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段洪涛，曹志刚，沈明，马金戈，齐天赐.2022.湖泊遥感研究进展与展望.遥感学报，26（1）： 3-18
Duan H T，Cao Z G，Shen M， Ma J G and Qi T C. 2022. Review of lake remote sensing research. National Remote Sensing Bulletin， 26（1）：3-18
湖泊遥感作为一门新型交叉学科，是湖泊科学和遥感科学的重要分支。本文探讨了湖泊遥感科学的研究对象、内容和方法，通过梳理国内外总体研究进展，总结出湖泊遥感的5个发展趋势：（1）关注问题，从兴趣导向发展到问题导向；（2）观测手段，从地基遥感/中分辨率卫星发展到高分辨率/高光谱/无人机；（3）算法算力，从单机版经验/机理模型发展到云计算机器算法；（4）研究维度，从水体表层发展到垂向剖面；（5）研究区域，从单一/区域湖泊发展到国家/大洲/全球尺度。最后，指出了湖泊遥感学科未来的重点发展方向：（1）研制满足湖泊观测特点的静止卫星或小卫星集群；（2）发展湖泊水色遥感标准算法，建设全球湖泊卫星遥感监测网络；（3）加强全球变化背景下的湖泊盐度、温度和碳循环等遥感研究；（4）开展全流域统筹的湖泊天空地遥感监测和模拟研究。
Lake remote sensing is an important branch of limnology and remote sensing science as a new interdisciplinary discipline. In general
lake remote sensing includes water color remote sensing
lake water environment remote sensing
and lake hydrology remote sensing. The key to lake remote sensing is to initially study the lake’s specific problems to address individual or a type of sensitive factors. To accomplish the monitoring of these lake factors through remote sensing
the scientific questions about the preprocessing of remote sensing data
atmospheric correction
algorithm development
and validation
as well as the reconstruction of a long time series data record
were introduced one by one.
With the reviews in published studies
this research discussed the research object
content
and method of lake remote sensing science and summarized five development progresses of lake remote sensing by reviewing the research progress
as follows:
(1) Concerns: from interest-oriented to problem-oriented. Lake remote sensing has gradually expanded from the water color remote sensing to water environment
water ecology
and hydrology remote sensing
with diverse research fields.
(2) Observation equipment: from ground-based remote sensing and medium resolution satellite to high-resolution/hyperspectral/drone. Satellite instruments for remote sensing of lakes have developed from scratch and achieved a development stage from low to high spatial
radiometric
and spectral resolution.
(3) Algorithm and computing force: from stand-alone experience/mechanism model to machine learning algorithms based on cloud computing. The machine learning models have been used to retrieve water constituents in the lakes with complicated optical properties
which were difficult for traditional empirical and semi-analytical algorithms.
(4) Research dimension: from surface to vertical profile. The remote sensing reflectance was related to the vertical distribution of lake water quality parameters (e.g.
algae biomass) in the water column; aquatic remote sensing has been gradually developed in a 3D scale.
(5) Spatial coverage: from individual/regional lake to national/continental/global scale. A number of aquatic parameters in national and global lakes
such as lake boundary
algae blooms
and water clarity
have been monitored by remote sensing data through cloud computing platforms.
Finally
the future development directions of lake remote sensing are identified
as follows: (1) Launch geostationary satellites or small satellite constellation to satisfy the requirements for lake observation; (2) Develop standard algorithms of lake remote sensing and establish the global lake satellite remote sensing monitoring network; (3) strengthen remote sensing of salinity
temperature
and carbon cycle in lakes under the background of global change; (4) conduct research with respect to satellites
aircraft
and ground remote sensing monitoring and simulation over lakes and the entire watershed.
湖泊遥感机器学习监测网络卫星星座可持续发展大数据水色遥感
lake remote sensingmachine learningnetworksatellite constellationSDGbig datawater color remote sensing
Adrian R, O’Reilly C M, Zagarese H, Baines S B, Hessen D O, Keller W, Livingstone D M, Sommaruga R, Straile D, Van Donk E, Weyhenmeyer G A and Winder M. 2009. Lakes as sentinels of climate change. Limnology and Oceanography, 54(6part2): 2283-2297 [DOI: 10.4319/lo.2009.54.6_part_2.2283http://dx.doi.org/10.4319/lo.2009.54.6_part_2.2283]
Bailey S W, Franz B A and Werdell P J. 2010. Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing. Optics Express, 18(7): 7521-7527 [DOI: 10.1364/OE.18.007521http://dx.doi.org/10.1364/OE.18.007521]
Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp M L and Bareth G. 2015. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39: 79-87 [DOI: 10.1016/j.jag.2015.02.012http://dx.doi.org/10.1016/j.jag.2015.02.012]
