction) and frequency response corrections were completed using the improved EdiRe software package (developed by the University of Edinburgh) to produce a half-hour flux dataset [39]. However, owing to instrument malfunctions, power failure, and severe weather conditions, approximately 25% of the 1-year observations were lost. Therefore, it was necessary to interpolate these gaps with a standardized gap-filling algorithm. Then, the time-series NEE flux data were partitioned into GPP and Re, separately. In this study, the half-hourly Re data provided by flux-tower measurements were integrated to the daily time scale, and then averaged over each 8-day period to match the 8-day composite of the MODIS products. The procedures including gap-filling and flux partitioning, were completed using the new R-based package (REddyProc) maintained by the Max Planck Institute for Biogeochemistry [40].

Currently, there are mainly two methods implemented for flux-partitioning: (1) Re is estimated from the nighttime temperature and extrapolated to daytime [20] and (2) the light–response curve is fit to daytime NEE measurements and Re is estimated from the intercept of the ordinate [21], which can avoid the use of potentially problematic nighttime data. The latter approach was chosen for flux partitioning because it uses a hyperbolic light-response curve algorithm, modified to account for the temperature dependency of respiration and the VPD limitation of photosynthesis [41]. Including the VPD dependency strongly improved the model’s ability to reproduce the asymmetric diurnal cycle during periods with high VPD, and enhances the reliability of Re estimates given that the reduction of GPP by VPD may be otherwise incorrectly attributed to higher Re. More details can be seen in the associated references.

MODIS products and processing

Re is generally comprised of two sources of respiration: Ra from maintenance respiration and growth respiration, and Rh from rhizomicrobial respiration and microbial decomposition of plant residues and other soil organic matter [42, 31]. Thus, it is strongly affected by plant growth status and climate conditions. This study used the enhanced vegetation index (EVI) and leaf area index (LAI), as well as the land surface water index (LSWI), and mean value of daytime and nighttime temperatures (LST) to represent the vegetation and climate-related variables.

All variables were derived from the time-series MODIS data, which can avoid the complications and difficulties associated with merging disparate data sources. The 8-day land surface reflectance (MOD09A1, V6, with resolution of 500 m), LAI product (MOD15A2, V6, with resolution of 500 m) and LST data (MOD11A2, V6, with resolution of 1 km) were downloaded from the NASA’s Earth Observing System Data and Information System ( We only used the data described as good quality in the quality layer. These remote sensing-based products were re-sampled to a spatial resolution of 1 km, and the data where corresponded to the geographical location of each flux site were extracted for model development. In addition, the vegetation type map at 1 km resolution in 2010 was obtained from Nanjing Institute of Geography and Limnology, Chinese Academy of Science. The product specifically classified grasslands in China into temperate meadow steppe, typical steppe, desert steppe and alpine meadow, which can meet the needs to estimate Re across different grassland types in this study. Then, all these data were used to develop models and map the spatio-temporal patterns of Re in grassland ecosystems in northern China.

Statistical analyses

To reveal the dominant environmental factors controlling the variability in Re on an 8-day time scale over the whole year and different phenological periods (growing season vs dormant season) at these four different grassland ecosystems, the Pearson correlation coefficient (r) was calculated to examine the relationships between site-level Re observations and these vegetation and climate-related variables. As these grasslands usually start to grow in early May and wither in late September, the growing period and dormant period were defined from WOY 16 to 34 and the rest of 1 year (WOY 1 to 15, and WOY 35 to 46), respectively. Then, we aimed to develop a rule-based piecewise regression model to capture the seasonal variations in Re of temperate meadow steppe, typical steppe, desert steppe and alpine meadow in northern China. For each grassland type, the training set and test set of these flux data were instructed in Table 1. The study defined the model developed directly from the entire year as model 1, and the model from the growth period and dormant season as model 2. The model’s accuracy was evaluated using two widely-used indicators: R2 and the root-mean-square error (RMSE). The best model generally had the highest R2 and lowest RMSE values. Finally, the optimal models were used to map the spatial patterns of annual mean Re for the grasslands in northern China. All statistical analyses were performed using SPSS 19.0 (IBM, Chicago, IL, USA). In addition, the MODIS Reprojection Tool (MRT) and the Interactive Data Language (IDL) in ENVI 5.3 were used to process over 2000 scenes of MODIS data and large-area estimation.


Differences of annual mean Re across grasslands

Multi-year mean Re of the temperate meadow steppe, typical steppe, desert steppe and alpine meadow ecosystems in northern China were exhibited in Fig. 2 with apparent differences in magnitude. It revealed that C emissions by Re were mainly concentrated in the growing season, which was even about twice and seven times of Re during the dormant period for desert steppe and the other grasslands, respectively. The low Re of desert steppe throughout the year can be ascribed to relatively sparse vegetation coverage. This study also found that during non-growing season, only small differences in Re existed among these grassland types. Generally, typical steppe exhibited the strongest Re of 1.08 ± 0.23 g C m−2 d−1, followed by temperate meadow steppe (1.03 ± 0.10 g C m−2 d−1) and alpine meadow (1.05 ± 0.48 g C m−2 d−1), and undoubtedly desert steppe had the weakest Re. These analyses again emphasized the importance to quantify the patterns of grassland Re separately for large-area estimation.

