Carbon-nitrogen-water Fluxes and Auxiliary Parameters of China's Ecosystems Zone II Versions EN1 Vol 4 (1) 2019
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A dataset of radiation and light use-efficiency of typical Chinese ecosystems (2002 – 2010)
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: 2018 - 05 - 30
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Abstract & Keywords
Abstract: Light is a primary energy source. Its use efficiency reflects the capacity of ecosystem in converting light energy and producing organic matter. Revealing the values of radiation and light use-efficiency in typical ecosystems provides references for assessing regional radiation resources and light use-efficiency, which is also helpful in assessing the regional capacity of organic matter production and carbon sequestration. Based on ChinaFLUX observations and published literature, we built the radiation and light use-efficiency dataset of typical Chinese ecosystems from 2002 to 2010. This dataset contains 126 annual observations conducted at 51 ecosystems, covering radiation resource, light use-efficiency, and absorbed light use efficiency. In addition, the dataset also contains the biotic and abiotic information such as ecosystem code, observation year, longitude, latitude, altitude, ecosystem type, annual air temperature, annual precipitation, annual CO2 mass concentration, annual mean leaf area index, annual maximum leaf area index. The dataset could provide data bases for research on carbon cycle and climate change.
Keywords: carbon cycle; photosynthetic active radiation; eddy covariance; terrestrial ecosystem
Dataset Profile
Chinese title2002–2010年中国典型生态系统辐射及光能利用效率数据集
English titleA dataset of radiation and light use-efficiency of typical Chinese ecosystems (2002 – 2010)
Data corresponding authorYu Guirui (yugr@igsnrr.ac.cn)
Data authorsZhu Xianjin, Yu Guirui, Wang Qiufeng, Chen Zhi, Zheng Han, Che Tao, Chen Shiping, Guo Jixun, Gu Song, Han Shijie, Hao Yanbin, Huang Hui, Jia Gensuo, Li Yan, Li Yingnian, Lin Guanghui, Meng Ping, Ouyang Zhu, Rao Liangyi, Shi Peili, Sun Chunjian, Wu Jinshui, Wang Chuankuan, Wang Huimin, Wang Yanfen, Wang Yuesi, Xiao Wenfa, Yan Junhua, Yang Dawen, Zha Tonggang, Zhang Fawei, Zhang Jinsong, Zhang Junhui, Zhang Xianzhou, Zhang Xudong, Zhang Yiping, Zhao Bin, Zhao Fenghua, Zhao Liang, Zhao Xinquan, Zhao Zhonghui, Zhou Guangsheng, Zhou Guoyi
Time range2002 – 2010
Geographical scope51 typical ecosystems of Chinese FLUX Observation and Research Network (ChinaFLUX)
Data format*.xlsx
Data volume58 KB
Data service system<http://www.cnern.org.cn/data/meta?id=40574>;
<http://www.sciencedb.cn/dataSet/handle/616>
Sources of fundingNational Natural Science Foundation of China (31500390), National Key Research and Development Program of China (2016YFA0600104), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020302), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-169).
Dataset compositionThis dataset includes 126 annual observations conducted towards 51 ecosystems. Specific information includes ecosystem code, observation year, latitude, longitude, ecosystem type, as well as biotic and abiotic factors such as annual mean air temperature, annual precipitation, annual mean CO2 mass concentration, and annual mean leaf area index.
1.   Introduction
Radiation, the main energy source of terrestrial surface, is the basis of organic fixation and food production on the Earth. Light use-efficiency (LUE), expressing the efficiency of plants’ radiation utilization, reflects the ecosystem’s capacity in converting light energy.1 LUE is also an important parameter in calculating ecosystem productivity and assessing regional carbon budget.2 It is crucial to reveal the intensity of radiation and its use efficiency in typical ecosystems, which not only helps assess the ecosystems’ capacity in light conversion, but also sets a data basis for assessing the regional productivity and its potential.3
Owing to the difference in light quality, the radiation reaching the land surface is divided into gross radiation (Rg ) and photosynthetic active radiation (PAR). PAR is the energy that can be directly used by plants, though it is rarely directly observed. Rg , though measured for a long time, is limited when used for assessing the radiation of ecosystem and understanding LUE in typical ecosystems, as its use by plants is restricted. Given the varied types of carbon flux (e.g., gross primary productivity (GPP), net primary productivity (NPP)) and light (e.g., Rg , PAR, and absorbed PAR) involved in calculating LUE, the definition and calculation of LUE are divergent.4 For example, the apparent quantum efficiency can be obtained from the light response curve, reflecting plants’ maximum capacity of light utilization,5 while the LUE calculated from the ratio of NPP (measured from biomass inventory) to measured radiation (i.e., PAR, Rg ) provides the primitive basis for LUE calculation.4, 6 Calculated from the ratio of GPP to PAR and absorbed PAR, LUE represents the plants’ capacity in utilizing the PAR arriving at the land surface and that absorbed by the plants during photosynthetic process, respectively. Given its basic role in calculating other defined LUEs, LUE defined in this way has attracted wide scholarly concerns.
