2021, Vol. 33 
富营养化水体浮游植物遥感监测研究进展
(1: 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008)
(2: 中国科学院大学, 北京 100049)
(2: 中国科学院大学, 北京 100049)
摘要：随着湖泊流域经济的快速发展，蓝藻水华频繁暴发的现象越来越严重，水体富营养化已经成为国内外重大环境问题.浮游植物是水体的初级生产者，是衡量水体富营养化程度的主要指标之一，遥感技术则是探测水体浮游植物时空分布的重要手段.在收集整理近千篇国内外水体浮游植物遥感研究论文的基础上，从卫星数据源、研究内容及研究方法等角度，总结了遥感技术在富营养化湖泊浮游植物监测应用的历史进展、研究重点及发展趋势.研究表明，现有的富营养化水体浮游植物遥感研究，以湖泊蓝藻水华问题为切入点，研究视角由水体表层（藻华面积、色素浓度）转至水下三维（藻总量），研究方法从定性识别转向定量反演，研究内容从监测蓝藻水华推进到探测不同类群蓝藻，逐渐形成了以应用为导向，“MODIS/VIIRS大中型湖泊日常监测-GF/Sentinel2小型湖泊针对性监测-无人机应急监测”的浮游植物遥感综合监测体系.上述研究梳理了富营养化水体浮游植物遥感监测湖泊水环境学科的发展动向，以期为从事蓝藻水华生态灾害监测和预警人员提供重要的技术支撑和理论参考.
关键词：富营养化 蓝藻水华 遥感监测 叶绿素 藻蓝素
Research progress on remote sensing monitoring of phytoplankton in eutrophic water
(1: State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China)
(2: University of Chinese Academy of Sciences, Beijing 100049, P. R. China)
(2: University of Chinese Academy of Sciences, Beijing 100049, P. R. China)
Abstract:
With the rapid economic development of lake basins, the frequent occurrence of cyanobacteria blooms has become more and more serious, and the eutrophication of water bodies has become a major environmental problem at home and abroad. Phytoplankton is the primary producer of water bodies and is one of the main indicators to measure the degree of eutrophication of water bodies. Remote sensing technology is an important means to detect the temporal and spatial distribution of phytoplankton in water bodies. Based on the collection and sorting of nearly a thousand research papers on phytoplankton remote sensing in water bodies at home and abroad, from the perspectives of satellite data sources, research content and research methods, the historical progress, research focus and development trends of remote sensing technology in the application of phytoplankton monitoring in eutrophic lakes are summarized. Studies have shown that the existing remote sensing research on phytoplankton in eutrophic water bodies takes the problem of cyanobacteria blooms in lakes as the starting point, and the research perspective is shifted from the surface of the water body (algae bloom area and pigment concentration) to three-dimensional underwater (total algae biomass); The method has shifted from qualitative identification to quantitative inversion; the research content has advanced from monitoring cyanobacteria blooms to detecting different types of cyanobacteria, and gradually formed an application-oriented, "MODIS/VIIRS daily monitoring of large and medium lakes-GF/Sentinel2 targeted monitoring of small lakes" -UAV emergency monitoring" phytoplankton remote sensing integrated monitoring system. The above research combed the development trend of the phytoplankton remote sensing monitoring lake water environment subject of eutrophic water bodies and provided important technical support and theoretical references for personnel engaged in the monitoring and early warning of cyanobacteria bloom ecological disasters.
Keywords: Eutrophication cyanobacteria blooms remote sensing monitoring chlorophyll phycocyanin
近年来，在自然因素与人为因素的共同作用下，湖泊水环境变化剧烈，水质持续恶化，生态系统遭受严重破坏，功能和效益不断下降，富营养化及引起的藻类水华频发问题逐渐成为公众媒体关注的焦点[1].
浮游植物是生活在河流、湖泊和海洋中的敏感有机体，其群落结构一定程度上可以反映水体富营养化的程度[2]. 通常浮游植物就是指浮游藻类，包括蓝藻门、绿藻门、硅藻门等8个门类的浮游种类. 足够的藻类生物量和适宜的环境条件是形成蓝藻水华的基本条件. 藻类大量繁殖引起的水华现象(简称“藻华”)是湖泊水体富营养化的重要特征[3]. 针对全球71个大型湖泊的遥感监测研究表明，68 % 的湖泊藻华发生强度增加趋势显著[4]. 2019年，我国监测了107个重点湖(库)的富营养化状况，其中6个呈中度富营养状态、24个呈轻度富营养状态、其余未呈现富营养化[5]. 而以太湖、巢湖、滇池为首的富营养化湖泊，蓝藻水华暴发尤为频繁[6-8]. 我国已经成为世界上蓝藻水华暴发最严重、分布最广的国家之一[9].
卫星遥感因其速度快、范围广、监测周期性短，已经成为湖泊富营养化及蓝藻水华监测和预测预警不可或缺的技术手段[10]. 藻华暴发时，覆盖有藻类的水体反射光谱在红光波段呈现出低反射率、绿光波段及近红外波段呈现高反射率，明显区分于清洁水体，是遥感技术识别藻华的关键依据. 因此，基于遥感手段监测浮游植物的相关主题越来越多，监测范围也由小尺度向大尺度转变. 目前，我国长江中下游少数富营养化湖泊(太湖、巢湖)已基本实现对其蓝藻水华面积及水体表层叶绿素a浓度的业务化卫星遥感监测[11]，为政府及水环境管理部门提供了重要的决策依据[12].
此前有关“富营养化水体浮游植物遥感监测”主题的综述大多侧重于藻华面积的识别、表层色素浓度反演算法的总结上；研究区域大多集中于长江中下游的太湖、巢湖、鄱阳湖以及云南的滇池、洱海等[13-14]. 近年来，相关研究在研究内容和方法上又有了全新的发展. 本研究基于中国知网(CNKI)和Web of Science等文献数据库，以富营养化水体和蓝藻水华为主题，综合1970—2020年间约803篇国内外研究成果，基本厘清和总结了在近些年来主要的研究进展和重点，并归纳总结了发展中所面临的困难以及未来发展的新趋势. 本综述皆在梳理富营养化水体浮游植物遥感监测的发展动向，促进水色遥感的进一步发展和深入应用，同时也为从事蓝藻水华生态灾害监测和预警人员提供重要的技术支撑和理论参考.
1 研究主题与数据源研究表明该领域逐渐发展为以应用为导向，研究主题趋于更多样化：从藻华水体识别、藻华面积监测、表层色素浓度反演发展至水下三维藻总量估算以及不同藻种定量区分等. 其中，藻类总存量的研究还处于起步阶段，这将为未来三维立体化监测浮游藻类浓度奠定基础. 研究方法也从传统的线性或非线性回归分析算法，发展为更复杂的人工智能(artificial intelligence，AI)方法，如支持向量机和人工神经网络等，且以“遥感+AI+大数据+Google Earth Engine云计算平台”为主题的新技术也逐渐成为新热点[13]. 研究数据源从单一数据源转至多数据源融合使用，利用的卫星数据也更加丰富多彩(图 1a，b). 围绕该主题的相关论文主要发表于《湖泊科学》、《环境科学》、Remote Sensing、Remote Sensing of Environment、Science of the Total Environment等国内外期刊上.
