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Monthly average satellite-estimated dataset of Lake Taihu’s dissolved carbon dioxide concentration from 2002 to 2018

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing 210008, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：tcqi@niglas.ac.cn
  • Introduction：齐天赐，1994年生，男，博士研究生， 研究方向为湖泊水环境遥感。E-mail： tcqi@niglas.ac.cn
QI Tianci12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing 210008, China

    College of Urban and Environmental Sciences, Northwest University, Xi'an 710027, China

  • Email：htduan@niglas.ac.cn
  • Introduction：段洪涛，1979年生，男，研究员， 研究方向为湖泊遥感与智慧流域。E-mail： htduan@niglas.ac.cn
DUAN Hongtao13*,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing 210008, China

CAO Zhigang1,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing 210008, China

    University of Chinese Academy of Sciences, Beijing 100049, China

SHEN Ming12,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing 210008, China

XIAO Qitao1,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing 210008, China

LIU Dong1,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing 210008, China

    University of Chinese Academy of Sciences, Beijing 100049, China

MA Jinge12

résumé

Lakes play an important role in the global carbon cycle. The dissolved carbon dioxide concentration (cCO2) controls the direction and amount of the lake CO2 flux, which makes it one of the keys to the Lake CO2 emission estimates. Due to the limitations of traditional field surveys on the spatiotemporal representativeness, large efforts of field surveys are still required to fulfil the requirements of monitoring lake cCO2 dynamics. China’s third largest freshwater lake—Lake Taihu is a hot spot for lake carbon cycle and eutrophication research because of its complex environmental problems. Although Lake Taihu has long-term field limnological observations, including the measurements of physical, chemical, and biological parameters, the spatiotemporal distributions of sampling sites are still limited for the accurate estimation of the CO2 emissions, which is likely to give uncertainty and deviation to its CO2 emission estimates. It is necessary to take advantages of high frequency and wide range remote sensing technologies for achieving larger-scale and longer-term estimations of lake cCO2 dynamics compared to field surveys.In this paper, we used the MODIS-derived chlorophyll-a concentration, lake surface temperature, diffuse attenuation coefficient of photosynthetically active radiation, and photosynthetically active radiation to estimate daily cCO2 of Lake Taihu (the coefficient of determination R2=0.84, root mean square error RMSE=11.81 μmol·L-1, unbiased percent difference UPD=22.46%). After data quality control, the daily cCO2 were averaged on a monthly scale to obtain the monthly average cCO2 of Lake Taihu. The data was stored in GeoTIFF grid format, with the GCS_WGS_1984 geographic coordinate system. The dataset contains 198 files of monthly average cCO2 of Lake Taihu from July 2002 to December 2018.The uncertainty assessment results of the product show that under the influence of all input variables, the monthly cCO2 product would overestimate about 30%. The differences between cCO2 of pixel-sample matchups were small in total (Root mean standard error RMSE=12.83 μmol·L-1, non-bias percentage deviation UPD=24.03%). The annual average of cCO2 estimated by field observation and MODIS were consistent with each other in different regions of Lake Taihu (Root mean standard error RMSE <13.24 μmol·L-1, non-bias percentage deviation UPD <25.82%). Based on the monthly average dataset, the cCO2 of Lake Taihu showed significant seasonal dynamics, which were was low in summer and autumn (June to November) and eastern region, and high in winter and spring (December to May) and western region. Besides, the annual average cCO2 showed a significant declining trend (0.80 μmol·L-1·a-1, p<0.01).This monthly average dataset (The download address is https://doi.org/10.5281/zenodo.4729048) corresponds to the time scale of traditional limnological and ecological observations, which is suitable for comparison and analysis with traditional field datasets. Besides, the satellite dataset provides more spatial details of cCO2. It is very enlightening for better understanding of the biogeochemical process associated with cCO2 in Lake Taihu. We believed this dataset would be very worth promoting to all researchers focusing on Lake Taihu.

mots-clés

MODIS;Lake Taihu;carbon dioxide;carbon emission;lake carbon cycle;dataset

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