Eddy covariance can be used directly to measure the net CO2 exchange between ecosystems and atmosphere, thereby deriving GPP and rendering possible the calculation of GPP-based LUE.7-9 When measuring carbon fluxes using eddy covariance techniques, scientists simultaneously measured the relevant biological and climatic factors like PAR, which lays a solid data foundation for evaluating the radiation and light use-efficiency in typical ecosystems.10 While there have been rich studies focusing on the dynamics of radiation and light use-efficiency in specific ecosystems, little effort has been made as to summarize the difference in radiation and light use-efficiency among ecosystems, which hinders our understanding of their regional differences. In view of this, based on eddy covariance measurements performed by ChinaFLUX and other sites in China, we systematically summarized the radiation and light use-efficiency data of typical Chinese ecosystems from 2002 to 2010. The dataset provides basis for regional assessment of radiation distribution, productivity and its potential.
2.   Data collection and processing
2.1   Data sources
This dataset covers 10 ecosystems observed by ChinaFLUX (i.e., Damxung alpine meadow, Haibei alpine wetland, Haibei alpine shrubland, Inner Mongolia temperate grassland, Changbaishan mixed temperate coniferous and broad-leaved forest, Yucheng temperate cropland, Qianyanzhou subtropical evergreen coniferous forest, Dinghushan subtropical broad-leaved forest, Ailaoshan subtropical evergreen broad-leaved forest, Xishuangbanna tropical evergreen broad-leaved forest) and other 41 ecosystems in China extracted from published literature (Figure 1). The latitude and longitude of each ecosystem are detailed in Table 1. This dataset is an assemblage of published literature and eddy covariance measurements.
Table 1   Ecosystems and their geographical information
CodeAbbreviationEcosystemLatitude (°N)Longitude (°E)Year observed
XSBNXishuangbannaXishuangbanna tropical evergreen broadleaved forest21.95101.202003~2008
DHSDinghushanDinghushan subtropical evergreen broadleaved forest23.17112.532003~2008
ALSAilaoshanAilaoshan subtropical evergreen broadleaved forest24.53101.022009~2010
QYZQianyanzhouQianyanzhou subtropical evergreen needleleaved forest26.73115.052003~2008
HTHuitongHuitong subtropical evergreen needleleaved forest26.83109.752008
TYTaoyuanTaoyuan subtropical rice paddy28.92111.452003
YYYueyangYueyang subtropical deciduous broadleaved forest29.53112.862006–2007
DXDangxiongDangxiong alpine meadow29.6791.332004–2008
AQAnqingAnqing subtropical deciduous broadleaved forest30.47116.992006–2007
DTGDongtan-GaotanDongtan gaotan subtropical coastal wetland31.52121.962005–2007
DTDDongtan-DitanDongtan ditan subtropical coastal wetland31.52121.972005–2007
DTZDongtan-ZhongtanDongtan zhongtan subtropical coastal wetland31.58121.902005
XPXipingXiping temperate deciduous broadleaved forest33.35113.912010
SJYSanjiangyuanSanjiangyuan alpine meadow34.35100.552006
WSWeishanWeishan temperate cropland36.65116.052007–2008
YCYuchengYucheng temperate cropland36.83116.572003–2008
HBHaibeiHaibei alpine meadow37.62101.302002–2004
HBGCHaibeiguancongHabei alpine shrubland37.67101.332003–2008
HBSDHaibeishidiHaibei alpine wetland37.68101.312004–2008
DXFDaxingDaxing temperate deciduous broadleaved forest39.53116.252006
KBQGKubuqi caodiKubuqi temperate steppe40.38108.552006
KBQFKubuqi senlinKubuqi temperate deciduous broadleaved forest40.54108.692005–2006
PJPanjinPanjin temperate coastal wetland41.13121.902005
DLCDuolun nongtianDuolun temperate cropland42.05116.672005–2006
DLGDuolun caodiDuolun temperate steppe42.05116.282005–2006, 2010
CBSChangbaishanChangbaishan temperate mixed forest42.40128.102003–2008
XLHTFXilinhaote weifengXilinhaote temperate fenced steppe43.55116.672006
XLHTDXilinhaote tuihuaXilinhaote temperate degraded steppe43.55116.672006