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图 1 研究主题(a)和数据源(b)的分布 Fig.1 Distribution of research topics (a) and data sources (b) |
多层面需求推动了多源卫星数据的多元化应用. 近年来中高分辨率遥感卫星的发展为多尺度的湖泊水质变化监测提供了多种数据源，促进了湖泊水体动态监测向业务化信息服务方向不断推进[15-16]. 但由于卫星载荷以及应用目标的需要，现有常用卫星传感器的时、空分辨率设置并不能完全满足内陆水体监测的时间连续性、空间完整性的要求[17]. 因此，除加强多源遥感数据联合监测外，研制更高时间分辨率、高空间分辨率传感器及星座组网，是未来研制水色遥感卫星的发展和应用方向(图 2).
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图 2 常用遥感数据源时空分辨率对比 Fig.2 Comparison of temporal and spatial resolutions of commonly used remote sensing data sources |
为了形成完善的富营养化湖库蓝藻监控体系，形成全方位、多层次的天-空-地一体化观测和应急防控平台，要充分发挥多源观测手段在不同时空信息观测方面的优势，整合不同手段信息，在空间尺度、时间尺度等方面取长补短、互相补充[17-18]. 在小型湖库应急监测中，选择无人机(分辨率最高能达到0.04 m)或人工监测，实现人工安排、随机机动，短时间内开展突发性湖库藻类大规模暴发的事故处置，已有学者应用无人机在太湖[19]、八里河[20]、Maspalomas自然保护区[21]等进行藻华监测；而在日常大中型湖库监测中，则更倾向于使用多源卫星数据实现长时序动态监测，如中分辨率成像光谱仪(moderate-resolution imaging spectrum-radiometer, MODIS)因其免费、时间分辨率高等优点成为日常水质监测中最受欢迎的数据资源.
2 水体表层藻华的遥感识别方法研究浮游植物大量聚集会引起水体色度、透明度等物理性质的变化，进而导致水体反射波谱特征的变化[22]. 覆盖蓝藻的水体反射光谱因叶绿素a和藻蓝蛋白的吸收效应，在可见光的蓝紫光及红光波段呈现较低反射率，在近红外则出现类似于植被的“陡坡效应”，这是湖泊藻华遥感识别的理论基础[23]. 基于近红外抬升的单波段法以及近红外与可见光波段差异的差值法、比值法、归一化植被指数(normalized difference vegetation index，NDVI)法、增强型植被指数(enchanted vegetation index，EVI)法、浮游藻类指数(floating algae index，FAI)法等均为目前开展湖泊藻华遥感识别的主要方法[24-28]. 此外，还有一些针对浮游植物色素的蓝藻水华指数(cyanobacteria bloom algae index，CAI)、最大特征峰高度(maximum characteristic peakheight, MPH)、最大叶绿素指数(maximum chlorophyll index，MCI)等方法(表 1).
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表 1 藻华水体识别算法* Tab. 1 Algae bloom water recognition algorithm |
在业务化应用中发现，水生植物、高浑浊水体以及薄云等对藻华遥感识别的影响显著[47-48]. 藻华具有近红外抬升的反射光谱特点，与植物的相类似，因此，基于该特点的藻华遥感识别方法均无法对两者进行区分. 基于先验知识，对于常年生长有水生植物的地区，通常用掩膜将其遮盖掉；对于水生植物分布变化较为显著的内陆水体，通过对比两者在可见光波段及短波红外波段的反射光谱差异，实现水生植物和藻华的同步遥感监测[49-50]. 朱庆等[51]利用叶绿素a光谱指数和藻蓝蛋白基线的水华和水草识别模型，提取太湖水华和水草分布图，表明高光谱遥感则可以利用625 nm附近藻蓝素吸收峰区分蓝藻水华和水草. 高浑浊水体在可见光-近红外整体增高，导致单波段法、比值法、NDVI、FAI等会将浑浊水体误判为低强度藻华[33, 52-53]，而适当的波段组合成的假彩色合成图上，藻华可明显区分于清洁水体、高浊水体及云等，水色指数(forel-ule index，FUI)为此问题提供了一个新的解决思路[54].
MODIS因其良好的时间分辨率(Auqa与Terra两星联合为2次/d)，成为大型富营养化湖泊藻华日常遥感监测的主要数据源. 然而250 m的空间分辨率会导致影像中存在藻华水体的混合像元[55-56]，在尚无更高分辨率的免费卫星数据用于日常遥感监测的前提下，开展亚像元藻华遥感监测研究是满足高精度藻华遥感日常监测的重要前提[57]. Zhang等[6-8]利用瑞利校正反射率(rayleighcorrected reflectance，Rrc)和从Rrc导出3个光谱带中的浮藻指数研发了藻华像元生长算法(algae pixel-growing algorithm)，将藻华面积识别精确到了亚像元内，将MODIS与(准)同步TM遥感监测结果一致性提升至85 % 以上，且该方法在太湖、巢湖以及滇池得到了广泛的研究与应用. 此外，马金戈[58]基于Google Earth Engine(GEE)对全球大型湖泊(>500 km2)的蓝藻水华进行了提取研究，得到了全球大型湖泊的藻华时空暴发情况. 结合GEE平台开展大尺度藻华遥感监测也成为水色遥感的一个新趋势.
3 水体表层浮游植物色素浓度遥感反演研究藻华水体识别定性地反映了浮游植物的空间分布，但不能定量评价水体中浮游植物的确切浓度. 浮游植物色素(叶绿素a和藻蓝素等)是水体光学活性物质，是定量表征水体富营养化程度以及浮游植物浓度的重要水质指标[59-60]. 目前，遥感反演浮游植物色素主要是基于实测多/高光谱数据的经验算法、半经验半分析算法[12]. 基本反演思路如图 3所示. 其中，实用性和应用性比较强的模型为精度较高且有理论支撑的半经验/半分析算法，完全分析算法的机理及参数仍需进一步探究和优化[61-62].
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图 3 遥感反演藻类色素流程 Fig.3 Remote sensing inversion process of algae pigments |
表 2和表 3列出了内陆湖泊叶绿素a及藻蓝素的遥感反演具体算法，并对这些算法做了多角度的对比分析和归纳总结. 对比表格可以发现相同的数据有不同的算法，相同的区域有不同的数据，但这些方法均是基于具体卫星遥感数据和湖泊的光学特性所构建的针对性较强的浮游植物色素反演模型. 内陆湖泊光学活性差异较大，甚至同一湖泊不同时间段水体光学活性物质组成及其比例也大为不同[96]，往往造成反演算法复杂程度及反演精度上的差异. 因此，此类算法通常都具有显著的区域性和季节性，适用性较差. 多源卫星中，针对海洋水色设置的中分辨率成像光谱仪(missouri emergency resource information system，MERIS)、哨兵3(Sentinel-3 OLCI)等，因具有对叶绿素和藻蓝素的特征峰波段设置，反演结果精度高、效果好[97-98]. 而Landsat等宽波段卫星是长时序日常遥感监测内陆湖泊的常用数据，其波段设置和信噪比性往往难以满足高精度定量遥感监测的要求[83, 99]. 近年来，机器学习方法，包括深度神经网络[100]、卷积神经网络[19]、随机森林[20]等，都已被用于水体藻类色素浓度的反演. Cao等就基于XGBoost(BST)模型，研发了陆地资源卫星数据(Landsat OLI)反演内陆湖泊浮游植物色素浓度的实用方法，该模型相比其他机器学习有更高的准确率、效率和并发. 结果也表明机器学习模型可以为进一步提高宽波段数据反演水体藻类色素浓度的适用性和准确性提供重要的参考和应用前提[101]. 此外，大气校正对水色参数定量反演也有很大的影响，当前的多种大气校正方法都未充分考虑水色遥感的特点，适用性与普适性较差. 宋挺等[102]就对高分四号卫星的大气校正算法进行了改进，结果显示红光波段校正精度最高，可以较好地应用于内陆浑浊二类水体的定量遥感监测.