Fig. 2
figure 2

Comparisons of multi-year mean Re during the growing period, dormant period and the entire year across grasslands in northern China. Error bars represent one standard error

Seasonal variations in Re and environmental controls

Seasonal dynamics in Re as well as the vegetation and climate-related variables across the four grassland ecosystems were illustrated in Figs. 3 and 4. It implied that except the desert steppe site (CN-Hzz), Re of the other three grassland types exhibited an apparent single-peak pattern. As the temperature rose in spring, Re gradually increased with plant growth, and reached the peak in July or August. However, the peak periods came earlier in typical steppe (CN-Nmg) at about week of year–WOY 23, followed by temperate meadow steppe (CN-Cng, WOY 25) and alpine meadow (CN-Ham, WOY 30). Natural rainfall generally occurred in summer with large fluctuations around the year. Contrastingly, relatively smooth SWC can reflect the true water availability. As a proxy of vegetation response to environmental variables, EVI and LAI exhibited consistent trends as Re. Several periods of the remote-derived LSWI in winter were quite large due to the snow cover on and under the grasslands.

Fig. 3
figure 3

Seasonal trends of ad ecosystem respiration (Re), eh air temperature (Ta) and soil temperature (Ts), il solar radiation (Rg) and vapor pressure deficit (VPD), mp precipitation (P) and soil water content (SWC) in an 8-day interval at the representative grassland flux sites for selected years including CN-Cng 2008 (Temperate meadow steppe), CN-Nmg 2004 (Typical steppe), CN-Hzz 2014 (Desert steppe) and CN-Ham 2003 (Alpine meadow)

Fig. 4
figure 4

The seasonal dynamics of 8-day EVI, LAI, LSWI and LST at the representative grassland flux sites for selected years including CN-Cng 2008 (Temperate meadow steppe), CN-Nmg 2004 (Typical steppe), CN-Hzz 2014 (Desert steppe) and CN-Ham 2003 (Alpine meadow)

Table 2 revealed that almost all variables were strongly correlated to Re without considering the phenology information. However, during the dominant growth period for respiration in temperate meadow steppe, typical steppe and alpine meadow, only temperature (Ta and Ts) and SWC strongly and positively affected the variability in Re. In the desert steppe, Re was found to be exerted a strong effect by SWC across growing and dormant seasons, which highlighted the water condition rather than temperature as the most important controlling factor in the extremely dry ecosystems. All the remotely-sensed vegetation indexes and climate-related LST and LSWI exhibited strong correlations, implying great potential to quantify the variability in Re using RS technique.

Table 2 Pearson correlation analysis between 8-day Re and the controlling environmental factors across the four grassland ecosystem types

Model development for quantitative remote sensing

By integrating site-level Re observations and time-series MODIS products, the study developed the optimal models for capturing the variability in Re of different grasslands using the stepwise multiple regression method (Table 3). Mainly two models were proposed with good performances. Generally, the model 2 established from the growth period and dormant season separately had relatively higher accuracy in estimating Re than that directly from the whole year (model 1). Besides the desert steppe, R2 and RMSE of the other grasslands varied from 0.77 to 0.88 and from 0.41 to 0.67 g C m−2 d−1, respectively. The model with only LAI as an explanatory variable also got good estimates for the desert steppe. Figure 5 further certified that the model promised well to estimate 8-day Re and captured the broad trend of seasonal patterns, especially for the Re_model 2, whereas the Re_model 1 caused individual abnormal Re estimates in wintertime. In spite of good accuracy, the RS-based models remained lacking of skills to capture abrupt changes in Re during the summertime growing season.

Table 3 Regression analysis of 8-day Re and the associated MODIS-derived products across the four grassland ecosystems using the stepwise multiple regression method
Fig. 5
figure 5

Performance of the remotely-derived Re (Re_model1 and Re_model2) in capturing seasonal variations of tower-based Re (Re_observed) at the temperate meadow steppe (a), typical steppe (b), desert steppe (c) and alpine meadow (d) sites, respectively

Meanwhile, this study evaluated the model’s performance on annual mean Re across these four grasslands. Figure 6 revealed that the remotely-derived products slightly underestimated Re at the temperate meadow steppe and desert steppe sites, but overestimated Re at the typical steppe and alpine meadow sites. The Re_model1 had superior Re estimates with mean bias of 9.6% at the typical steppe site, while Re_model2 only had an underestimation of 6.2% at the temperate meadow steppe site. Both models had similar accuracy at the desert steppe and alpine meadow sites.