XLHTXilintaote-Stipa krylovii steppeXilinhaote temperate typical steppe44.13116.332004–2006
FKFukangFukang temperate desert44.2887.932004–2007
NMNeimengNeimeng temperate steppe44.53116.672004–2008
TYCTongyu nongtianTongyu temperate cropland44.57122.922004–2006
CLChanglingChangling temperate steppe44.58123.502007–2008
TYGTongyu caodiTongyu temperate steppe44.59122.522004–2006
LSLaoshanLaoshan temperate evergreen needleleaved forest45.33127.672004
MESMaoershanMaoershan temperate evergreen needleleaved forest45.42127.672005
SJSSanjiang shidiSanjiang temperate wetland47.58133.522005
SJDSanjiang shuidaoSanjiang temperate rice paddy47.58133.522005
SJCSanjiang dadouSanjiang temperate soybean cropland47.58133.522005
HZHuzhongHuzhong temperate evergreen needleleaved forest51.78123.022007–2008
REGRuoergaiRuoergai alpine wetland33.93102.872008–2009
GQZhanjianggaoqiaoZhanjiang Gaoqiao tropical mangrove wetland21.57109.762010
YXYunxiaoYunxiao subtropical mangrove wetland23.92117.422009
HNHuainingHuaining subtropical deciduous broadleaved forest33.00117.002005
YKYingkeYingke temperate cropland38.86100.412008
XLDXiaolangdiXiaolangdi temperate deciduous broadleaved forest35.020112.472007–2009
DGDongguanDongguan subtropical grassland22.97113.742009–2010
HGHuangtugaoyuanHuangtugaoyuan temperate grassland35.95104.132007–2008
JFLJianfenglingJianfengling tropical evergreen broadleaved forest18.61108.842006–2009
HYHaiyanHaiyan alpine meadow36.95100.752010
ARArouArou alpine meadow38.04100.462009


Fig.1   Spatial distribution of the ecosystems
2.2   Data collection method
2.2.1   Radiation data
Considering the scarcity of ecosystem radiation data in existing literature, we extracted Rg and PAR of each ecosystem based on their latitude and longitude and their spatial distributions in China, which was to ensure the data consistency of each ecosystem. The spatial distribution of Rg in China was interpolated by geostatistics software, calculated based on relative humidity, air temperature, precipitation and other factors.11 However, the spatial distribution of PAR was calculated from the meteorological data of 740 sites and the Rg data of 122 sites affiliated to China Meteorological Administration, the observed Rg and PAR of 36 sites from Chinese Ecosystem Research Network (CERN). The results were then interpolated with ArcGIS. Annual PAR was summed from the daily interpolated PAR.12
2.2.2   Light use-efficiency data
In this study, light use-efficiency (LUE) was calculated from annual gross primary productivity (AGPP) and annual photosynthetic active radiation (PAR), including LUE calculated from the annual PAR and the absorbed light use-efficiency (ALUE) calculated from the annual absorbed photosynthetic active radiation (APAR).9 The calculations of LUE and ALUE were as follows:
(1)
(2)
where AGPP is annual gross primary productivity, PAR and APAR are annual photosynthetic active radiation and annual absorbed photosynthetic active radiation, respectively.
AGPP was obtained from eddy covariance measurements, which measures the net exchange of CO2, H2O, and energy between ecosystems and the atmosphere based on micrometeorology theory. Utilizing the infrared gas analyzer and the anemometer with high frequency response, eddy covariance technique measured the density pulsation of CO2, H2O and temperature above the canopy to calculate the net carbon, water, and energy fluxes between ecosystems and the atmosphere. The measured net carbon flux was further divided into gross primary productivity and ecosystem respiration based on the nonlinear regression relationship.13, 14 For the data from ChinaFLUX sites, we obtained AGPP by processing the measured raw data according to the general data processing routines of ChinaFLUX, which includes data quality control, gap-filling, and flux partition.10 For the data from existing literatures, we required that AGPP values should be published in a consecutive year.