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表 2 叶绿素a遥感反演模型 Tab. 2 Remote sensing inversion model of chlorophyll-a |
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表 3 藻蓝素遥感反演模型 Tab. 3 Remote sensing inversion model of phycocyanin |
现有水体表层叶绿素a浓度的遥感反演研究，是基于藻类垂向均一传统水色遥感的理论假设. 但已有研究表明藻类在垂向上是非均匀分布的，会对基于传统假设而形成的水体光学特性、水下光场分布、水质参数反演产生很大误差[103]. 只有充分考虑藻类垂向分布异质，精准获取真光层内藻类总生物量(即藻总量)，才能准确把握蓝藻水华强度变化情势及湖泊营养状态变化趋势. 传统的藻总量及其空间分布的估算方法是基于几个生物量分布样本的离散测量，然后插值到整个湖泊；随着遥感技术的发展，水体表层色素浓度的遥感反演被广泛地应用到湖泊蓝藻水华预测预警中[104]；近几年，考虑到利用水体表层藻量信息的局限性，部分学者开展了富营养化湖泊藻总量遥感估算方面的初步研究[105-106].
理想条件下，假定像元水柱内藻类水平均匀分布，通过水体表层的遥感反射率，准确获知水体中藻类垂向分布函数及其结构参数，结合水位及湖盆DEM数据，完成每个像元水柱内藻总量积分计算，基于所有像元水柱的藻总量计算结果，完成全湖的藻总量估算. 然而，目前受水体其他光学活性物质以及野外监测数据准确性的影响，直接基于遥感反射率精确获取藻类垂向分布函数的结构参数难度很大，存在着较多的“相同藻类垂向分布反射率光谱相异”或“相同反射率光谱而藻类垂向分布相异”的情况.
现有的研究主要基于以下思路“由浅入深”地开展水体藻总量遥感估算研究：①在水平剖面面积为单位面积、垂向水深为D m的单位水柱中，假定整个水柱中藻类呈现水平、垂向都是均匀分布. Xue等[107-109]研究巢湖藻类垂向分布对水体遥感反射比的影响已表明，基于藻类垂向分布均匀的假定会造成遥感估算藻总量的显著高估；②假设藻类垂向分布为高斯类型，利用表面叶绿素信息和总生物量之间的经验关系计算藻类总生物量. 该方法被用于遥感估算海洋系统的初级生产力，适用于大多数海洋水域[110]. Li等[106]借鉴该思路，基于MODIS遥感数据建立算法开展了我国巢湖非藻华条件下(即均一型、高斯型)藻总量遥感估算研究，但也表明这种估算方法存在卫星空间分辨率差异及卫星和野外测量之间时间间隔的不足；③基于野外实测及Ecolight模拟数据，统计分析水体表层到40 cm深度处的藻类生物量与单元水柱内的藻类总存量的线性关系，实现了藻华条件下(即指数型、幂函数型)的藻总量估算研究. Li等[106]基于这种方法对浅层富营养化湖泊的藻总量进行估算，表明该方法不仅对表层藻类生物估算精度要求较高，而且其线性关系随着藻类垂向分布函数结构参数差异而不同，普适性较差.
由此可见，现有的富营养化湖泊藻总量遥感估算方法，均基于水体表层藻类生物量估算. 鉴于优势藻种差异、藻类垂向分布的复杂性以及变化的快速性，表层生物量与水柱内藻总量的关系差异性较大，提出一个普适性较强的定量关系难度较大.
5 不同藻类种群的遥感定量监测研究不同湖泊中蓝藻水华的优势种群有着较大差异，甚至同一湖泊在不同的季节或者不同的地区也都存在不同的水华蓝藻优势种群[111]，即存在“一湖多种”的现象. 相关研究表明，形成蓝藻水华的种类主要是微囊藻(Microcystis)、束丝藻(Aphanizomenon)、鱼腥藻(Dolichospermum)和颤藻(Oscillatoria)[104]. Aldrich等对传统藻种识别做出了相关研究，主要有基于形态学的显微计数法和图像法[112]、基于藻种DNA的分子生物学方法[113]以及基于色素光学特征的高效液相色谱法[114-115]等. 而针对湖泊蓝藻种群结构的定量遥感反演的研究目前鲜有文献报道. 马万泉[116]、戴红亮等[117-118]、吕恒等[119]和王瑜[120]基于室内控制试验及模拟试验，开展了不同门类藻体生物光学特性研究，包括蓝藻门(铜绿微囊藻(Microcystis aeruginosa))、绿藻门(小球藻(Chlorella vulgaris))、硅藻门(梅尼小环藻(Cyclotella meneghiniana)) 以及隐藻门(卵形隐藻(Cryptomonas ovata))，并探索性地建立了藻种间比例遥感估算方法[121]，结果表明二层球形模型误差较小，有较好的优越性，但也存在波谱曲线抖动的不足.
此外，针对内陆富营养化湖泊典型水华蓝藻，张壹萱等[111]以微囊藻、鱼腥藻、束丝藻3种典型水华蓝藻为研究对象，通过室内光学控制实验对其固有光学特性进行研究，并探讨色素浓度、色素占比以及藻类等效粒径对不同水华蓝藻固有光学特性的影响. 上述3种典型蓝藻的细胞形状、粒径大小以及形成蓝藻群落的聚集形态存在明显差异，这为基于生物光学特性开展水华蓝藻种群结构的定量反演研究提供了理论上的可行性[119-123]. 但室内培养的藻类基本为单细胞，与湖泊环境中藻颗粒形态及其生物光学特征存在显著差异，因此，基于人工培养藻类的光学特性构建的反演算法，其实际应用能力和反演精度受到一定的限制. 而Chu等[124]利用MODIS卫星数据对准分析算法(quasi-analytical algorithm)进行了改进与优化，在获取巢湖水体表层吸收特性的基础上，初步开展了巢湖不同水华蓝藻类群的遥感定量识别研究，结果表明相比人工培养藻类的光学特性构建的反演算法，卫星遥感模型有更好的准确性和适用性. 这也为下一步加强构建遥感定量反演模型、精确解析湖泊水体中主要水华蓝藻的种群结构，为富营养化湖泊蓝藻生态灾害的预测预警提供准确时空分布信息等奠定了基础.