Bi S, Li Y M, Lyu H, Mu M, Xu J, Lei S H, Miao S, Hong T L and Zhou L. 2019. Quantifying spatiotemporal dynamics of the column-integrated algal biomass in nonbloom conditions based on OLCI data: a case study of Lake Dianchi, China. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 7447-7459 [DOI: 10.1109/tgrs.2019.2913401http://dx.doi.org/10.1109/tgrs.2019.2913401]
Brezonik P L, Olmanson L G, Finlay J C and Bauer M E. 2015. Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters. Remote Sensing of Environment, 157: 199-215 [DOI: 10.1016/j.rse.2014.04.033http://dx.doi.org/10.1016/j.rse.2014.04.033]
Cao Z G, Duan H T, Feng L, Ma R H and Xue K. 2017. Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales. Remote Sensing of Environment, 192: 98-113 [DOI: 10.1016/j.rse.2017.02.007http://dx.doi.org/10.1016/j.rse.2017.02.007]
Cao Z G, Duan H T, Shen M, Ma R H, Xue K, Liu D and Xiao Q T. 2018. Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake. International Journal of Applied Earth Observation and Geoinformation, 64: 256-265 [DOI: 10.1016/j.jag.2017.09.012http://dx.doi.org/10.1016/j.jag.2017.09.012]
Cao Z G, Ma R H, Duan H T, Pahlevan N, Melack J, Shen M and Xue K. 2020. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sensing of Environment, 248: 111974 [DOI: 10.1016/j.rse.2020.111974http://dx.doi.org/10.1016/j.rse.2020.111974]
Cao Z G, Ma R H, Duan H T and Xue K. 2019. Effects of broad bandwidth on the remote sensing of inland waters: implications for high spatial resolution satellite data applications. ISPRS Journal of Photogrammetry and Remote Sensing, 153: 110-122 [DOI: 10.1016/j.isprsjprs.2019.05.001http://dx.doi.org/10.1016/j.isprsjprs.2019.05.001]
Chang N B, Xuan Z M and Yang Y J. 2013. Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models. Remote Sensing of Environment, 134: 100-110 [DOI: 10.1016/j.rse.2013.03.002http://dx.doi.org/10.1016/j.rse.2013.03.002]
Coffer M M, Schaeffer B A, Darling J A, Urquhart E A and Salls W B. 2020. Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing. Ecological Indicators, 111: 105976 [DOI: 10.1016/j.ecolind.2019.105976http://dx.doi.org/10.1016/j.ecolind.2019.105976]
Dekker A G and Peters S W M. 1993. The use of the Thematic Mapper for the analysis of eutrophic lakes: a case study in the Netherlands. International Journal of Remote Sensing, 14(5): 799-821 [DOI: 10.1080/01431169308904379http://dx.doi.org/10.1080/01431169308904379]
Du J, Jacinthe P A, Zhou H H, Xiang X Y, Zhao B Y, Wang M and Song K S. 2020. Monitoring of water surface temperature of Eurasian large lakes using MODIS land surface temperature product. Hydrological Processes, 34(16): 3582-3595 [DOI: 10.1002/hyp.13830http://dx.doi.org/10.1002/hyp.13830]
Duan H T, Feng L, Ma R H, Zhang Y C and Loiselle S A. 2014a. Variability of particulate organic carbon in inland waters observed from MODIS Aqua imagery. Environmental Research Letters, 9(8): 084011 [DOI: 10.1088/1748-9326/9/8/084011http://dx.doi.org/10.1088/1748-9326/9/8/084011]
Duan H T, Luo J H, Cao Z G, Xue K, Xiao Q T and Liu D. 2019. Progress in remote sensing of aquatic environments at the watershed scale. Progress in Geography, 38(8): 1182-1195
段洪涛, 罗菊花, 曹志刚, 薛坤, 肖启涛, 刘东. 2019. 流域水环境遥感研究进展与思考. 地理科学进展, 38(8): 1182-1195 [DOI: 10.18306/dlkxjz.2019.08.007http://dx.doi.org/10.18306/dlkxjz.2019.08.007]
Duan H T, Ma R H, Loiselle S A, Shen Q S, Yin H B and Zhang Y C. 2014b. Optical characterization of black water blooms in eutrophic waters. Science of the Total Environment, 482-483: 174-183 [DOI: 10.1016/j.scitotenv.2014.02.113http://dx.doi.org/10.1016/j.scitotenv.2014.02.113]
Duan H T, Ma R H, Xu X F, Kong F X, Zhang S X, Kong W J, Hao J Y and Shang L L. 2009. Two-decade reconstruction of algal blooms in China’s Lake Taihu. Environmental Science and Technology, 43(10): 3522-3528 [DOI: 10.1021/es8031852http://dx.doi.org/10.1021/es8031852]
Duan H T, Wan N S, Qiu Y G, Liu G, Chen Q, Luo J H, Chen Y and Qi T C. 2020. Discussions and practices on the framework of monitoring system in eutrophic lakes and reservoirs. Journal of Lake Sciences, 32(5): 1396-1405
段洪涛, 万能胜, 邱银国, 刘刚, 陈青, 罗菊花, 陈远, 齐天赐. 2020. 富营养化湖库天—空—地一体化监控平台系统设计与实践. 湖泊科学, 32(5): 1396-1405 [DOI: 10.18307/2020.0513http://dx.doi.org/10.18307/2020.0513]
Fan Y Z, Li W, Gatebe C K, Jamet C, Zibordi G, Schroeder T and Stamnes K. 2017. Atmospheric correction over coastal waters using multilayer neural networks. Remote Sensing of Environment, 199: 218-240 [DOI: 10.1016/j.rse.2017.07.016http://dx.doi.org/10.1016/j.rse.2017.07.016]
Feng L. 2021. Key issues in detecting lacustrine cyanobacterial bloom using satellite remote sensing. Journal of Lake Sciences, 33(3): 647-652
