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黄昌春，姚凌，李俊生，周成虎，郭宇龙，李云梅.2022.湖泊碳循环研究中遥感技术的机遇与挑战.遥感学报，26（1）： 49-67
Huang C C，Yao L，Li J S，Zhou C H，Guo Y L and Li Y M. 2022. Remote sensing technology in the study of lake carbon cycle： Opportunities and challenges. National Remote Sensing Bulletin， 26（1）：49-67
湖泊碳循环是全球碳循环过程中的重要环节，随着全球碳循环研究的不断深入，湖泊碳循环对全球碳循环的影响，以及其对全球气候变化的调节作用越来越受到关注。然而，由于湖泊分布的破碎性（大于0.002 km
2
的湖泊约有1.17×10
8
个，并零星地分布在全球）和多样性（流域生态多样性，湖泊类型多样性，分布的气候带多样性等），使得全面监测和研究全球湖泊碳循环具有较大的挑战性。具有大面积同步连续观测优势的遥感技术可以克服传统观测方法的局限，可为全球湖泊碳循环研究提供大面积同步观测数据的支撑。同时，由于光谱在物质识别和探测方面的优势，使得遥感技术在有机质类型反演方面与地球化学方法存在结合的可能。本文回顾了目前水环境遥感研究中与湖泊碳循环相关的湖泊不同类型碳浓度、水体理化参数等遥感反演算法及其应用的现状，结合湖泊碳循环中有机碳迁移转化的生物地球化学过程，以及湖泊碳循环研究、遥感大数据和人工智能的发展，探讨了湖泊碳循环研究中遥感技术应用的机遇和挑战。
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 CO
2
and CH
4
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.
湖泊碳循环遥感生物地球化学大数据与人工智能水环境温室气体
lake carbon cycleremote sensingbiogeochemistrybig data and artificial intelligencewater environmentgreenhouse gase
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