6 结束语本文基于大量文献的检索和筛选，从研究主题、研究数据源、研究内容、研究方法等方面对内陆富营养化水体浮游植物的遥感监测进展进行了深入分析，并围绕藻华遥感监测、浮游植物色素遥感反演以及最新的湖泊藻总量和不同水华蓝藻的遥感定量识别方面的研究，开展了具有针对性的分析与讨论. 该综述可为湖泊水环境管理和决策的相关研究人员提供参考.
目前，内陆富营养化水体浮游植物的监测仍面临着诸多问题，如遥感反演模型的普适性、多源卫星数据监测结果的可比性和一致性等. 因此，为了促进富营养化湖泊浮游植物的遥感应用能力，亟待补充完善不同湖泊、不同优势藻种的光谱数据库，为发展普适性更强的反演算法奠定数据基础. 此外，由于不同湖泊面积和水环境的差异以及不同数据源之间分辨率的差异，需要进一步发展多源数据融合的反演算法，以此实现系统化、体系化的监测. 利用多卫星、多通道、多模式的方法，构建一个“空-天-地一体化”的水环境监测平台，从而实现全覆盖、多角度、多手段的实时监测，这将进一步促进内陆富营养化水体浮游植物的遥感监测更智能、更高效、更准确.
7 参考文献
| [1] |
Yang GS, Jiang JH, Xue B et al. Chinese lake survey report. Beijing: Science Press, 2019. [杨桂山, 姜加虎, 薛滨等. 中国湖泊调查报告. 北京: 科学出版社, 2019.]
|
| [2] |
Xin SJ, Ye J, Wang YX. Talking about planktonic algae and its application in the study of water eutrophication. Chemical Engineering and Equipment, 2018(6): 3-5. [辛思洁, 叶菁, 王义祥. 浅谈浮游藻类及其在水体富营养化研究中的应用. 化学工程与装备, 2018(6): 3-5.] |
| [3] |
Kong FX, Gao G. Hypothesis on cyanobacteria bloom-forming mechanism in large shallow eutrophic lakes. Acta Ecologica Sinica, 2005, 25(3): 589-595. [孔繁翔, 高光. 大型浅水富营养化湖泊中蓝藻水华形成机理的思考. 生态学报, 2005, 25(3): 589-595. DOI:10.3321/j.issn:1000-0933.2005.03.028] |
| [4] |
Ho JC, Michalak AM, Pahlevan N. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature, 2019, 574(7780): 667-670. DOI:10.1038/s41586-019-1648-7 |
| [5] |
Ministry of Ecology and Environment of the People's Republic of China. 2019 Bulletin of China's Ecological Environment, 2020. [中华人民共和国生态环境部. 2019年中国生态环境状况公报, 2020. ]
|
| [6] |
Zhang YC, Ma RH, Zhang M et al. Fourteen-year record (2000-2013) of the spatial and temporal dynamics of floating algae blooms in Lake Chaohu, observed from time series of MODIS images. Remote Sensing, 2015, 7(8): 10523-10542. DOI:10.3390/rs70810523 |
| [7] |
Zhang YC, Ma RH, Duan HT et al. Satellite analysis to identify changes and drivers of CyanoHABs dynamics in Lake Taihu. Water Supply, 2016, 16(5): 1451-1466. DOI:10.2166/ws.2016.074 |
| [8] |
Jing YY, Zhang YC, Hu MQ et al. MODIS-satellite-based analysis of long-term temporal-spatial dynamics and drivers of algal blooms in a plateau Lake Dianchi, China. Remote Sensing, 2019, 11(21): 2582. DOI:10.3390/rs11212582 |
| [9] |
Wu QL, Xie P, Yang LY et al. Ecological consequences of cyanobacetrial blooms in lakes and their countermeasures. Advances in Earth Science, 2008, 23(11): 1115-1123. [吴庆龙, 谢平, 杨柳燕等. 湖泊蓝藻水华生态灾害形成机理及防治的基础研究. 地球科学进展, 2008, 23(11): 1115-1123. DOI:10.3321/j.issn:1001-8166.2008.11.001] |
| [10] |
Pan DL, Ma RH. Several key problems of lake water quality remote sensing. J Lake Sci, 2008, 20(2): 139-144. [潘德炉, 马荣华. 湖泊水质遥感的几个关键问题. 湖泊科学, 2008, 20(2): 139-144. DOI:10.18307/2008.0201] |
| [11] |
Zhu L, Wang Q, Wu CQ et al. Monitoring and annual statistical analysis of algal blooms in Chaohu based on remote sensing. Environmental Monitoring in China, 2013, 29(2): 162-166. [朱利, 王桥, 吴传庆等. 巢湖水华遥感监测与年度统计分析研究. 中国环境监测, 2013, 29(2): 162-166. DOI:10.3969/j.issn.1002-6002.2013.02.034] |
| [12] |
Ma RH. Remote sensing of lake water environment. Beijing: Science Press, 2010. [马荣华. 湖泊水环境遥感. 北京: 科学出版社, 2010.]
|
| [13] |
Wang XY, Yang W. Water quality monitoring and evaluation using remote sensing techniques in China: A systematic review. Ecosystem Health and Sustainability, 2019, 5(1): 47-56. DOI:10.1080/20964129.2019.1571443 |
| [14] |
Pan YY, Guo QZ, Sun JH. Advances in remote sensing inversion method of chlorophyll a concentration. Science of Surveying and Mapping, 2017, 42(1): 43-48. [潘应阳, 国巧真, 孙金华. 水体叶绿素a浓度遥感反演方法研究进展. 测绘科学, 2017, 42(1): 43-48.] |
| [15] |
Song CQ, Zhan PF, Ma RH. Progress in remote sensing study on lake hydrologic regime. J Lake Sci, 2020, 32(5): 1406-1420. [宋春桥, 詹鹏飞, 马荣华. 湖泊水情遥感研究进展. 湖泊科学, 2020, 32(5): 1406-1420. DOI:10.18307/2020.0514] |
| [16] |
Sun YY, Ma JW, Huang SF. Application of my country's high-resolution satellites in lake monitoring and protection. Satellite Applications, 2019(11): 36-40. [孙亚勇, 马建威, 黄诗峰. 我国高分辨率卫星在湖泊监测和保护中的应用. 卫星应用, 2019(11): 36-40. DOI:10.3969/j.issn.1674-9030.2019.11.010] |
| [17] |
Duan HT, Wan NS, Qiu YG et al. Discussions and practices on the framework of monitoring system in eutrophic lakes and reservoirs. J Lake Sci, 2020, 32(5): 1396-1405. [段洪涛, 万能胜, 邱银国等. 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, 2020, 32(5): 1396-1405. DOI:10.18307/2020.0513] |
| [18] |
Wang HL, Liu H, Liu YD. "Heaven and Earth Integration" Water Environment Monitoring System. Chinese Society of Instrumentation. 2016 China Environmental and Safety Monitoring Technology Symposium—The 27th MICONEX2016 Scientific Instruments Benefiting People's Livelihood Series Sub-session Proceedings. China Instrument and Control Society: Editorial Department of Modern Scientific Instruments, 2016: 4. [王红丽, 刘浩, 刘永定. "天地一体化"水环境监测系统. 中国仪器仪表学会. 2016年中国环境与安全监测技术研讨会——第27届MICONEX2016科学仪器惠及民生系列分会场论文集. 中国仪器仪表学会: 现代科学仪器编辑部, 2016: 4. ]
|
| [19] |
Pyo J, Duan HT, Baek S et al. A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery. Remote Sensing of Environment, 2019, 233: 111350. DOI:10.1016/j.rse.2019.111350 |
| [20] |
Chen SL, Hu CM, Barnes BB et al. A machine learning approach to estimate surface ocean pCO2 from satellite measurements. Remote Sensing of Environment, 2019, 228: 203-226. DOI:10.1016/j.rse.2019.04.019 |
| [21] |
Wen L, Li S, Chen Q et al. Integration and application of water quality monitoring information based on UAV remote sensing. Jiangsu Water Resources, 2020(10): 35-40. [闻亮, 李胜, 陈清等. 基于无人机遥感的水质监测信息集成与应用. 江苏水利, 2020(10): 35-40.] |
| [22] |