冯炼. 2021. 卫星遥感解译湖泊蓝藻水华的几个关键问题探讨. 湖泊科学, 33(3): 647-652
Feng L, Hou X J, Li J S and Zheng Y. 2018. Exploring the potential of Rayleigh-corrected reflectance in coastal and inland water applications: a simple aerosol correction method and its merits. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 52-64 [DOI: 10.1016/j.isprsjprs.2018.08.020http://dx.doi.org/10.1016/j.isprsjprs.2018.08.020]
Feng L, Hu C M, Chen X L, Tian L Q and Chen L Q. 2012. Human induced turbidity changes in Poyang Lake between 2000 and 2010: observations from MODIS. Journal of Geophysical Research: Oceans, 117(C7): C07006 [DOI: 10.1029/2011jc007864http://dx.doi.org/10.1029/2011jc007864]
Gao Y, Jia J J, Lu Y, Yang T T, Lyu S, Shi K, Zhou F and Yu G R. 2021. Determining dominating control mechanisms of inland water carbon cycling processes and associated gross primary productivity on regional and global scales. Earth-Science Reviews, 213: 103497 [DOI: 10.1016/j.earscirev.2020.103497http://dx.doi.org/10.1016/j.earscirev.2020.103497]
Gordon H R and Wang M H. 1994. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. Applied Optics, 33(3): 443-452 [DOI: 10.1364/AO.33.000443http://dx.doi.org/10.1364/AO.33.000443]
Guanter L, Del Carmen González‐Sanpedro M and Moreno J. 2007. A method for the atmospheric correction of ENVISAT/MERIS data over land targets. International Journal of Remote Sensing, 28(3/4): 709-728 [DOI: 10.1080/01431160600815525http://dx.doi.org/10.1080/01431160600815525]
He X Q, Bai Y, Pan D L, Tang J W and Wang D F. 2012. Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters. Optics Express, 20(18): 20754-20770 [DOI: 10.1364/OE.20.020754http://dx.doi.org/10.1364/OE.20.020754]
Ho J C, Michalak A M and Pahlevan N. 2019. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature, 574(7780): 667-670 [DOI: 10.1038/s41586-019-1648-7http://dx.doi.org/10.1038/s41586-019-1648-7]
Ho J C, Stumpf R P, Bridgeman T B and Michalak A M. 2017. Using Landsat to extend the historical record of lacustrine phytoplankton blooms: a Lake Erie case study. Remote Sensing of Environment, 191: 273-285 [DOI: 10.1016/j.rse.2016.12.013http://dx.doi.org/10.1016/j.rse.2016.12.013]
Hou X J, Feng L, Duan H T, Chen X L, Sun D Y and Shi K. 2017. Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China. Remote Sensing of Environment, 190: 107-121 [DOI: 10.1016/j.rse.2016.12.006http://dx.doi.org/10.1016/j.rse.2016.12.006]
Hu C M. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment, 113(10): 2118-2129 [DOI: 10.1016/j.rse.2009.05.012http://dx.doi.org/10.1016/j.rse.2009.05.012]
Hu C M, Lee Z, Ma R H, Yu K, Li D Q and Shang S L. 2010. Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. Journal of Geophysical Research: Oceans, 115(C4): C04002 [DOI: 10.1029/2009jc005511http://dx.doi.org/10.1029/2009jc005511]
Huang C C, Guo Y L, Yang H, Li Y M, Zou J, Zhang M L, Lyu H, Zhu A X and Huang T. 2015. Using remote sensing to track variation in phosphorus and its interaction with chlorophyll-a and suspended sediment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8): 4171-4180 [DOI: 10.1109/JSTARS.2015.2438293http://dx.doi.org/10.1109/JSTARS.2015.2438293]
Hunter P D, Tyler A N, Présing M, Kovács A W and Preston T. 2008. Spectral discrimination of phytoplankton colour groups: the effect of suspended particulate matter and sensor spectral resolution. Remote Sensing of Environment, 112(4): 1527-1544 [DOI: 10.1016/j.rse.2007.08.003http://dx.doi.org/10.1016/j.rse.2007.08.003]
IOCCG. 1998. Minimum requirements for an operational, ocean-colour sensor for the open ocean//Morel A, ed. Reports of the International Ocean-Colour Coordinating Group. Dartmouth, Canada: IOCCG
IOCCG. 2010. Atmospheric correction for remotely-sensed ocean-colour products//Wang M H, ed. Reports of the International Ocean-Colour Coordinating Group. Dartmouth, Canada: IOCCG
IOCCG. 2018. Earth observations in support of global water quality monitoring//Greb S, Dekker A and Binding C, eds. Reports of the International Ocean-Colour Coordinating Group. Dartmouth, Canada: IOCCG
Kahru M. 2017. Ocean productivity from space: commentary. Global Biogeochemical Cycles, 31(1): 214-216 [DOI: 10.1002/2016GB005582http://dx.doi.org/10.1002/2016GB005582]
Ke C Q. 2004. A review of monitoring lake environment change by means of remote sensing. Transactions of Oceanology and Limnology, (4): 81-86
柯长青. 2004. 湖泊遥感研究进展. 海洋湖沼通报, (4): 81-86 [DOI: 10.13984/j.cnki.cn37-1141.2004.04.014]