In theory, the annual APAR of a particular ecosystem should be the cumulative value of the ecosystem's daily APAR of that particular year. However, in practice, existing literature did not always, even scarcely, record the daily LAI and PAR of ecosystems, which rendered impossible the summing of daily APAR. Therefore, we selected an approximate approach to calculate APAR to keep a relative data consistency. APAR was calculated as the product of annual PAR and annual mean fraction of absorbed photosynthetic active radiation (fPAR), where fPAR was estimated with Bill – Lambert’s Law.
(3)
where k is the extinction coefficient, set to 0.5 according to the existing results,15 and LAI is the annual mean leaf area index of an ecosystem, extracted from Global Land Surface Satellite (GLASS) Dataset based on the latitude and longitude of each ecosystem and the year of observation.16 The data processing framework is shown in Figure 2.
2.2.3   Auxiliary data
This dataset also provides the annual CO2 mass concentration (ρc ) of each ecosystem, which was calculated based on the CO2 mole fraction and atmosphere pressure. The CO2 mole fraction was estimated as the measured value of Mauna Loa in Hawaii, USA, while the atmospheric pressure was calculated by using the pressure-height formula.9
3.   Data sample description
The dataset consists of two datasheets, named “Data” and “Data Sources”, which totals a volume of 57 KB. The Data datasheet includes basic information, observation year, and observed values for each ecosystem, with 126 data records covering 51 ecosystems of 4 ecosystem types involving forest, grassland, cropland and wetland. In summary, there are 18 forests, 16 grasslands, 8 croplands, and 9 wetlands in the built dataset. The Data Sources datasheet exhibits the main sources of the data included in this dataset, which consists of 31 records.


Fig.2   Data processing framework
A code was assigned to each ecosystem according to the following rule: the ecosystem was generally coded as the acronym of its name. The initials of management measures or ecosystem types were added as suffix in case of duplication, as a way to distinguish. Taking XLHTD as an example: XLHT denotes Xi, Lin, Hao and Te, while D indicates that the ecosystem is a degraded ecosystem (degradation). The coding rule is illustrated in Table 2.
Table 2   Data fields of this dataset and their illustrations
Data fieldData typeExample
IDNumber1
CodeStringDHS
AbbreviationStringDinghushan
EcosystemStringDinghushan subtropical evergreen broadleaved forest
LatitudeNumber23.167
LongitudeNumber112.533
Altitude (m)Number300
Year observedNumber2003
Annual mean air temperature (℃)Number20.66
Annual precipitation (mm)Number1289.40
Annual mean CO2 mass concentration (mg CO2 m-3)Number723.34
Interpolated annual gross radiation (MJ m-2 yr-1)Number4891.72
Interpolated annual photosynthetic active radiation (MJ m-2 yr-1)Number2019.36
Observed annual gross radiation (MJ m-2 yr-1)Number4534.91
Observed annual photosynthetic active radiation (MJ m-2 yr-1)Number1796.79
Annual mean leaf area index (m2 m-2)Number3.84
Maximum leaf area index (m2 m-2)Number4.4
Light use efficiency (g C MJ-1)Number744.94
Absorbed light use efficiency (g C MJ-1)Number1.97
Data sourcesStringChinaFLUX
4.   Data quality control
To control the quality of the radiation and light use-efficiency data obtained in this study, we conduct data quality control from AGPP and light aspects.
(1) AGPP sourced from ChinaFLUX long-term measurements and published data in literature. First, ChinaFLUX had sufficient experience in eddy covariance measurements and regularly calibrated the observation instruments, which ensured the accuracy of the observation. Second, the processing of the observed raw data abided by the ChinaFLUX data processing routines, whose wide recognition guaranteed the accuracy and comparability of the data for different ecosystems.17 In addition, AGPP obtained from published literature were sent for peer-review to ensure they met the publishing criteria.