Li Y. Research on remote sensing inversion of inland water color parameters and water bloom monitoring[Dissertation]. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 2017. [李瑶. 内陆水体水色参数遥感反演及水华监测研究[学位论文]. 北京: 中国科学院遥感与数字地球研究所, 2017. ]
|
| [23] |
Hu CM, Muller-Karger FE, Taylor C et al. Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sensing of Environment, 2005, 97(3): 311-321. DOI:10.1016/j.rse.2005.05.013 |
| [24] |
Duan HT, Zhang SX, Zhang YZ. Cyanobacteria bloom monitoring with remote sensing in Lake Taihu. J Lake Sci, 2008, 20(2): 145-152. [段洪涛, 张寿选, 张渊智. 太湖蓝藻水华遥感监测方法. 湖泊科学, 2008, 20(2): 145-152. DOI:10.18307/2008.0202] |
| [25] |
Ma RH, Kong FX, Duan HT et al. Spatio-temporal distribution of cyanobacteria blooms based on satellite imageries in Lake Taihu, China. J Lake Sci, 2008, 20(6): 687-694. [马荣华, 孔繁翔, 段洪涛等. 基于卫星遥感的太湖蓝藻水华时空分布规律认识. 湖泊科学, 2008, 20(6): 687-694. DOI:10.18307/2008.0605] |
| [26] |
Liu HQ, Huete A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(2): 457-465. DOI:10.1109/TGRS.1995.8746027 |
| [27] |
Xu JP, Zhang B, Li F et al. Detecting modes of cyanobacteria bloom using MODIS data in Lake Taihu. J Lake Sci, 2008, 20(2): 191-195. [徐京萍, 张柏, 李方等. 基于MODIS数据的太湖藻华水体识别模式. 湖泊科学, 2008, 20(2): 191-195. DOI:10.18307/2008.0209] |
| [28] |
Hu CM. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment, 2009, 113(10): 2118-2129. DOI:10.1016/j.rse.2009.05.012 |
| [29] |
Shi K, Zhang YL, Zhu GW et al. Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250 m MODIS-Aqua data. Remote Sensing of Environment, 2015, 164: 43-56. DOI:10.1016/j.rse.2015.02.029 |
| [30] |
Gower JFR. Red tide monitoring using AVHRR HRPT imagery from a local receiver. Remote Sensing of Environment, 1994, 48(3): 309-318. DOI:10.1016/0034-4257(94)90005-1 |
| [31] |
Zhu L, Li YM, Zhao SH et al. Remote sensing monitoring of Taihu Lake water quality by using GF-1 satellite WFV data. Remote Sensing for Land & Resources, 2015, 27(1): 113-120. [朱利, 李云梅, 赵少华等. 基于GF-1号卫星WFV数据的太湖水质遥感监测. 国土资源遥感, 2015, 27(1): 113-120. DOI:10.6046/gtzyyg.2015.01.18] |
| [32] |
Song Y, Song XD, Guo QH et al. Remote sensing monitoring and pre-alarming of algal blooms in Taihu Lake. Spectroscopy and Spectral Analysis, 2011, 31(3): 753-757. [宋瑜, 宋晓东, 郭青海等. 太湖藻华水体的遥感监测与预警. 光谱学与光谱分析, 2011, 31(3): 753-757. DOI:10.3964/j.issn.1000-0593(2011)03-0753-05] |
| [33] |
Zhang T, Su FZ, Yang XM et al. A method for detecting red tide information from MODIS data and its application in Pearl River estuary. Journal of Geo-Information Science, 2009, 11(2): 244-249. [张涛, 苏奋振, 杨晓梅等. MODIS遥感数据提取赤潮信息方法与应用——以珠江口为例. 地球信息科学学报, 2009, 11(2): 244-249. DOI:10.3969/j.issn.1560-8999.2009.02.017] |
| [34] |
Stumpf RP, Tomlinson MC. Remote Sensing of Harmful Algal Blooms. Springer Netherlands, 2007. DOI:10.1007/978-1-4020-3100-7_12 |
| [35] |
Shi JZ, Wu W, Song T et al. The FY-3/MERSI's application in monitoring of cyanobacteria bloom in Lake Taihu. Environmental Monitoring and Forewarning, 2018, 10(2): 6-10. [石浚哲, 吴蔚, 宋挺等. 基于国产"风云三号" 卫星MERSI的太湖蓝藻水华监测业务化应用. 环境监控与预警, 2018, 10(2): 6-10. DOI:10.3969/j.issn.1674-6732.2018.02.002] |
| [36] |
Zhou T, Liu XN, Qi WY et al. Temporal and spatial evolution characteristics of cyanobacteria in Chaohu Lake based on GF1 satellite imagery. Journal of North China University of Water Resources and Electric Power: Natural Science Edition, 2020, 41(3): 62-66. [周婷, 刘小妮, 戚王月等. 基于GF1卫星影像的巢湖蓝藻时空演变特征分析. 华北水利水电大学学报: 自然科学版, 2020, 41(3): 62-66. DOI:10.19760/j.ncwu.zk.2020036] |
| [37] |
Xie GQ, Li M, Lu WK et al. Spectral features, remote sensing identification and breaking-out meteorological conditions of algal bloom in Lake Dianchi. J Lake Sci, 2010, 22(3): 327-336. [谢国清, 李蒙, 鲁韦坤等. 滇池蓝藻水华光谱特征、遥感识别及暴发气象条件. 湖泊科学, 2010, 22(3): 327-336. DOI:10.18307/2010.0304] |
| [38] |
Lin Y, Pan C, Chen YY et al. Recognition of cyanobacteria bloom based on spectral analysis of remote sensing imagery. Journal of Tongji University: Natural Science, 2011, 39(8): 1247-1252. [林怡, 潘琛, 陈映鹰等. 基于遥感影像光谱分析的蓝藻水华识别方法. 同济大学学报: 自然科学版, 2011, 39(8): 1247-1252. DOI:10.3969/j.issn.0253-374x.2011.08.028] |
| [39] |
Cao P, Liang QC, Li SM. Remote sensing synchronous monitoring method of cyanobacteria bloom and aquatic vegetation in Taihu Lake based on Otsu algorithm. Jiangsu Agricultural Sciences, 2019, 47(14): 288-294. [曹鹏, 梁其椿, 李淑敏. 基于Otsu算法的太湖蓝藻水华与水生植被遥感同步监测方法. 江苏农业科学, 2019, 47(14): 288-294.] |
| [40] |
Zhao D. Remote sensing monitoring of cyanobacteria bloom in typical inland lakes[Dissertation]. Xi'an: Xi'an University of Science and Technology, 2018. [赵丹. 典型内陆湖库蓝藻水华遥感监测[学位论文]. 西安: 西安科技大学, 2018. ]
|
| [41] |
Guo WC. Study on extraction methods of cyanophytes bloom based on HJ-1[Dissertation]. Nanjing: Nanjing Normal University, 2011. [郭望成. 基于环境1号卫星的蓝藻水华提取方法研究[学位论文]. 南京: 南京师范大学, 2011. ]
|
| [42] |
Matthews MW. Eutrophication and cyanobacterial blooms in South African inland waters: 10 years of MERIS observations. Remote Sensing of Environment, 2014, 155: 161-177. DOI:10.1016/j.rse.2014.08.010 |
| [43] |
Matthews MW, Odermatt D. Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sensing of Environment, 2015, 156: 374-382. DOI:10.1016/j.rse.2014.10.010 |
| [44] |
Li XW, Zhang Y, Shi H et al. Application of maximum chlorophyll index derived from sentinel-3A satellite OLCI data for monitoring cyanobacteria blooms in lake Taihu. Environmental Monitoring in China, 2019, 35(3): 146-155. [李旭文, 张悦, 侍昊等. 基于哨兵-3A卫星OLCI数据的最大叶绿素指数在太湖蓝藻水华监测中的应用. 中国环境监测, 2019, 35(3): 146-155.] |