Kravitz J, Matthews M, Lain L, Fawcett S and Bernard S. 2021. Potential for high fidelity global mapping of common inland water quality products at high spatial and temporal resolutions based on a synthetic data and machine learning approach. Frontiers in Environmental Science, 9: 587660 [DOI: 10.3389/fenvs.2021.587660http://dx.doi.org/10.3389/fenvs.2021.587660]
Kutser T, Pierson D C, Kallio K Y, Reinart A and Sobek S. 2005. Mapping lake CDOM by satellite remote sensing. Remote Sensing of Environment, 94(4): 535-540 [DOI: 10.1016/j.rse.2004.11.009http://dx.doi.org/10.1016/j.rse.2004.11.009]
Kwon Y S, Pyo J, Kwon Y H, Duan H T, Cho K H and Park Y. 2020. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir. Remote Sensing of Environment, 236: 111517 [DOI: 10.1016/j.rse.2019.111517http://dx.doi.org/10.1016/j.rse.2019.111517]
Lee Z, Carder K L and Arnone R A. 2002. Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Applied Optics, 41(27): 5755-5772 [DOI: 10.1364/AO.41.005755http://dx.doi.org/10.1364/AO.41.005755]
Lee Z, Shang S L, Hu C M, Du K P, Weidemann A, Hou W L, Lin J F and Lin G. 2015. Secchi disk depth: a new theory and mechanistic model for underwater visibility. Remote Sensing of Environment, 169: 139-149 [DOI: 10.1016/j.rse.2015.08.002http://dx.doi.org/10.1016/j.rse.2015.08.002]
Li J, Ma R H, Xue K, Zhang Y C and Loiselle S. 2018. A remote sensing algorithm of column-integrated algal biomass covering algal bloom conditions in a shallow eutrophic lake. Isprs International Journal of Geo-Information, 7(12): 466 [DOI: 10.3390/ijgi7120466http://dx.doi.org/10.3390/ijgi7120466]
Li J, Zhang Y C, Ma R H, Duan H T, Loiselle S, Xue K and Liang Q C. 2017. Satellite-based estimation of column-integrated algal biomass in nonalgae bloom conditions: a case study of Lake Chaohu, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(2): 450-462 [DOI: 10.1109/JSTARS.2016.2601083http://dx.doi.org/10.1109/JSTARS.2016.2601083]
Li J L and Sheng Y W. 2012. An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas. International Journal of Remote Sensing, 33(16): 5194-5213 [DOI: 10.1080/01431161.2012.657370http://dx.doi.org/10.1080/01431161.2012.657370]
Li X W. 2006. Remote sensing science and quantitative remote sensing. Science Focus, 1(5): 45
李小文. 2006. 遥感科学与定量遥感. 科学观察, 1(5): 45
Lillesand T M, Johnson W L, Deuell R L, Lindstrom O M and Meisner D E. 1983. Use of Landsat data to predict the trophic state of Minnesota lakes. Photogrammetric Engineering and Remote Sensing, 49(2): 219-229
Liu C, Zhu L P, Li J S, Wang J B, Ju J T, Qiao B J, Ma Q F and Wang S L. 2021a. The increasing water clarity of Tibetan lakes over last 20 years according to MODIS data. Remote Sensing of Environment, 253: 112199 [DOI: 10.1016/j.rse.2020.112199http://dx.doi.org/10.1016/j.rse.2020.112199]
Liu C, Zhu L P, Wang J B, Ju J T, Ma Q F, Qiao B J, Wang Y, Xu T, Chen H, Kou Q Q, Zhang R and Kai J L. 2021b. In-situ water quality investigation of the lakes on the tibetan plateau. Science Bulletin, 66(17): 1727-1730 [DOI: 10.1016/j.scib.2021.04.024http://dx.doi.org/10.1016/j.scib.2021.04.024]
Liu D, Duan H T, Loiselle S, Hu C M, Zhang G Q, Li J L, Yang H, Thompson J R, Cao Z G, Shen M, Ma R H, Zhang M and Han W X. 2020. Observations of water transparency in China’s lakes from space. International Journal of Applied Earth Observation and Geoinformation, 92: 102187 [DOI: 10.1016/j.jag.2020.102187http://dx.doi.org/10.1016/j.jag.2020.102187]
Liu D, Yu S J, Cao Z G, Qi T C and Duan H T. 2021c. Process-oriented estimation of column-integrated algal biomass in eutrophic lakes by MODIS/Aqua. International Journal of Applied Earth Observation and Geoinformation, 99: 102321 [DOI: 10.1016/j.jag.2021.102321http://dx.doi.org/10.1016/j.jag.2021.102321]
Liu H Z, He X Q, Li Q Q, Kratzer S, Wang J J, Shi T Z, Hu Z W, Yang C, Hu S B, Zhou Q M and Wu G F. 2021d. Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing. Remote Sensing of Environment, 258: 112404 [DOI: 10.1016/j.rse.2021.112404http://dx.doi.org/10.1016/j.rse.2021.112404]
Liu Y B, Wu G P, Zhao X S, Fan X W, Pan X, Gan G J, Liu J W, Guo R F, Zhou H, Wang Y, Wang R N and Cui Y F. 2020. Remote sensing for watershed hydrology: issues and challenges. Advances in Earth Science, 35(5): 488-496
刘元波, 吴桂平, 赵晓松, 范兴旺, 潘鑫, 甘国靖, 刘永伟, 郭瑞芳, 周晗, 王颖, 王若男, 崔逸凡. 2020. 流域水文遥感的科学问题与挑战. 地球科学进展, 35(5): 488-496 [DOI: 10.11867/j.issn.1001-8166.2020.045http://dx.doi.org/10.11867/j.issn.1001-8166.2020.045]
Liu Z W, Su Y L and Yang L. 2020. Limnology is a multidisciplinary and integrative science for studying inland waters: with special reference to the challenges and opportunities for the development of limnology in China. Journal of Lake Sciences, 32(5): 1244-1253