Fig. 6
figure 6

Comparisons of annual mean Re observations and the remotely-derived products across the temperate meadow steppe, typical steppe, desert steppe and alpine meadow sites

Spatial patterns of satellite-derived Re

The study mapped the spatial distribution of annual mean Re for grasslands in northern China using the proposed regression models (Fig. 7), which were extrapolated from the 8-day Re estimates throughout the year of 2010. Generally, our results showed a declining trend from the southeast to the northwest direction. The highest Re usually occurred in the temperate meadow steppe under good hydrothermal conditions. However, the desert steppe and the Tibetan alpine meadow emitted relatively fewer CO2 through respiration. The total regional Re estimate in the northern China’s grasslands during 2010 was about 100.66 Tg C, which was approximately 1/3 of the carbon uptake through plant photosynthesis with 332.08 Tg C from the MODIS GPP product. Specially, the ratio of Re to GPP exhibited the highest value in the desert steppe (88.6%), followed by the temperate meadow steppe (36.9%) and typical steppe (21.5%), with the lowest ratio in the alpine meadow of 18.0%.

Fig. 7
figure 7

Spatial patterns of grassland types (a) and annual mean Re (b) over the northern China’s grasslands in 2010. The unit of Re is g C m−2 d−1


Along the east–west precipitation gradient in northern China, a wide variety of grasslands constitute the dominant landscape in servicing the ecological environment and socio-economics of the region and in supporting diverse species of plants and animals [32]. The meadow steppes usually occur in the most moist and fertile sites among the four grassland ecosystem types, typically in areas with annual precipitation of about 450 mm. The typical steppes are developed under a semi-arid climate in the temperate zone with annual precipitation of around 350 mm and the plant species are characteristically drought-tolerant. The desert steppes are the most arid grassland type, occurring in areas with annual precipitation between 150 and 250 mm under the influence of continental climate. Alpine steppes are found between 2300 and 5300 m a.s.l. in southwestern China. The common construction plant species are cold- and drought-tolerant grasses and small shrubs. Arid and semiarid ecosystems are often overlooked in this regard because of their spatially sparse vegetation and fragile environmental conditions [43, 44]. In fact, the grassland ecosystems are not only economically important, but recent work has shown water-limited systems to drive the interannual variability in the global C cycle significantly [45, 46]. Therefore, a deep understanding of the C sequestration potential as well as environmental controls over the northern China’s grasslands is vital for reducing the uncertainty in future climate projections given the large area of grasslands.

Accurate quantification of the main C fluxes across terrestrial ecosystems is crucial to our understanding of global carbon balance [23, 47]. Till now, there are fewer successful models of Re compared to GPP particularly using the RS data directly [29, 48]. Reichstein et al. [49] found that soil water and temperature were good predictors for Rs, and adding LAI as a proxy for productivity further improved the accuracy. This study was based on closed-chamber data from forest and shrubland sites across Europe and North America, but it was suggested that the variables could easily be acquired from RS data. Anderson et al. [50] used a model which calculated soil moisture from microwave sensing, soil temperature from thermal imaging, and LAI, showed good agreement with tower flux data over pasture land in Oklahoma. However, model development over such a small area is unlikely to generate a model which is applicable to other ecosystems or climates, and more validation work is also needed. In spite of this, subsequent studies have suggested that the strong relationships between Rs and Re and GPP [26], LST [17, 25, 48] from the time-series RS data.

Changes in temperature and water availability, and thus climate change, are likely to be significant drivers of the future C balance of land systems and their feedbacks to climate change. Grasslands in the arid and semi-arid regions are ecologically fragile and sensitive to climate change and human disturbances, especially to the changes in precipitation [51]. The precipitation in arid and semi-arid regions is highly variable both temporally and spatially. Fluctuations in carbon budget have been found to be closely associated with interannual and intra-annual variations in precipitation in arid and semi-arid ecosystems, and persistent drought has caused a general decrease in vegetation productivity in the grassland systems of northern China [5]. These work also demonstrated that water condition is an important environmental indicator for the estimates of Re in the grassland ecosystems. Soil moisture affects Re processes in various ways, including the growth and development of both aboveground vegetation and roots, growth and activity of microbial populations, in addition to gas transport throughout soils [52, 53]. Green et al. [2] also emphasize that the capacity of continents to act as a future carbon sink critically depends on the response of C fluxes to soil moisture. The study used the LSWI derived from the shortwave infrared (SWIR) and the near infrared (NIR) bands of MODIS data to represent land surface water condition, and got reasonably good performance. Actually, LSWI is different from soil moisture (SWC) because most information of LSWI reflects the total amount of liquid water in vegetation [54]. An interesting avenue of future work would be to consider employing the soil moisture data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSE-E) [55]. The problem with many of these studies, however, is that they are too narrow for comparison. They consider one particular site in one particular ecosystem, or attempt to create a global model often focus on a narrow range of only four or five ecosystem types [17, 29]. Grasslands and their huge variety of types are almost never included as a separate category in remote sensing models of carbon flux, and as such are certain to be over or under-estimated. Ge et al. [30] proposed similar models to evaluate grassland Re at a regional scale by integrating flux measurements and the corresponding MODIS products. But this study simplified the grasslands in northern China by alpine grasslands and temperate grasslands. In fact, large differences existed among the temperate meadow steppe, typical steppe and desert steppe when exploring the dominant environmental controls and developing associated models. Meanwhile, the variability in Re rely on grassland types and local environmental conditions. Therefore, our research on remotely monitoring Re from various grasslands along a large-scale east–west transect across northern China can provide more information than previous studies.