| [45] |
Gower J, King S. Satellite images show the movement of floating Sargassum in the gulf of Mexico and Atlantic ocean. Nature Precedings, 2008, 1. DOI:10.1038/npre.2008.1894.1 |
| [46] |
Gower J, King S, Borstad G et al. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 2005, 26(9): 2005-2012. DOI:10.1080/01431160500075857 |
| [47] |
Mouw CB, Greb S, Aurin D et al. Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions. Remote Sensing of Environment, 2015, 160: 15-30. DOI:10.1016/j.rse.2015.02.001 |
| [48] |
Wang MH, Shi W, Tang JW. Water property monitoring and assessment for China's inland Lake Taihu from MODIS-Aqua measurements. Remote Sensing of Environment, 2011, 115(3): 841-854. DOI:10.1016/j.rse.2010.11.012 |
| [49] |
Liang QC, Zhang YC, Ma RH et al. A MODIS-based novel method to distinguish surface cyanobacterial scums and aquatic macrophytes in Lake Taihu. Remote Sensing, 2017, 9(2): 133. DOI:10.3390/rs9020133 |
| [50] |
Zhang YC, Ma RH, Liang QC et al. Secondary impacts of eutrophication control activities in shallow lakes: Lessons from aquatic macrophyte dynamics in Lake Taihu from 2000 to 2015. Freshwater Science, 2019, 38(4): 802-817. DOI:10.1086/706197 |
| [51] |
Zhu Q, Li JS, Zhang FF et al. Distinguishing cyanobacteria bloom and aquatic plants in Lake Taihu based on hyperspectral imager for the coastal ocean images. Remote Sensing Technology and Application, 2016, 31(5): 879-885. [朱庆, 李俊生, 张方方等. 基于海岸带高光谱成像仪影像的太湖蓝藻水华和水草识别. 遥感技术与应用, 2016, 31(5): 879-885.] |
| [52] |
Hu CM, Lee Z, Ma RH et al. Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. Journal of Geophysical Research: Oceans, 2010, 115(C4): C04002. DOI:10.1029/2009JC005511 |
| [53] |
Hu CM, Li DQ, Chen CS et al. On the recurrent Ulva prolifera blooms in the yellow sea and East China Sea. Journal of Geophysical Research: Oceans, 2010, 115(C5): C05017. DOI:10.1029/2009JC005561 |
| [54] |
Wang SL. Research on remote sensing monitoring of lake and reservoir water quality based on water color index[Dissertation]. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 2018. [王胜蕾. 基于水色指数的大范围长时序湖库水质遥感监测研究[学位论文]. 北京: 中国科学院遥感与数字地球研究所, 2018. ]
|
| [55] |
Palmer SCJ, Kutser T, Hunter PD. Remote sensing of inland waters: Challenges, progress and future directions. Remote Sensing of Environment, 2015, 157: 1-8. DOI:10.1016/j.rse.2014.09.021 |
| [56] |
Qi L, Lee Z, Hu CM et al. Requirement of minimal signal-to-noise ratios of ocean color sensors and uncertainties of ocean color products. Journal of Geophysical Research: Oceans, 2017, 122(3): 2595-2611. DOI:10.1002/2016JC012558 |
| [57] |
Zhang YC, Ma RH, Duan HT et al. A novel algorithm to estimate algal bloom coverage to subpixel resolution in Lake Taihu. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(7): 3060-3068. DOI:10.1109/JSTARS.2014.2327076 |
| [58] |
Ma JG. Research on global large lake algae bloom extraction based on Google Earth Engine platform[Dissertation]. Beijing: University of Chinese Academy of Sciences, 2020. [马金戈. 基于Google Earth Engine平台的全球大型湖泊藻华提取研究[学位论文]. 北京: 中国科学院大学, 2020. ]
|
| [59] |
Liu MY, Tong CF, Wu FR. Longitudinal variation characteristics of chlorophyll a and the influencing factors along the Taipu River. Acta Ecologica Sinica, 2020, 40(19): 7084-7092. [刘毛亚, 童春富, 吴逢润. 太浦河水体叶绿素a纵向演变特征及主要影响因子. 生态学报, 2020, 40(19): 7084-7092. DOI:10.5846/stxb201908291790] |
| [60] |
Wei YC, Wang GX, Cheng CM et al. Baseline correction of spectrum for the inversion of chlorophyll-a concentration in the turbidity water. Spectroscopy and Spectral Analysis, 2012, 32(9): 2546-2550. [韦玉春, 王国祥, 程春梅等. 面向浑浊水体叶绿素a浓度遥感反演的光谱基线校正. 光谱学与光谱分析, 2012, 32(9): 2546-2550. DOI:10.3964/j.issn.1000-0593(2012)09-2546-05] |
| [61] |
Zhou FF. Optical properties of chlorophyll-a in reservoir water and its concentration inversion models by remote sensing[Dissertation]. Hangzhou: Zhejiang University, 2011. [周方方. 水库水体叶绿素a光学性质及浓度遥感反演模式研究[学位论文]. 杭州: 浙江大学, 2011. ]
|
| [62] |
Tao R, Peng JC, Zhang H et al. Research progress on chlorophyll-a monitoring in inland waters based on remote sensing. Geomatics World, 2019, 26(4): 44-53. [陶然, 彭金婵, 张豪等. 内陆水体叶绿素a浓度遥感监测方法研究进展. 地理信息世界, 2019, 26(4): 44-53. DOI:10.3969/j.issn.1672-1586.2019.04.008] |
| [63] |
Ma L, Liu JP. Research on chlorophyll a inversion model of Xianghai wetland water body based on Landsat-OLI image data. Xiandai Nongcun Keji, 2019(6): 60-62. [马兰, 刘吉平. 基于Landsat-OLI影像数据的向海湿地水体叶绿素a反演模型研究. 现代农村科技, 2019(6): 60-62. DOI:10.3969/j.issn.1674-5329.2019.06.051] |
| [64] |
Allee RJ, Johnson JE. Use of satellite imagery to estimate surface chlorophyll a and Secchi disc depth of Bull Shoals Reservoir, Arkansas, USA. International Journal of Remote Sensing, 1999, 20(6): 1057-1072. DOI:10.1080/014311699212849 |
| [65] |
Li TT, Tian LQ, Li J et al. Comparison study on the retrieval of chlorophyll in turbid waters based on Sentinel satellites—a case study of Poyang Lake. Journal of Central China Normal University: Natural Sciences, 2017, 51(6): 858-864. [李亭亭, 田礼乔, 李建等. 基于Sentinel卫星的浑浊水体叶绿素反演对比研究——以鄱阳湖为例. 华中师范大学学报: 自然科学版, 2017, 51(6): 858-864.] |
| [66] |
Zhou ZL, Tian WJ, Mei X. Quantitative retrieval of chlorophyll-a concentration by remote sensing in Honghu Lake based on Landsat8 data. Journal of Hubei University: Natural Science Edition, 2017, 39(2): 212-216. [周志立, 田文俊, 梅新. 基于Landsat8影像反演洪湖叶绿素a浓度. 湖北大学学报: 自然科学版, 2017, 39(2): 212-216. DOI:10.3969/j.issn.1000-2375.2017.02.020] |
| [67] |
