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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
|English title||A dataset of radiation and light use-efficiency of typical Chinese ecosystems (2002 – 2010)|
|Data corresponding author||Yu Guirui (firstname.lastname@example.org)|
|Data authors||Zhu 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 range||2002 – 2010|
|Geographical scope||51 typical ecosystems of Chinese FLUX Observation and Research Network (ChinaFLUX)|
|Data volume||58 KB|
|Data service system||<http://www.cnern.org.cn/data/meta?id=40574>;|
|Sources of funding||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).|
|Dataset composition||This 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.|
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.
2.1 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.
|Code||Abbreviation||Ecosystem||Latitude (°N)||Longitude (°E)||Year observed|
|XSBN||Xishuangbanna||Xishuangbanna tropical evergreen broadleaved forest||21.95||101.20||2003~2008|
|DHS||Dinghushan||Dinghushan subtropical evergreen broadleaved forest||23.17||112.53||2003~2008|
|ALS||Ailaoshan||Ailaoshan subtropical evergreen broadleaved forest||24.53||101.02||2009~2010|
|QYZ||Qianyanzhou||Qianyanzhou subtropical evergreen needleleaved forest||26.73||115.05||2003~2008|
|HT||Huitong||Huitong subtropical evergreen needleleaved forest||26.83||109.75||2008|
|TY||Taoyuan||Taoyuan subtropical rice paddy||28.92||111.45||2003|
|YY||Yueyang||Yueyang subtropical deciduous broadleaved forest||29.53||112.86||2006–2007|
|DX||Dangxiong||Dangxiong alpine meadow||29.67||91.33||2004–2008|
|AQ||Anqing||Anqing subtropical deciduous broadleaved forest||30.47||116.99||2006–2007|
|DTG||Dongtan-Gaotan||Dongtan gaotan subtropical coastal wetland||31.52||121.96||2005–2007|
|DTD||Dongtan-Ditan||Dongtan ditan subtropical coastal wetland||31.52||121.97||2005–2007|
|DTZ||Dongtan-Zhongtan||Dongtan zhongtan subtropical coastal wetland||31.58||121.90||2005|
|XP||Xiping||Xiping temperate deciduous broadleaved forest||33.35||113.91||2010|
|SJY||Sanjiangyuan||Sanjiangyuan alpine meadow||34.35||100.55||2006|
|WS||Weishan||Weishan temperate cropland||36.65||116.05||2007–2008|
|YC||Yucheng||Yucheng temperate cropland||36.83||116.57||2003–2008|
|HB||Haibei||Haibei alpine meadow||37.62||101.30||2002–2004|
|HBGC||Haibeiguancong||Habei alpine shrubland||37.67||101.33||2003–2008|
|HBSD||Haibeishidi||Haibei alpine wetland||37.68||101.31||2004–2008|
|DXF||Daxing||Daxing temperate deciduous broadleaved forest||39.53||116.25||2006|
|KBQG||Kubuqi caodi||Kubuqi temperate steppe||40.38||108.55||2006|
|KBQF||Kubuqi senlin||Kubuqi temperate deciduous broadleaved forest||40.54||108.69||2005–2006|
|PJ||Panjin||Panjin temperate coastal wetland||41.13||121.90||2005|
|DLC||Duolun nongtian||Duolun temperate cropland||42.05||116.67||2005–2006|
|DLG||Duolun caodi||Duolun temperate steppe||42.05||116.28||2005–2006, 2010|
|CBS||Changbaishan||Changbaishan temperate mixed forest||42.40||128.10||2003–2008|
|XLHTF||Xilinhaote weifeng||Xilinhaote temperate fenced steppe||43.55||116.67||2006|
|XLHTD||Xilinhaote tuihua||Xilinhaote temperate degraded steppe||43.55||116.67||2006|
|XLHT||Xilintaote-Stipa krylovii steppe||Xilinhaote temperate typical steppe||44.13||116.33||2004–2006|
|FK||Fukang||Fukang temperate desert||44.28||87.93||2004–2007|
|NM||Neimeng||Neimeng temperate steppe||44.53||116.67||2004–2008|
|TYC||Tongyu nongtian||Tongyu temperate cropland||44.57||122.92||2004–2006|
|CL||Changling||Changling temperate steppe||44.58||123.50||2007–2008|
|TYG||Tongyu caodi||Tongyu temperate steppe||44.59||122.52||2004–2006|
|LS||Laoshan||Laoshan temperate evergreen needleleaved forest||45.33||127.67||2004|
|MES||Maoershan||Maoershan temperate evergreen needleleaved forest||45.42||127.67||2005|
|SJS||Sanjiang shidi||Sanjiang temperate wetland||47.58||133.52||2005|
|SJD||Sanjiang shuidao||Sanjiang temperate rice paddy||47.58||133.52||2005|
|SJC||Sanjiang dadou||Sanjiang temperate soybean cropland||47.58||133.52||2005|
|HZ||Huzhong||Huzhong temperate evergreen needleleaved forest||51.78||123.02||2007–2008|
|REG||Ruoergai||Ruoergai alpine wetland||33.93||102.87||2008–2009|
|GQ||Zhanjianggaoqiao||Zhanjiang Gaoqiao tropical mangrove wetland||21.57||109.76||2010|
|YX||Yunxiao||Yunxiao subtropical mangrove wetland||23.92||117.42||2009|
|HN||Huaining||Huaining subtropical deciduous broadleaved forest||33.00||117.00||2005|
|YK||Yingke||Yingke temperate cropland||38.86||100.41||2008|
|XLD||Xiaolangdi||Xiaolangdi temperate deciduous broadleaved forest||35.020||112.47||2007–2009|
|DG||Dongguan||Dongguan subtropical grassland||22.97||113.74||2009–2010|
|HG||Huangtugaoyuan||Huangtugaoyuan temperate grassland||35.95||104.13||2007–2008|
|JFL||Jianfengling||Jianfengling tropical evergreen broadleaved forest||18.61||108.84||2006–2009|
|HY||Haiyan||Haiyan alpine meadow||36.95||100.75||2010|
|AR||Arou||Arou alpine meadow||38.04||100.46||2009|
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:
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.
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
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.
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.
|Data field||Data type||Example|
|Ecosystem||String||Dinghushan subtropical evergreen broadleaved forest|
|Annual mean air temperature (℃)||Number||20.66|
|Annual precipitation (mm)||Number||1289.40|
|Annual mean CO2 mass concentration (mg CO2 m-3)||Number||723.34|
|Interpolated annual gross radiation (MJ m-2 yr-1)||Number||4891.72|
|Interpolated annual photosynthetic active radiation (MJ m-2 yr-1)||Number||2019.36|
|Observed annual gross radiation (MJ m-2 yr-1)||Number||4534.91|
|Observed annual photosynthetic active radiation (MJ m-2 yr-1)||Number||1796.79|
|Annual mean leaf area index (m2 m-2)||Number||3.84|
|Maximum leaf area index (m2 m-2)||Number||4.4|
|Light use efficiency (g C MJ-1)||Number||744.94|
|Absorbed light use efficiency (g C MJ-1)||Number||1.97|
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
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.
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.
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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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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).
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