Our results suggested that simple models relying entirely on spatial data have the potential to estimate Re over the diverse grassland ecosystems. This result provides a framework for the development of Re models aimed to obtain spatial pattern in Re. Meanwhile, the models incorporating the phenology information generally provided better estimations. In this study, we simply separated the growing season and dormant season as these grasslands usually start to grow in early May and wither in late September. To acquire more specific phenological information of each pixel, the global land surface phenology metrics at yearly intervals (MCD12Q2) can be used for auxiliary analysis. But the models would be complicated for large-scale extrapolation owing to strong spatial heterogeneity. In addition, there is a need for more long-term studies in order to monitor the interannual variations of Re as well as the underlying mechanisms. The study calculated the spatial distribution of grassland Re in 2010 because we only have a period of classification data with detailed grassland types. The total of carbon emissions of Re across the northern China’s grasslands was accumulated to 100.66 Tg C during 2010, which was approximately 1/3 of the regional carbon uptake through plant photosynthesis with 332.08 Tg C from the MODIS GPP product. Particularly, the carbon use efficiency was the highest in the alpine meadow, followed by the typical steppe, meadow steppe and desert steppe. However, this approach generally need large amounts of Re data to constrain parameters. The regression coefficients obtained at this study would not work well for other grassland sites with different climate, soil, and vegetation. Although we tried to establish the robust models for the temperate meadow steppe, typical steppe, desert steppe and alpine meadow using 11 flux sites, the representativeness of these limited sites presumably affected the general applicability of our predictive model. Thus, the current Coordinated Observation and Synthesis in Arid and Semi-arid China should be augmented by building more sites across a full range of grassland types.


On the basis of 24 EC-based years of flux measurements over 11 grasslands sites under a wide range of geographic, weather and ecological conditions, the study was an attempt to upscale site-level Re data to the northern China’s grasslands including temperate meadow steppe, meadow steppe, desert steppe and alpine meadow, using the satellite-based RS data. The results demonstrated that the rule-based piecewise regression models can successfully estimate the seasonal variations in Re and provide a new framework to map the regional patterns of large-scale Re. The reduction of uncertainties in Re is crucial for projecting climate change impacts on terrestrial carbon cycling and future atmospheric CO2 concentrations. This work also offers an opportunity to further understand the environmental drivers controlling the variability in Re. Specially, not only temperate but also soil water content, had a strong correlation with grassland Re, which should not be neglected when developing the RS models for arid and semiarid ecosystems. Meanwhile, the models incorporating the phenology information generally performed better. The spatial patterns of Re across the northern China’s grasslands exhibited a distinctly declining trend from the southeast to the northwest, with a regional estimate of approximately 100.66 Tg C during 2010. With more periods of grassland classification information, future studies can even evaluate long-term land-use change and its impact on the large-scale Re of grasslands.

Availability of data and materials

The data used in this article are available upon request.


R e :

Ecosystem respiration


Gross primary production


Moderate-resolution Imaging Spectroradiometer

R a :

Autotrophic respiration

R h :

Heterotrophic respiration


Eddy covariance


remote sensing

R s :

Soil respiration


Land surface temperature


Normalized difference vegetation index


Enhanced vegetation index


Leaf area index

R 2 :

Coefficient of determination


Root-mean-square error

r :

Correlation coefficient


China Flux Observation and Research Network


Net ecosystem carbon flux

Rg :

Solar radiation

Ta :

Air temperature

Ts :

Soil temperature


Soil water content




Vapor pressure deficit


Land surface water index


  1. Ballantyne AP, Andres R, Houghton R, Stocker BD, Wanninkhof R, Anderegg W, Cooper LA, DeGrandpre M, Tans PP, Miller JB, Alden C, White JWC. Audit of the global carbon budget: estimate errors and their impact on uptake uncertainty. Biogeosciences. 2015;12:2565–84.