Meng FX, Chen SB, Zhang GL et al. Quantitative inversion of nearshore suspended sediment and chlorophyll-a concentration based on Landsat-8 data in South China Sea. Global Geology, 2017, 36(2): 616-623, 642. [孟凡晓, 陈圣波, 张国亮等. 基于Landsat-8数据南海近岸悬浮泥沙与叶绿素a浓度定量反演. 世界地质, 2017, 36(2): 616-623, 642. DOI:10.3969/j.issn.1004-5589.2017.02.029] |
| [68] |
Pan CH, Xia LH, Wu ZF et al. Remote sensing retrieval of chlorophyll-a concentration in coastal aquaculture area of Zhelin Bay. Journal of Tropical Oceanography, 2021, 40(1): 142-153. [潘翠红, 夏丽华, 吴志峰等. 柘林湾近岸水产养殖区水域叶绿素a浓度反演. 热带海洋学报, 2021, 40(1): 142-153. DOI:10.11978/2019110] |
| [69] |
Binding CE, Greenberg TA, Bukata RP. Time series analysis of algal blooms in Lake of the Woods using the MERIS maximum chlorophyll index. Journal of Plankton Research, 2011, 33(12): 1847-1852. DOI:10.1093/plankt/fbr079 |
| [70] |
Zhu YF, Zhu L, Li JG et al. The study of inversion of chlorophyll a in Taihu based on GF-1 WFV image and BP neural network. Acta Scientiae Circumstantiae, 2017, 37(1): 130-137. [朱云芳, 朱利, 李家国等. 基于GF-1 WFV影像和BP神经网络的太湖叶绿素a反演. 环境科学学报, 2017, 37(1): 130-137. DOI:10.13671/j.hjkxxb.2016.0275] |
| [71] |
Zhang YC, Qian X, Qian Y et al. Quantitative retrieval of chlorophyll a concentration in Taihu Lake using machine learning methods. Environmental Science, 2009, 30(5): 1321-1328. [张玉超, 钱新, 钱瑜等. 基于机器学习方法的太湖叶绿素a定量遥感研究. 环境科学, 2009, 30(5): 1321-1328. DOI:10.13227/j.hjkx.2009.05.014] |
| [72] |
Liu Y, Li XL. The inversion study of chlorophyll a concentration in Jinpo Lake based on water body index. Journal of Hunan Agricultural University: Natural Sciences, 2019, 45(2): 172-178. [刘宇, 李旭龙. 基于水体指数的镜泊湖叶绿素a质量浓度反演研究. 湖南农业大学学报: 自然科学版, 2019, 45(2): 172-178.] |
| [73] |
Wang GS. Inversion of chlorophyll concentration in Lake Taihu based on BP neural network[Dissertation]. Nanjing: Nanjing University of Posts and Telecommunications, 2018. [王根深. 基于BP神经网络的太湖叶绿素浓度反演[学位论文]. 南京: 南京邮电大学, 2018. ]
|
| [74] |
Feng H, Li JG, Zhu YF et al. Synergistic inversion method of chlorophyll a concentration in GF -1 and Landsat8 imagery: A case study of the Taihu Lake. Remote Sensing for Land & Resources, 2019, 31(4): 182-189. [封红娥, 李家国, 朱云芳等. GF-1与Landsat8水体叶绿素a浓度协同反演——以太湖为例. 国土资源遥感, 2019, 31(4): 182-189. DOI:10.6046/gtzyyg.2019.04.24] |
| [75] |
Duan HT, Ma RH, Hu CM. Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China. Remote Sensing of Environment, 2012, 126: 126-135. DOI:10.1016/j.rse.2012.08.011 |
| [76] |
Tao M, Duan HT, Qi L et al. An operational algorithm to estimate chlorophyll-a concentrations in Lake Chaohu from MODIS imagery. J Lake Sci, 2015, 27(6): 1140-1150. [陶憨, 段洪涛, 齐琳等. 一种基于MODIS影像可业务化运行的巢湖水体叶绿素a估算算法. 湖泊科学, 2015, 27(6): 1140-1150. DOI:10.18307/2015.0620] |
| [77] |
Zhou L, Ma RH, Duan HT et al. Remote sensing retrieval for chlorophyll-a concentration in turbid case Ⅱ waters(Ⅰ): The optimal model. Journal of Infrared and Millimeter Waves, 2011, 30(6): 531-536. [周琳, 马荣华, 段洪涛等. 浑浊Ⅱ类水体叶绿素a浓度遥感反演(Ⅰ): 模型的选择. 红外与毫米波学报, 2011, 30(6): 531-536.] |
| [78] |
Tian Y. A remote sensing research of Tai's water parameters based on semi-analytical model[Dissertation]. Nanjing: Nanjing University of Posts and Telecommunications, 2019. [田园. 基于半分析模型的太湖水色参数遥感反演研究[学位论文]. 南京: 南京邮电大学, 2019. ]
|
| [79] |
Wang YQ, Yan W. Combining principal component analysis and artificial neural network to retrieve chlorophyll-a concentrations in case I water. Ocean Technology, 2007, 26(4): 54-57. [王迎强, 严卫. 主成分分析与人工神经网络相结合反演一类水体叶绿素-a浓度. 海洋技术, 2007, 26(4): 54-57. DOI:10.3969/j.issn.1003-2029.2007.04.016] |
| [80] |
Xie TT, Chen YZ, Lu WF. Retrieval of chlorophyll-a in lower reaches of the Minjiang River via the three-band bio-optical model. Laser and Optoelectronics Progress, 2020, 57(7): 248-255. [谢婷婷, 陈芸芝, 卢文芳. 基于三波段生物光学模型反演闽江下游叶绿素a. 激光与光电子学进展, 2020, 57(7): 248-255.] |
| [81] |
Huang CC, Li YM, Wang Q et al. Suspended particle matter and chlorophyll-a universal bio-optical retrieval model. Journal of Infrared and Millimeter Waves, 2013, 32(5): 462-467. [黄昌春, 李云梅, 王桥等. 悬浮颗粒物和叶绿素普适性生物光学反演模型. 红外与毫米波学报, 2013, 32(5): 462-467. DOI:10.3724/SP.J.1010.2013.00462] |
| [82] |
Giardino C, Brando VE, Dekker AG et al. Assessment of water quality in Lake Garda (Italy) using Hyperion. Remote Sensing of Environment, 2007, 109(2): 183-195. DOI:10.1016/j.rse.2006.12.017 |
| [83] |
Vincent RK, Qin XM, McKay RML et al. Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sensing of Environment, 2004, 89(3): 381-392. DOI:10.1016/j.rse.2003.10.014 |
| [84] |
Becker RH, Sultan MI, Boyer GL et al. Mapping cyanobacterial blooms in the Great Lakes using MODIS. Journal of Great Lakes Research, 2009, 35(3): 447-453. DOI:10.1016/j.jglr.2009.05.007 |
| [85] |
Hunter PD, Tyler AN, Willby NJ et al. The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: A case study using high spatial resolution time-series airborne remote sensing. Limnology and Oceanography, 2008, 53(6): 2391-2406. DOI:10.4319/lo.2008.53.6.2391 |
| [86] |
Schalles JF, Yacobi YZ. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll pigments in eutrophic waters. Ergebnisse Der Limnologie, 2000, 55: 153-168. |
| [87] |
Dash P, Walker ND, Mishra DR et al. Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data. Remote Sensing of Environment, 2011, 115(12): 3409-3423. DOI:10.1016/j.rse.2011.08.004 |
| [88] |
Dekker AG. Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing[Dissertation]. Amsterdam: Vrije University, 1993: 1-240.