刘正文, 苏雅玲, 杨柳. 2020. 湖沼学是研究内陆水体的多学科整合科学——兼论我国湖沼学发展面临的挑战与机遇. 湖泊科学, 32(5): 1244-1253 [DOI: 10.18307/2020.0502http://dx.doi.org/10.18307/2020.0502]
Luo J H, Duan H T, Ma R H, Jin X L, Li F, Hu W P, Shi K and Huang W J. 2017. Mapping species of submerged aquatic vegetation with multi-seasonal satellite images and considering life history information. International Journal of Applied Earth Observation and Geoinformation, 57: 154-165 [DOI: 10.1016/j.jag.2016.11.007http://dx.doi.org/10.1016/j.jag.2016.11.007]
Ma R H, Duan H T, Hu C M, Feng X Z, Li A N, Ju W M, Jiang J H and Yang G S. 2010. A half-century of changes in China’s lakes: global warming or human influence?. Geophysical Research Letters, 37(24): L24106 [DOI: 10.1029/2010gl045514http://dx.doi.org/10.1029/2010gl045514]
Ma R H, Yang G S, Duan H T, Jiang J H, Wang S M, Feng X Z, Li A N, Kong F X, Xue B, Wu J L and Li S J. 2011. China’s lakes at present: number, area and spatial distribution. Science China Earth Sciences, 54(2): 283-289
马荣华, 杨桂山, 段洪涛, 姜加虎, 王苏民, 冯学智, 李爱农, 孔繁翔, 薛滨, 吴敬禄, 李世杰. 2011. 中国湖泊的数量、面积与空间分布. 中国科学: 地球科学, 41(3): 394-401 [DOI: 10.1360/zd-2011-41-3-394http://dx.doi.org/10.1360/zd-2011-41-3-394]
Ma R H, Zhang Y C and Duan H T. 2016. The status and development of the non-traditional lake water color remote sensing. Journal of Lake Sciences, 28(2): 237-245
马荣华, 张玉超, 段洪涛. 2016. 非传统湖泊水色遥感的现状与发展. 湖泊科学, 28(2): 237-245 [DOI: 10.18307/2016.0201http://dx.doi.org/10.18307/2016.0201]
Ma Y, Xu N, Zhang W H, Wang X H, Sun J Y, Feng X J and Sun Y. 2020b. Increasing water levels of global lakes between 2003 and 2009. IEEE Geoscience and Remote Sensing Letters, 17(2): 187-191 [DOI: 10.1109/LGRS.2019.2920387http://dx.doi.org/10.1109/LGRS.2019.2920387]
Meissner T, Wentz F J, Scott J and Vazquez-Cuervo J. 2016. Sensitivity of ocean surface salinity measurements from spaceborne l-band radiometers to ancillary sea surface temperature. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7105-7111 [DOI: 10.1109/TGRS.2016.2596100http://dx.doi.org/10.1109/TGRS.2016.2596100]
Moore T S, Churnside J H, Sullivan J M, Twardowski M S, Nayak A R, McFarland M N, Stockley N D, Gould R W, Johengen T H and Ruberg S A. 2019. Vertical distributions of blooming cyanobacteria populations in a freshwater lake from LIDAR observations. Remote Sensing of Environment, 225: 347-367 [DOI: 10.1016/j.rse.2019.02.025http://dx.doi.org/10.1016/j.rse.2019.02.025]
Morel A and Prieur L. 1977. Analysis of variations in ocean color. Limnology and Oceanography, 22(4): 709-722 [DOI: 10.4319/lo.1977.22.4.0709http://dx.doi.org/10.4319/lo.1977.22.4.0709]
Mouw C B, Greb S, Aurin D, DiGiacomo P M, Lee Z, Twardowski M, Binding C, Hu C M, Ma R H, Moore T, Moses W and Craig S E. 2015. Aquatic color radiometry remote sensing of coastal and inland waters: challenges and recommendations for future satellite missions. Remote Sensing of Environment, 160: 15-30 [DOI: 10.1016/j.rse.2015.02.001http://dx.doi.org/10.1016/j.rse.2015.02.001]
Ouyang Z T, Shao C L, Chu H S, Becker R, Bridgeman T, Stepien C A, John R and Chen J Q. 2017. The effect of algal blooms on carbon emissions in western Lake Erie: an integration of remote sensing and eddy covariance measurements. Remote Sensing, 9(1): 44 [DOI: 10.3390/rs9010044http://dx.doi.org/10.3390/rs9010044]
Pahlevan N, Sarkar S, Franz B A, Balasubramanian S V and He J. 2017a. Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: demonstrations and validations. Remote Sensing of Environment, 201: 47-56 [DOI: 10.1016/j.rse.2017.08.033http://dx.doi.org/10.1016/j.rse.2017.08.033]
Pahlevan N, Mangin A, Balasubramanian S V, Smith B, Alikas K, Arai K, Barbosa C, Bélanger S, Binding C, Bresciani M, Giardino C, Gurlin D, Fan Y Z, Harmel T, Hunter P, Ishikaza J, Kratzer S, Lehmann M K, Ligi M, Ma R H, Martin-Lauzer F R, Olmanson L, Oppelt N, Pan Y Q, Peters S, Reynaud N, de Carvalho L A S, Simis S, Spyrakos E, Steinmetz F, Stelzer K, Sterckx S, Tormos T, Tyler A, Vanhellemont Q and Warren M. 2021. ACIX-Aqua: a global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sensing of Environment, 258: 112366 [DOI: 10.1016/j.rse.2021.112366http://dx.doi.org/10.1016/j.rse.2021.112366]
Pahlevan N, Schott J R, Franz B A, Zibordi G, Markham B, Bailey S, Schaaf C B, Ondrusek M, Greb S and Strait C M. 2017b. Landsat 8 remote sensing reflectance (Rrs) products: evaluations, intercomparisons, and enhancements. Remote Sensing of Environment, 190: 289-301 [DOI: 10.1016/j.rse.2016.12.030http://dx.doi.org/10.1016/j.rse.2016.12.030]