    Article  Google Scholar 

  2. Green JK, Seneviratne SI, Berg AM, Findell KL, Hagemann S, Lawrence DM, Gentine P. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature. 2019;565:476–9.

    CAS  Article  Google Scholar 

  3. Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H. A continuous satellite-derived measure of global terrestrial primary production. Bioscience. 2004;54:547–60.

    Article  Google Scholar 

  4. Wu C, Chen JM, Huang N. Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: evaluation and calibration. Remote Sens Environ. 2011;115:3424–35.

    Article  Google Scholar 

  5. Zhang L, Guo H, Jia G, Wylie B, Gilmanov T, Howard D, Ji L, Xiao J, Li J, Yuan W, Zhao T, Chen S, Zhou G, Kato T. Net ecosystem productivity of temperate grasslands in northern China: an upscaling study. Agric For Meteorol. 2014;184:71–81.

    Article  Google Scholar 

  6. Zhao M, Heinsch FA, Nemani RR, Running SW. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens Environ. 2005;95:164–76.

    Article  Google Scholar 

  7. Chapin FS, Woodwell GM, Randerson JT, Rastetter EB, Lovett GM, Baldocchi DD, Clark DA, Harmon ME, Schimel DS, Valentini R, Wirth C, Aber JD, Cole JJ, Goulden ML, Harden JW, Heimann M, Howarth RW, Matson PA, McGuire AD, Melillo JM, Mooney HA, Neff JC, Houghton RA, Pace ML, Ryan MG, Running SW, Sala OE, Schlesinger WH, Schulze ED. Reconciling carbon-cycle concepts, terminology, and methods. Ecosystems. 2006;9(7):1041–50.

    CAS  Article  Google Scholar 

  8. Schimel DS, House JI, Hibbard KA, Bousquet P, Ciais P, Peylin P, Braswell BH, Apps MJ, Baker D, Bondeau A, Canadell J, Churkina G, Cramer W, Denning AS, Field CB, Friedlingstein P, Goodale C, Heimann M, Houghton RA, Melillo JM, Moore B, Murdiyarso D, Noble I, Pacala SW, Prentice IC, Raupach MR, Rayner PJ, Scholes RJ, Steffen WL, Wirth C. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature. 2001;414(6860):169.

    CAS  Article  Google Scholar 

  9. Messerschmidt J, Parazoo N, Wunch D, Deutscher NM, Roehl C, Warneke T, Wennberg PO. Evaluation of seasonal atmosphere-biosphere exchange estimations with TCCON measurements. Atmos Chem Phys. 2013;13(10):5103–15.

    Article  CAS  Google Scholar 

  10. Milyukova IM, Kolle O, Varlagin AV, Vygodskaya NN, Schulze ED, Lloyd J. Carbon balance of a southern taiga spruce stand in European Russia. Tellus Ser B Chem Phys Meteorol. 2002;54(5):429–42.

    Article  Google Scholar 

  11. Byrne B, Wunch D, Jones DBA, Strong K, Deng F, Baker I, Köhler P, Frankenberg C, Joiner J, Arora VK, Badawy B, Harper AB, Warneke T, Petri C, Kivi R, Roehl CM. Evaluating GPP and respiration estimates over northern midlatitude ecosystems using solar-induced fluorescence and atmospheric CO2 measurements. J Geophys Res Biogeosci. 2018;123:2976–97.

    CAS  Article  Google Scholar 

  12. Peylin P, Law RM, Gurney KR, Chevallier F, Jacobson AR, Maki T, Niwa Y, Patra PK, Peters W, Rayner PJ, Rödenbeck C, Van Der Laan-Luijkx IT, Zhang X. Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions. Biogeosciences. 2013;10:6699–720.

    CAS  Article  Google Scholar 

  13. Tian H. Regional carbon dynamics in monsoon Asia and its implications for the global carbon cycle. Glob Planet Change. 2003;37:201–17.

    Google Scholar 

  14. Tang X, Li H, Ma M, Yao L, Peichl M, Arain A, Xu X, Goulden M. How do disturbances and climate effects on carbon and water fluxes differ between multi-aged and even-aged coniferous forests? Sci Total Environ. 2017;599:1583–97.

    Article  CAS  Google Scholar 

  15. Wohlfahrt G, Anderson-Dunn M, Bahn M, Balzarolo M, Berninger F, Campbell C, Carrara A, Cescatti A, Christensen T, Dore S, Eugster W, Friborg T, Furger M, Gianelle D, Gimeno C, Hargreaves K, Hari P, Haslwanter A, Johansson T, Marcolla B, Milford C, Nagy Z, Nemitz E, Rogiers N, Sanz MJ, Siegwolf RTW, Susiluoto S, Sutton M, Tuba Z, Ugolini F, Valentini R, Zorer R, Cernusca A. Biotic, abiotic, and management controls on the net ecosystem CO2 exchange of European mountain grassland ecosystems. Ecosystems. 2008;11:1338–51.