(2) Radiation sourced from the spatial distributions of Rg11 and PAR.12 This method of radiation data collection was well-accepted, as indicated by its high application rate. Meanwhile, we further validated the radiation data using the observations of ChinaFLUX (Figure 3). Results suggested that the extracted data were in a good agreement with the observations, among which the observed Rg and PAR could explain 93% and 77% spatial variation of the extracted data (Figure 3). Though there were some deviations between the observed PAR and the extracted values, these deviations primarily resulted from the attenuation of PAR measuring instruments in ChinaFLUX. As the PAR measuring instrument attenuated year by year, the observation values decreased accordingly.18


Fig.3   A comparison between measured (x-axis) gross radiation (a) and photosynthetic active radiation (b) and their corresponding extracted values (y-axis) for ChinaFLUX ecosystems
5.   Usage notes and recommendations
This dataset collected radiation and light use-efficiency data of typical ecosystems in China based on eddy covariance measurements, which can provide data reference for assessing the production capacity and potential of typical ecosystems and managing the regional light and thermal resources. However, considering the shortcomings in acquiring the key variables, we must admit that this dataset has certain uncertainties, which can be summarized into the following aspects.
(1) Uncertainties in the radiation data can result in uncertainties in the light use-efficiency data. The radiation data of this dataset was extracted from the interpolated spatial data, which might deviate from other data sources for varied, though widely-accepted, interpolation methods thus further introducing certain biases in the light use-efficiency data.
(2) The uncertainties in AGPP would introduce some uncertainties to light use-efficiency. In this dataset, AGPP of the ChinaFLUX ecosystems stemmed from the long-term observation subject to the general data processing routines, while that of other ecosystems originated from published literature. The varied circumstances resulted in varied data processing procedures adopted for different ecosystems. In addition, even with the same data processing routines, AGPP might also be affected by different parameters set by different data processors, which would further affect the value of light use-efficiency. Therefore, the light use-efficiency data reported by this dataset may differ slightly from those reported by other references.
(3) The values in this dataset only reflected the status of radiation and light use-efficiency of specific ecosystem in the observed year. Considering the values of radiation and light use-efficiency varied from one year to another, users should be cautious in extrapolating the values from one year to another.
(4) The light use-efficiency reported by this dataset were obtained through Eqs. (1)- (3), reflecting the capacity of each ecosystem in light conversion at a fixed temporal scale (a whole year). These values could provide references for these under other definitions. However, considering the varied definitions of light use-efficiency under different temporal and spatial scales, users should be cautious in applying the data of this dataset in other circumstances.
For other questions regarding data usage, please refer to a published paper.9
This dataset can be accessed and downloaded at the Synthesis Research Center of Chinese Ecosystem Research Network (http://www.cnern.org.cn). After logging into the system, users can click the "Data Paper Data" icon on the home page or select "Carbon, nitrogen, and water flux observation special issue" in the "Data Paper Data" column. As an alternative, users can also log in to Science Data Bank (http://www.sciencedb.cn/dataSet/handle/616) for data browse and download.
Acknowledgments
The following authors have contributed to data collection and quality control (in alphabetical order): Che Tao, Chen Shiping, Guo Jixun, Gu Song, Han Shijie, Hao Yanbin, Huang Hui, Jia Gensuo, Li Yan, Li Yingnian, Lin Guanghui, Meng Ping, Ouyang Zhu, Rao Liangyi, Shi Peili, Sun Chunjian, Wu Jinshui, Wang Chuankuan, Wang Huimin, Wang Yanfen, Wang Yuesi, Xiao Wenfa, Yan Junhua, Yang Dawen, Zha Tonggang, Zhang Fawei, Zhang Jinsong, Zhang Junhui, Zhang Xianzhou, Zhang Xudong, Zhang Yiping, Zhao Bin, Zhao Fenghua, Zhao Liang, Zhao Xinquan, Zhao Zhonghui, Zhou Guangsheng, and Zhou Guoyi.
1.
Monteith JL, Solar Radiation and Productivity in Tropical Ecosystems. Journal of Applied Ecology 9(1972): 747-766.
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Running SW, Nemani RR, Heinsch FA et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54(2004): 547-560.
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Hilker T, Coops NC, Wulder MA, et al. The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements. Science of the Total Environment 404(2008): 411-423.
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Albrizio R & Steduto P. Resource use efficiency of field-grown sunflower, sorghum, wheat and chickpea: I. Radiation use efficiency. Agricultural and Forest Meteorology 130(2005): 254-268.
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Zhang LM, Yu GR, Sun XM et al. Seasonal variations of ecosystem apparent quantum yield (alpha) and maximum photosynthesis rate (P-max) of different forest ecosystems in China. Agricultural and Forest Meteorology 137(2006): 176-187.
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Yuan W, Cai W, Liu D & Dong W. Satellite-based vegetation production models of terrestrial ecosystem: An overview. Advances in Earth Science 29(2014): 541-550.