Pahlevan N, Smith B, Binding C, Gurlin D, Li L, Bresciani M and Giardino C. 2020. Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters. Remote Sensing of Environment, 253: 112200 [DOI: 10.1016/j.rse.2020.112200http://dx.doi.org/10.1016/j.rse.2020.112200]
Palmer S C J, Kutser T and Hunter P D. 2015. Remote sensing of inland waters: challenges, progress and future directions. Remote Sensing of Environment, 157: 1-8 [DOI: 10.1016/j.rse.2014.09.021http://dx.doi.org/10.1016/j.rse.2014.09.021]
Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422 [DOI: 10.1038/nature20584http://dx.doi.org/10.1038/nature20584]
Pi X H, Feng L, Li W F, Liu J G, Kuang X X, Shi K, Qi W, Chen D L and Tang J. 2021. Chlorophyll-a concentrations in 82 large alpine lakes on the Tibetan Plateau during 2003-2017: temporal–spatial variations and influencing factors. International Journal of Digital Earth, 14(6): 714-735 [DOI: 10.1080/17538947.2021.1872722http://dx.doi.org/10.1080/17538947.2021.1872722]
Pickens A H, Hansen M C, Hancher M, Stehman S V, Tyukavina A, Potapov P, Marroquin B and Sherani Z. 2020. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sensing of Environment, 243: 111792 [DOI: 10.1016/j.rse.2020.111792http://dx.doi.org/10.1016/j.rse.2020.111792]
Pu J J and Wang C Y. 1979. Studies on dynamics of some river deltas and lakes using Landsat-MSS imagery. Acta Geographica Sinica, 34(1): 43-54
濮静娟, 王长耀. 1979. 利用卫星遥感资料研究河口三角洲、湖泊的动态. 地理学报, 34(1): 43-54
Qi L, Hu C M, Duan H T, Cannizzaro J and Ma R H. 2014. A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: theoretical basis and practical considerations. Remote Sensing of Environment, 154: 298-317 [DOI: 10.1016/j.rse.2014.08.026http://dx.doi.org/10.1016/j.rse.2014.08.026]
Qi T C, Xiao Q T, Cao Z G, Shen M, Ma J G, Liu D and Duan H T. 2020. Satellite estimation of dissolved carbon dioxide concentrations in China’s Lake Taihu. Environmental Science and Technology, 54(21): 13709-13718 [DOI: 10.1021/acs.est.0c04044http://dx.doi.org/10.1021/acs.est.0c04044]
Qin B Q. 2020. Shallow lake limnology and control of eutrophication in Lake Taihu. Journal of Lake Sciences, 32(5): 1229-1243
秦伯强. 2020. 浅水湖泊湖沼学与太湖富营养化控制研究. 湖泊科学, 32(5): 1229-1243 [DOI: 10.18307/2020.0501http://dx.doi.org/10.18307/2020.0501]
Ran L S, Butman D E, Battin T J, Yang X K, Tian M Y, Duvert C, Hartmann J, Geeraert N and Liu S D. 2021. Substantial decrease in CO2 emissions from Chinese inland waters due to global change. Nature Communications, 12(1): 1730 [DOI: 10.1038/s41467-021-21926-6http://dx.doi.org/10.1038/s41467-021-21926-6]
Ruddick K, Vanhellemont Q, Dogliotti A, Nechad B, Pringle N and Van der Zande D. 2016. New opportunities and challenges for high resolution remote sensing of water colour//Ocean Optics XXIII. 2016. Victoria, CB, Canada: [s.n.]
Ruddick K G, Ovidio F and Rijkeboer M. 2000. Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters. Applied Optics, 39(6): 897-912 [DOI: 10.1364/AO.39.000897http://dx.doi.org/10.1364/AO.39.000897]
Sagan V, Peterson K T, Maimaitijiang M, Sidike P, Sloan J, Greeling B A, Maalouf S and Adams C. 2020. Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Science Reviews, 205: 103187 [DOI: 10.1016/j.earscirev.2020.103187http://dx.doi.org/10.1016/j.earscirev.2020.103187]
Shen J, Liu Z W, Yang X D, Zhang Y L, Zhu G W, Wu Q L, Jiang H L, Xue B. 2020. Limnology. Beijing: Science Press
沈吉, 刘正文, 羊向东, 张运林, 朱广伟, 吴庆龙, 江和龙, 薛滨. 2020. 湖泊学. 北京: 高等教育出版社
Shen M, Duan H T, Cao Z G, Xue K, Loiselle S and Yesou H. 2017. Determination of the downwelling diffuse attenuation coefficient of lake water with the Sentinel-3A OLCI. Remote Sensing, 9(12): 1246 [DOI: 10.3390/rs9121246http://dx.doi.org/10.3390/rs9121246]
Shen M, Duan H T, Cao Z G, Xue K, Qi T C, Ma J G, Liu D, Song K S, Huang C L and Song X Y. 2020. Sentinel-3 OLCI observations of water clarity in large lakes in eastern China: implications for SDG 6.3.2 evaluation. Remote Sensing of Environment, 247: 111950 [DOI: 10.1016/j.rse.2020.111950http://dx.doi.org/10.1016/j.rse.2020.111950]
Shi K, Zhang Y L, Song K S, Liu M L, Zhou Y Q, Zhang Y B, Li Y, Zhu G W and Qin B Q. 2019. A semi-analytical approach for remote sensing of trophic state in inland waters: bio-optical mechanism and application. Remote Sensing of Environment, 232: 111349 [DOI: 10.1016/j.rse.2019.111349http://dx.doi.org/10.1016/j.rse.2019.111349]