    CAS  Article  Google Scholar 

  16. Zheng C, Tang X, Gu Q, Wang T, Wei J, Song L, Ma M. Climatic anomaly and its impact on vegetation phenology, carbon sequestration and water-use efficiency at a humid temperate forest. J Hydrol. 2018;565:150–9.

    CAS  Article  Google Scholar 

  17. Ai J, Jia G, Epstein HE, Wang H, Zhang A, Hu Y. MODIS-based estimates of global terrestrial ecosystem respiration. J Geophys Res Biogeosci. 2018;123:326–52.

    Article  Google Scholar 

  18. Zhang Q, Lei HM, Yang DW. Seasonal variations in soil respiration, heterotrophic respiration and autotrophic respiration of a wheat and maize rotation cropland in the North China Plain. Agric For Meteorol. 2013;180:34–43.

    CAS  Article  Google Scholar 

  19. Hinko-najera N, Fest B, Livesley SJ, Arndt SK. Agricultural and Forest Meteorology Reduced throughfall decreases autotrophic respiration, but not heterotrophic respiration in a dry temperate broadleaved evergreen forest. Agric For Meteorol. 2015;200:66–77.

    Article  Google Scholar 

  20. Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival JM, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D, Valentini R. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob Change Biol. 2005;11:1424–39.

    Article  Google Scholar 

  21. Lasslop G, Reichstein M, Papale D, Richardson A, Arneth A, Barr A, Stoy P, Wohlfahrt G. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob Change Biol. 2010;16:187–208.

    Article  Google Scholar 

  22. Gu Q, Wei J, Luo S, Ma M, Tang X. Potential and environmental control of carbon sequestration in major ecosystems across arid and semi-arid regions in China. Sci Total Environ. 2018;645:796–805.

    CAS  Article  Google Scholar 

  23. Tang X, Wang Z, Liu D, Song K, Jia M, Dong Z, Munger JW, Hollinger DY, Bolstad PV, Goldstein AH, Desai AR, Dragoni D, Liu X. Estimating the net ecosystem exchange for the major forests in the northern United States by integrating MODIS and AmeriFlux data. Agric For Meteorol. 2012;156:75–84.

    Article  Google Scholar 

  24. Xiao J, Zhuang Q, Baldocchi DD, Law BE, Richardson AD, Chen J, Oren R, Starr G, Noormets A, Ma S, Verma SB, Wharton S, Wofsy SC, Bolstad PV, Burns SP, Cook DR, Curtis PS, Drake BG, Falk M, Fischer ML, Foster DR, Gu L, Hadley JL, Hollinger DY, Katul GG, Litvak M, Martin TA, Matamala R, McNulty S, Meyers TP, Monson RK, Munger JW, Oechel WC, Paw UKT, Schmid HP, Scott RL, Sun G, Suyker AE, Torn MS. Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data. Agric For Meteorol. 2008;148:1827–47.

    Article  Google Scholar 

  25. Huang N, He JS, Niu Z. Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data. Ecol Indic. 2013;26:117–25.

    Article  Google Scholar 

  26. Huang N, Gu L, Niu Z. Estimating soil respiration using spatial data products: a case study in a deciduous broadleaf forest in the Midwest USA. J Geophys Res. 2014;119:6393–408.

    Google Scholar 

  27. Wu C, Gaumont-Guay D, Andrew Black T, Jassal RS, Xu S, Chen JM, Gonsamo A. Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan, Canada. ISPRS J Photogramm Remote Sens. 2014;94:80–90.

    Article  Google Scholar 

  28. Berryman E, Vanderhoof MK, Bradford JB, et al. Estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data. J Geophys Res. 2018;123(10):3231–49.

    Article  Google Scholar 

  29. Jägermeyr J, Gerten D, Lucht W, Hostert P, Migliavacca M, Nemani R. A high-resolution approach to estimating ecosystem respiration at continental scales using operational satellite data. Glob Change Biol. 2014;20(4):1191–210.

    Article  Google Scholar 

  30. Ge R, He HL, Ren XL, Zhang L, Li P, Zeng N, Yu GR, Zhang LY, Yu SR, Zhang FW, Li HQ, Shi PL, Chen SP, Wang YF, Xin XP, Ma YM, Ma MG, Zhang Y, Du MY. A satellite-based model for simulating ecosystem respiration in the Tibetan and Inner Mongolian grasslands. Remote Sens. 2018;10(1):149.

    Article  Google Scholar 

  31. Gao Y, Yu G, Li S, et al. A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau. Ecol Model. 2015;304:34–43.

    CAS  Article  Google Scholar 

  32. Kang L, Han X, Zhang Z, Sun OJ. Grassland ecosystems in China: review of current knowledge and research advancement. Philos Trans R Soc B Biol Sci. 2007;362:997–1008.

