Remote sensing technology in the study of lake carbon cycle: Opportunities and challenges

被引：0
作者：
Huang C. [1 ]
Yao L. [2 ,3 ]
Li J. [4 ]
Zhou C. [2 ,3 ]
Guo Y. [5 ]
Li Y. [1 ]
机构：
[1] School of Geography, Nanjing Normal University, Nanjing
[2] Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing
[3] Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou
[4] Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing
[5] Henan Agricultural University, College of Resources and Environmental Sciences, Zhengzhou
来源：
National Remote Sensing Bulletin | 2022年 / 26卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Big data and artificial intelligence; Biogeochemistry; Greenhouse gase; Lake carbon cycle; Remote sensing; Water environment;
D O I：
10.11834/jrs.20221220
中图分类号：
学科分类号：
摘要：
lake carbon cycle is an important segment in the global carbon cycle. Growing attention has been received to lake carbon cycle for its virtual effect on the global carbon cycle and climate change. However, comprehensive monitoring and assessment of the global lake carbon cycle is still challenging due to the fragmentary distribution and diversity in ecology, type and climatic zone of lake. Remote sensing technology with advantages of large area continuously synchronous observation could conquer the limitations of conventional observation method, supporting the research of global lake carbon cycle with huge of observation data. Meanwhile, the estimation of organic carbon source and composition via the remote sensing technology could be combined with biogeochemical technology for the advantage of spectral detection by remote sensing. In this paper, recent studies about the remote sensing application and research on lake basin and water were reviewed based on the active demand of remote sensing in the lake carbon cycle. The application of remote sensing in a geography of lake carbon cycling was proposed due to the highly variable among lakes within basin characteristics. Much more precision and higher spatial resolution results of land use, vegetation canopy, primary productivity, soil properties, population density and other watershed attribute data from remote sensing should be considered in geography of lake carbon cycling to improve the estimation of carbon input in lake. The remote sensing retrieval of particulate and dissolved organic carbon concentration in the lake water have been widely used, yet the carbon pool estimation is flimsy for the difficulty in the acquirement of carbon vertical distribution. Meanwhile, the sources of organic carbon significantly affect the turnover time of organic carbon, presenting the short turnover time of endogenous organic carbon and relative long turnover time of terrestrial organic carbon. The remote sensing should be cooperatively estimated endogenous and terrestrial organic carbon with isotopic geochemistry technology, which can distinguish the source of organic carbon effectively. The retrieval algorithms of inorganic carbon, such as CO2 and CH4, are being developed by the active and passive remote sensing. The black carbon from incomplete combustion of fossil fuel and biomass is a higher aromatic content and different from other types of organic carbon (such as: terrestrial, endogenous organic carbon) should be taken as a new inversion parameter from remote sensing. The estimation of physicochemical characteristics of lake water, which significantly affected the lake carbon cycle, should be concerned and combined in the research of lake carbon cycle. The virtual sensors with high temporal, spectral and spatial resolution should be established due to the limitation of current remote sensing satellite data. Multi-source remote sensing data fusion is a recommendable method to overcome the limitation application of remote sensing in lake carbon cycle due to the exclusive highly temporal, spectral or spatial resolution. The opportunities and challenges of remote sensing application in the lake carbon cycle were discussed according to biogeochemical processes of carbon in the lake and the recent advances of big data and artificial intelligence in remote sensing technology, as well as the development of lake carbon cycle studies. © 2022, Science Press. All right reserved.
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页码：49 / 67
页数：18
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