Shi K, Zhang Y L, Xu H, Zhu G W, Qin B Q, Huang C C, Liu X H, Zhou Y Q and Lv H. 2015. Long-term satellite observations of microcystin concentrations in Lake Taihu during cyanobacterial bloom periods. Environmental Science and Technology, 49(11): 6448-6456 [DOI: 10.1021/es505901ahttp://dx.doi.org/10.1021/es505901a]
Shi K, Zhang Y L, Zhang Y B, Qin B Q and Zhu G W. 2020. Understanding the long-term trend of particulate phosphorus in a cyanobacteria-dominated lake using MODIS-Aqua observations. Science of the Total Environment, 737: 139736 [DOI: 10.1016/j.scitotenv.2020.139736http://dx.doi.org/10.1016/j.scitotenv.2020.139736]
Simis S G H, Peters S W M and Gons H J. 2005. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnology and Oceanography, 50(1): 237-245 [DOI: 10.4319/lo.2005.50.1.0237http://dx.doi.org/10.4319/lo.2005.50.1.0237]
Song C Q, Zhan P F and Ma R H. 2020. Progress in remote sensing study on lake hydrologic regime. Journal of Lake Sciences, 32(5): 1406-1420
宋春桥, 詹鹏飞, 马荣华. 2020. 湖泊水情遥感研究进展. 湖泊科学, 32(5): 1406-1420 [DOI: 10.18307/2020.0514http://dx.doi.org/10.18307/2020.0514]
Song K S, Fang C, Jacinthe P A, Wen Z D, Liu G, Xu X F, Shang Y X and Lyu L. 2021. Climatic versus anthropogenic controls of decadal trends (1983-2017) in algal blooms in lakes and reservoirs across China. Environmental Science and Technology, 55(5): 2929-2938 [DOI: 10.1021/acs.est.0c06480http://dx.doi.org/10.1021/acs.est.0c06480]
Song K S, Liu G, Wang Q, Wen Z D, Lyu L, Du Y X, Sha L W and Fang C. 2020. Quantification of lake clarity in China using Landsat OLI imagery data. Remote Sensing of Environment, 243: 111800 [DOI: 10.1016/j.rse.2020.111800http://dx.doi.org/10.1016/j.rse.2020.111800]
Song K S, Wen Z D, Liu G, Shang Y X, Zhao Y and Du Y X. 2018. The research progress of CDOM optical characteristics and remote sensing retrieval for inland waters. Journal of Jilin Normal University (Natural Science Edition), 39(4): 115-125
宋开山, 温志丹, 刘阁, 尚盈辛, 赵莹, 杜云霞. 2018. 内陆水体CDOM光学特性与遥感反演研究进展. 吉林师范大学学报(自然科学版), 39(4): 115-125 [DOI: 10.16862/j.cnki.issn1674-3873.2018.04.022http://dx.doi.org/10.16862/j.cnki.issn1674-3873.2018.04.022]
Steinmetz F, Deschamps P Y and Ramon D. 2011. Atmospheric correction in presence of sun glint: application to MERIS. Optics Express, 19(10): 9783-9800 [DOI: 10.1364/OE.19.009783http://dx.doi.org/10.1364/OE.19.009783]
Stumpf R P, Davis T W, Wynne T T, Graham J L, Loftin K A, Johengen T H, Gossiaux D, Palladino D and Burtner A. 2016. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria. Harmful Algae, 54: 160-173 [DOI: 10.1016/j.hal.2016.01.005http://dx.doi.org/10.1016/j.hal.2016.01.005]
Villa P, Bresciani M, Bolpagni R, Pinardi M and Giardino C. 2015. A rule-based approach for mapping macrophyte communities using multi-temporal aquatic vegetation indices. Remote Sensing of Environment, 171: 218-233 [DOI: 10.1016/j.rse.2015.10.020http://dx.doi.org/10.1016/j.rse.2015.10.020]
Wan Z M and Dozier J. 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 892-905 [DOI: 10.1109/36.508406http://dx.doi.org/10.1109/36.508406]
Wang M H. 2007. Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations. Applied Optics, 46(9): 1535-1547 [DOI: 10.1364/AO.46.001535http://dx.doi.org/10.1364/AO.46.001535]
Wang S L, Li J D, Zhang B, Shen Q, Zhang F F and Lu Z Y. 2016. A simple correction method for the MODIS surface reflectance product over typical inland waters in China. International Journal of Remote Sensing, 37(24): 6076-6096 [DOI: 10.1080/01431161.2016.1256508http://dx.doi.org/10.1080/01431161.2016.1256508]
Wang S L, Li J S, Zhang B, Spyrakos E, Tyler A N, Shen Q, Zhang F F, Kuster T, Lehmann M K, Wu Y H and Peng D L. 2018. Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index. Remote Sensing of Environment, 217: 444-460 [DOI: 10.1016/j.rse.2018.08.026http://dx.doi.org/10.1016/j.rse.2018.08.026]
Wang X W, Gong P, Zhao Y Y, Xu Y, Cheng X, Niu Z G, Luo Z C, Huang H B, Sun F D and Li X W. 2013. Water-level changes in China’s large lakes determined from ICESat/GLAS data. Remote Sensing of Environment, 132: 131-144 [DOI: 10.1016/j.rse.2013.01.005http://dx.doi.org/10.1016/j.rse.2013.01.005]
Woolway R I, Jennings E, Shatwell T, Golub M, Pierson D C and Maberly S C. 2021. Lake heatwaves under climate change. Nature, 589(7842): 402-407 [DOI: 10.1038/s41586-020-03119-1http://dx.doi.org/10.1038/s41586-020-03119-1]
Wrigley R C and Horne A J. 1974. Remote sensing and lake eutrophication. Nature, 250(5463): 213-214 [DOI: 10.1038/250213a0http://dx.doi.org/10.1038/250213a0]