    Article  Google Scholar 

  33. Wei J, Chen Y, Gu Q, Jiang C, Ma M, Song L, Tang X. Potential of the remotely-derived products in monitoring ecosystem water use efficiency across grasslands in Northern China. Int J Remote Sens. 2019;40:6203–23.

    Article  Google Scholar 

  34. Schuman G, Janzen H, Herrick J. Soil carbon dynamics and potential carbon sequestration by rangelands. Environ Pollut. 2002;116(3):391–6.

    CAS  Article  Google Scholar 

  35. Zhang L, Zhou G, Ji Y, Bai Y. Spatiotemporal dynamic simulation of grassland carbon storage in China. Sci China Earth Sci. 2016;59(10):1946–58.

    CAS  Article  Google Scholar 

  36. Chen Y, Gu H, Wang M, Gu Q, Ding Z, Ma M, Liu R, Tang X. Contrasting performance of the remotely-derived GPP products over different climate zones across China. Remote Sens. 2019;11(16):1855.

    Article  Google Scholar 

  37. Hua T, Wang X, Zhang C, Lang L, Li H. Responses of vegetation activity to drought in northern China. Land Degrad Dev. 2017;28(7):1913–21.

    Article  Google Scholar 

  38. Gilmanov TG, Svejcar TJ, Johnson DA, Angell RF, Saliendra NZ, Wylie BK. Long-term dynamics of production, respiration, and net CO2 exchange in two sagebrush-steppe ecosystems. Rangel Ecol Manag. 2006;59:585–99.

    Article  Google Scholar 

  39. Mauder M, Foken T, Clement R, Elbers JA, Eugster W, Grünwald T, Heusinkveld B, Kolle O. Quality control of CarboEurope flux data—Part 2: inter-comparison of eddy-covariance software. Biogeosciences. 2008;5:451–62.

    CAS  Article  Google Scholar 

  40. Wutzler T, Lucas-Moffat A, Migliavacca M, Knauer J, Sickel K, Šigut L, Menzer O, Reichstein M. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences. 2018;15:5015–30.

    CAS  Article  Google Scholar 

  41. Gilmanov TG, Johnson DA, Saliendra NZ. Growing season CO2 fluxes in a sagebrush-steppe ecosystem in Idaho: bowen ratio/energy balance measurements and modeling. Basic Appl Ecol. 2003;4:167–83.

    Article  Google Scholar 

  42. Hibbard KA, Law BE, Reichstein M, Sulzman J. An analysis of soil respiration across northern hemisphere temperate ecosystems. Biogeochemistry. 2005;73(1):29–70.

    Article  Google Scholar 

  43. Lioubimtseva E, Henebry GM. Climate and environmental change in arid Central Asia: Impacts, vulnerability, and adaptations. J Arid Environ. 2009;73(11):963–77.

    Article  Google Scholar 

  44. Ahlström A, Raupach MR, Schurgers G, et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science. 2015;348(6237):895–99.

    Article  CAS  Google Scholar 

  45. Poulter B, Frank D, Ciais P, Myneni RB, Andela N, Bi J, Broquet G, Canadell JG, Chevallier F, Liu Y, Running SW, Sitch S, Van der Werf GR. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature. 2014;509:600–3.

    CAS  Article  Google Scholar 

  46. Soussana J-F, Loiseau P, Vuichard N, Ceschia E, Balesdent J, Chevallier T, Arrouays D. Carbon cycling and sequestration opportunities in temperate grasslands. Soil Use Manag. 2004;20:219–30.

    Article  Google Scholar 

  47. Baldocchi D. Measuring fluxes of trace gases and energy between ecosystems and the atmosphere—the state and future of the eddy covariance method. Glob Change Biol. 2014;20:3600–9.

    Article  Google Scholar 

  48. Olofsson P, Lagergren F, Lindroth A, Lindström J, Klemedtsson L, Kutsch W, Eklundh L. Towards operational remote sensing of forest carbon balance across Northern Europe. Biogeosciences. 2008;5:817–32.

    Article  Google Scholar 

  49. Reichstein M, Rey A, Freibauer A, Tenhunen J, Valentini R, Banza J, Casals P, Cheng Y, Grünzweig JM, Irvine J, Joffre R, Law BE, Loustau D, Miglietta F, Oechel W, Ourcival J-M, Pereira JS, Peressotti A, Ponti F, Qi Y, Rambal S, Rayment M, Romanya J, Rossi F, Tedeschi V, Tirone G, Xu M, Yakir D. Modeling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices. Glob Biogeochem Cycles. 2003;17:4.

    Article  CAS  Google Scholar 

  50. Anderson MC, Norman JM, Kustas WP, Houborg R, Starks PJ, Agam N. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sens Environ. 2008;112:4227–41.