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Garbulsky MF, Peñuelas J, Papale D et al. Patterns and controls of the variability of radiation use efficiency and primary productivity across terrestrial ecosystems. Global Ecology and Biogeography 19(2010): 253-267.
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Schwalm CR, Black TA, Arniro BD et al. Photosynthetic light use efficiency of three biomes across an east-west continental-scale transect in Canada. Agricultural and Forest Meteorology 140(2006): 269-286.
9.
Zhu X-J, Yu G-R, Wang Q-F et al. Approaches of climate factors affecting the spatial variation of annual gross primary productivity among terrestrial ecosystems in China. Ecological Indicators 62(2016): 174-181.
10.
Yu GR, Wen XF, Sun XM et al. Overview of ChinaFLUX and evaluation of its eddy covariance measurement. Agricultural and Forest Meteorology 137(2006): 125-137.
11.
He H, Yu G, Liu X et al. Study on sptialization technology of terrestrial eco-information in China (II): Solar radiation. Journal of Natural Resources 19(2004): 679-687.
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Zhu X, He H, Liu M et al. Spatio-temporal variation of photosynthetically active radiation in China in recent 50 years. Journal of Geographical Sciences 20(2010): 803-817.
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Falge E, Baldocchi D, Olson R et al. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology 107(2001): 43-69.
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Reichstein M, Falge E, Baldocchi D et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology 11(2005): 1424-1439.
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Yuan WP, Liu SG, Yu GR et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment 114(2010): 1416-1431.
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Liang S, Zhao X, Liu S et al. A long-term Global Land Surface Satellite (GLASS) data-set for environmental studies. International Journal of Digital Earth 6(2013): 5-33.
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Yu G, Ren W, Chen Z et al. Construction and progress of Chinese terrestrial ecosystem carbon, nitrogen and water fluxes coordinated observation. Journal of Geographical Sciences 26(2016): 803-826.
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Zhu Z, Sun X, Yu G et al. Radiometers performance attenuation and data correction in long-term observation of total radiation and photosynthetically active radiation in typical forest ecosystems in China. Chinese Journal of Applied Ecology 22(2011): 2954-2962.
Data citation
1. Zhu X, Yu G, He H et al. A dataset of radiation and light use-efficiency of typical Chinese ecosystems (2002 – 2010). Science Data Bank. DOI: 10.11922/sciencedb.616 (2018).
Article and author information
How to cite this article
Zhu X, Yu G, He H et al. A dataset of radiation and light use-efficiency of typical Chinese ecosystems (2002 – 2010). China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0035.zh
Zhu Xianjin
dataset building and manuscript writing.
PhD, research area: ecosystem carbon, water fluxes, and their interaction.
Yu Guirui
framework design.
yugr@igsnrr.ac.cn
PhD, Professor, research area: global change and carbon cycle.
He Honglin
framework design.
PhD, Professor, research area: ecological informatics.
Chen Zhi
data collection and manuscript revision.
PhD, research area: global change and carbon cycle.
Wang Qiufeng
data collection and manuscript revision.
PhD, research area: global change and carbon cycle.
Zheng Han
data collection and manuscript revision.
PhD, research area: global change and water cycle.
Che Tao
Chen Shiping
Guo Jixun
Gu Song
Han Shijie
Hao Yanbin
Huang Hui
Jia Gensuo
Li Yan
Li Yingnian
Lin Guanghui
Meng Ping
Ouyang Zhu
Rao Liangyi
Shi Peili
Sun Chunjian
Wu Jinshui
Wang Chuankuan
Wang Huimin
Wang Yanfen
Wang Yuesi
Xiao Wenfa
Yan Junhua
Yang Dawen
Zha Tonggang
Zhang Fawei
Zhang Jinsong
Zhang Junhui
Zhang Xianzhou
Zhang Xudong
Zhang Yiping
Zhao Bin
Zhao Fenghua
Zhao Liang
Zhao Xinquan
Zhao Zhonghui
Zhou Guangsheng
Zhou Guoyi
National Natural Science Foundation of China (31500390), National Key Research and Development Program of China (2016YFA0600104), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020302), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-169).
Publication records
Published: Dec. 28, 2018 ( VersionsEN1
Released: June 27, 2018 ( VersionsZH3
Published: Dec. 28, 2018 ( VersionsZH4
Updated: Dec. 28, 2018 ( VersionsZH5
References
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