Wulder M A, Loveland T R, Roy D P, Crawford C J, Masek J G, Woodcock C E, Allen R G, Anderson M C, Belward A S, Cohen W B, Dwyer J, Erb A, Gao F, Griffiths P, Helder D, Hermosilla T, Hipple J D, Hostert P, Hughes M J, Huntington J, Johnson D M, Kennedy R, Kilic A, Li Z, Lymburner L, McCorkel J, Pahlevan N, Scambos T A, Schaaf C, Schott J R, Sheng Y W, Storey J, Vermote E, Vogelmann J, White J C, Wynne R H and Zhu Z. 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225: 127-147 [DOI: 10.1016/j.rse.2019.02.015http://dx.doi.org/10.1016/j.rse.2019.02.015]
Xu J, Li Y M, Lyu H, Lei S H, Mu M, Bi S, Xu J F, Xu X G, Miao S, Li L L and Yan X C. 2021. Simultaneous inversion of concentrations of POC and its endmembers in lakes: a novel remote sensing strategy. Science of the Total Environment, 770: 145249 [DOI: 10.1016/j.scitotenv.2021.145249http://dx.doi.org/10.1016/j.scitotenv.2021.145249]
Xue K, Zhang Y C, Duan H T and Ma R H. 2017. Variability of light absorption properties in optically complex inland waters of Lake Chaohu, China. Journal of Great Lakes Research, 43(1): 17-31 [DOI: 10.1016/j.jglr.2016.10.006http://dx.doi.org/10.1016/j.jglr.2016.10.006]
Xue K, Zhang Y C, Duan H T, Ma R H, Loiselle S and Zhang M W. 2015. A remote sensing approach to estimate vertical profile classes of phytoplankton in a eutrophic lake. Remote Sensing, 7(11): 14403-14427 [DOI: 10.3390/rs71114403http://dx.doi.org/10.3390/rs71114403]
Yang G S, Ma R H, Zhang L, Jiang J H, Yao S C, Zhang M and Zeng H A. 2010. Lake status, major problems and protection strategy in China. Journal of Lake Sciences, 22(6): 799-810
杨桂山, 马荣华, 张路, 姜加虎, 姚书春, 张民, 曾海鳌. 2010. 中国湖泊现状及面临的重大问题与保护策略. 湖泊科学, 22(6): 799-810 [DOI: 10.18307/2010.0601http://dx.doi.org/10.18307/2010.0601]
Yueh S H, West R, Wilson W J, Li F K, Njoku E G and Rahmat-Samii Y. 2001. Error sources and feasibility for microwave remote sensing of ocean surface salinity. IEEE Transactions on Geoscience and Remote Sensing, 39(5): 1049-1060 [DOI: 10.1109/36.921423http://dx.doi.org/10.1109/36.921423]
Zhang B, Li J S, Shen Q, Wu Y H, Zhang F F, Wang S L, Yao Y, Guo L N and Yin Z Y. 2021. Recent research progress on long time series and large scale optical remote sensing of inland water. National Remote Sensing Bulletin, 25(1): 37-52
张兵, 李俊生, 申茜, 吴艳红, 张方方, 王胜蕾, 姚月, 郭立男, 殷子瑶. 2021. 长时序大范围内陆水体光学遥感研究进展. 遥感学报, 25(1): 37-52 [DOI: 10.11834/jrs.20210570http://dx.doi.org/10.11834/jrs.20210570]
Zhang G Q. 2018. Changes in lakes on the Tibetan Plateau observed from satellite data and their responses to climate variations. Progress in Geography, 37(2): 214-223
张国庆. 2018. 青藏高原湖泊变化遥感监测及其对气候变化的响应研究进展. 地理科学进展, 37(2): 214-223 [DOI: 10.18306/dlkxjz.2018.02.004http://dx.doi.org/10.18306/dlkxjz.2018.02.004]
Zhang G Q, Yao T D, Chen W F, Zheng G X, Shum C K, Yang K, Piao S L, Sheng Y W, Yi S, Li J L, O’Reilly C M, Qi S H, Shen S S P, Zhang H B and Jia Y Y. 2019. Regional differences of lake evolution across China during 1960s—2015 and its natural and anthropogenic causes. Remote Sensing of Environment, 221: 386-404 [DOI: 10.1016/j.rse.2018.11.038http://dx.doi.org/10.1016/j.rse.2018.11.038]
Zhang G Q, Yao T D, Xie H J, Yang K, Zhu L P, Shum C K, Bolch T, Yi S, Allen S, Jiang L G, Chen W F and Ke C Q. 2020. Response of Tibetan Plateau lakes to climate change: trends, patterns, and mechanisms. Earth-Science Reviews, 208: 103269 [DOI: 10.1016/j.earscirev.2020.103269http://dx.doi.org/10.1016/j.earscirev.2020.103269]
Zhang M W, Ma R H, Li J S, Zhang B and Duan H T. 2014. A validation study of an improved SWIR iterative atmospheric correction algorithm for MODIS-Aqua measurements in Lake Taihu, China. IEEE Transactions on Geoscience and Remote Sensing, 52(8): 4686-4695 [DOI: 10.1109/TGRS.2013.2283523http://dx.doi.org/10.1109/TGRS.2013.2283523]
Zhang Q, Liu Y B, Yao J, Lai X J, Li X H, Wu G P, Huang Q, Sun Z D, Zhang D, Li Y L, Tan Z Q and Liu X G. 2020. Lake hydrology in China: advances and prospects. Journal of Lake Sciences, 32(5): 1360-1379
张奇, 刘元波, 姚静, 赖锡军, 李相虎, 吴桂平, 黄群, 孙占东, 张丹, 李云良, 谭志强, 刘星根. 2020. 我国湖泊水文学研究进展与展望. 湖泊科学, 32(5): 1360-1379 [DOI: 10.18307/2020.0511http://dx.doi.org/10.18307/2020.0511]
Zhang Y L. 2011. Progress and prospect in lake optics: a review. Journal of Lake Sciences, 23(4): 483-497
张运林. 2011. 湖泊光学研究进展及其展望. 湖泊科学, 23(4): 483-497 [DOI: 10.18307/2011.0401http://dx.doi.org/10.18307/2011.0401]
Zou W N, Yuan L and Zhang L Q. 2013. Analyzing the spectral response of submerged aquatic vegetation in a eutrophic lake, Shanghai, China. Ecological Engineering, 57: 65-71 [DOI: 10.1016/j.ecoleng.2013.04.008http://dx.doi.org/10.1016/j.ecoleng.2013.04.008]
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