    Article  Google Scholar 

  51. Hu Z, Yu G, Fan J, Zhong H, Wang S, Li S. Precipitation-use efficiency along a 4500-km grassland transect. Global Ecol Biogeogr. 2010;19(6):842–51.

    Article  Google Scholar 

  52. Moyano FE, Manzoni S, Chenu C. Responses of soil heterotrophic respiration to moisture availability: an exploration of processes and models. Soil Biol Biochem. 2013;59:72–85.

    CAS  Article  Google Scholar 

  53. Yan Z, Bond-Lamberty B, Todd-Brown KE, Bailey VL, Li S, Liu C, Liu C. A moisture function of soil heterotrophic respiration that incorporates microscale processes. Nat Commun. 2018;9:2562.

    Article  CAS  Google Scholar 

  54. Chandrasekar K, Sesha Sai MVR, Roy PS, Dwevedi RS. Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS vegetation index product. Int J Remote Sens. 2010;31:3987–4005.

    Article  Google Scholar 

  55. Al-Yaari A, Wigneron JP, Ducharne A, Kerr Y, de Rosnay P, de Jeu R, Govind A, Al Bitar A, Albergel C, Muñoz-Sabater J, Richaume P, Mialon A. Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates. Remote Sens Environ. 2014;149:181–95.

    Article  Google Scholar 

  56. Dong G, Guo J, Chen J, Sun G, Gao S, Hu L, Wang Y. Effects of spring drought on carbon sequestration, evapotranspiration and water use efficiency in the songnen meadow steppe in northeast China. Ecohydrology. 2011;4(2):211–24.

    Article  Google Scholar 

  57. Chen S, Chen J, Lin G, Zhang W, Miao H, Wei L, Huang J, Han X. Energy balance and partition in Inner Mongolia steppe ecosystems with different land use types. Agric For Meteorol. 2009;149:1800–9.

    Article  Google Scholar 

  58. Lian J, Huang M. Comparison of three remote sensing based models to estimate evapotranspiration in an oasis-desert region. Agric Water Manag. 2016;165:153–62.

    Article  Google Scholar 

  59. Shao P, Zeng X, Sakaguchi K, Monson RK, Zeng X. Terrestrial carbon cycle: climate relations in eight CMIP5 earth system models. J Clim. 2013;26(22):8744–64.

    Article  Google Scholar 

  60. Kato T, Tang Y, Gu S, Hirota M, Du M, Li Y, Zhao X. Temperature and biomass influences on interannual changes in CO2 exchange in an alpine meadow on the Qinghai-Tibetan Plateau. Glob Change Biol. 2006;12(7):1285–98.

    Article  Google Scholar 

  61. Liu SM, Xu ZW, Wang WZ, Jia ZZ, Zhu MJ, Bai J, Wang JM. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrol Earth Syst Sci. 2011;15:1291–306.

    Article  Google Scholar 

  62. Hu Z, Yu G, Fu Y, Sun X, Li Y, Shi P, Wang Y, Zheng Z. Effects of vegetation control on ecosystem water use efficiency within and among four grassland ecosystems in China. Glob Change Biol. 2008;14(7):1609–19.

    Article  Google Scholar 

  63. Wang Z, Xiao X, Yan X. Modeling gross primary production of maize cropland and degraded grassland in northeastern China. Agric For Meteorol. 2010;150(9):1160–7.

    Article  Google Scholar 

  64. Shang L, Zhang Y, Lü S, Wang S. Energy exchange of an alpine grassland on the eastern Qinghai-Tibetan Plateau. Sci Bull. 2015;60(4):435–46.

    Article  Google Scholar 

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All scientists and technicians maintaining the flux site management, data collection and long-term observations are greatly appreciated. The eddy covariance data and meteorological data are acquired from the Coordinated Observation and Synthesis in Arid and Semi-arid China (COSAS), a part of ChinaFLUX. We are also grateful to the Distributed Active Archive Center of the Oak Ridge National Laboratory, and the Earth Observing System Data for making these MODIS products available.


The study was jointly supported by the National Natural Science Foundation of China (Grant Nos. 41830648, 41401221), the State Cultivation Base of Eco-agriculture for Southwest Mountainous Land, Southwest University China (Grant No. 5330200076) and Chongqing Basic and Frontier Research Program (Grant No. cstc2018jcyjAX0056).

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PY conceived the research; XT and ZD contributed to data analysis and wrote the manuscript; YZ and HL provided data support; LY and MM improved the manuscript. All authors read and approved the final manuscript.

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Correspondence to Pujia Yu.

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Tang, X., Zhou, Y., Li, H. et al. Remotely monitoring ecosystem respiration from various grasslands along a large-scale east–west transect across northern China. Carbon Balance Manage 15, 6 (2020).

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  • Ecosystem respiration
  • Grassland
  • Piecewise regression