|
| [89] |
Qi L, Hu CM, Duan HT et al. An EOF-based algorithm to estimate chlorophyll a concentrations in Taihu Lake from MODIS land-band measurements: Implications for near real-time applications and forecasting models. Remote Sensing, 2014, 6(11): 10694-10715. DOI:10.3390/rs61110694 |
| [90] |
Qi L, Hu CM, Duan HT et al. A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations. Remote Sensing of Environment, 2014, 154: 298-317. DOI:10.1016/j.rse.2014.08.026 |
| [91] |
Hunter PD, Tyler AN, Carvalho L et al. Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes. Remote Sensing of Environment, 2010, 114(11): 2705-2718. DOI:10.1016/j.rse.2010.06.006 |
| [92] |
Stumpf RP, Wynne TT, Baker DB et al. Interannual variability of cyanobacterial blooms in Lake Erie. PLoS One, 2012, 7(8): e42444. DOI:10.1371/journal.pone.0042444 |
| [93] |
Simis SGH, Peters SWM, Gons HJ. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnology and Oceanography, 2005, 50(1): 237-245. DOI:10.4319/lo.2005.50.1.0237 |
| [94] |
Le CF, Li YM, Zha Y et al. Remote estimation of chlorophyll a in optically complex waters based on optical classification. Remote Sensing of Environment, 2011, 115(2): 725-737. DOI:10.1016/j.rse.2010.10.014 |
| [95] |
Simis SGH, Ruiz-Verdú A, Domínguez-Gómez JA et al. Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass. Remote Sensing of Environment, 2007, 106(4): 414-427. DOI:10.1016/j.rse.2006.09.008 |
| [96] |
Su W, Jiang GJ, Ma RH et al. Vertical distribution of optically active water components and its influence on remote sensing reflectance in a eutrophic water. Acta Scientiae Circumstantiae, 2016, 36(10): 3589-3599. [苏文, 姜广甲, 马荣华等. 富营养化水体中光学活性物质的垂向分布及其对遥感反射光谱的影响. 环境科学学报, 2016, 36(10): 3589-3599. DOI:10.13671/j.hjkxxb.2016.0033] |
| [97] |
Salem S, Strand M, Higa H et al. Evaluation of MERIS chlorophyll-a retrieval processors in a complex turbid Lake Kasumigaura over a 10-year mission. Remote Sensing, 2017, 9(10): 1022. DOI:10.3390/rs9101022 |
| [98] |
Li XW, Jiang S, Zhang Y et al. Maximum peak height (MPH) algorithm applied to sentinel-3 OLCI data for retrieving chlorophyll-a and distinguishing cyanobacteria and floating vegetation areas in Lake Taihu. Environmental Monitoring and Forewarning, 2019, 11(5): 59-65. [李旭文, 姜晟, 张悦等. "哨兵-3" 卫星OLCI影像MPH算法反演太湖叶绿素a及藻草区分的研究. 环境监控与预警, 2019, 11(5): 59-65.] |
| [99] |
Li XW, Niu ZC, Jiang S. Study of reflectance spectra characteristics of cyano-bacterica blooms in Lake Taihu on Landsat5 thematic mapper imagery. The Administration and Technique of Environmental Monitoring, 2010, 22(6): 25-31. [李旭文, 牛志春, 姜晟. Landsat5 TM遥感影像上太湖蓝藻水华反射光谱特征研究. 环境监测管理与技术, 2010, 22(6): 25-31. DOI:10.3969/j.issn.1006-2009.2010.06.006] |
| [100] |
Cao ZG, Ma RH, Duan HT et al. 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, 2019, 153: 110-122. DOI:10.1016/j.isprsjprs.2019.05.001 |
| [101] |
Cao ZG, Ma RH, Duan HT et al. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sensing of Environment, 2020, 248: 111974. DOI:10.1016/j.rse.2020.111974 |
| [102] |
Song T, Gong SQ, Liu JZ et al. Performance assessment of atmospheric correction for multispectral data of GF-4 on inland case Ⅱ turbid water. Spectroscopy and Spectral Analysis, 2018, 38(4): 1191. [宋挺, 龚绍琦, 刘军志等. 浑浊二类水体的高分四号卫星大气校正效果分析. 光谱学与光谱分析, 2018, 38(4): 1191.] |
| [103] |
Ma RH, Zhang YC, Duan HT. The status and development of the non-traditional lake water color remote sensing. J Lake Sci, 2016, 28(2): 237-245. [马荣华, 张玉超, 段洪涛. 非传统湖泊水色遥感的现状与发展. 湖泊科学, 2016, 28(2): 237-245. DOI:10.18307/2016.0201] |
| [104] |
Matthews MW, Bernard S, Robertson L. An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sensing of Environment, 2012, 124: 637-652. DOI:10.1016/j.rse.2012.05.032 |
| [105] |
Li J, Zhang YC, Ma RH et al. 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, 2017, 10(2): 450-462. DOI:10.1109/JSTARS.2016.2601083 |
| [106] |
Li J, Ma RH, Xue K et al. A remote sensing algorithm of column-integrated algal biomass covering algal bloom conditions in a shallow eutrophic lake. ISPRS International Journal of Geo-Information, 2018, 7(12): 466. DOI:10.3390/ijgi7120466 |
| [107] |
Xue K. The effect of the vertical distribution of algae in Chaohu Lake on the remote sensing reflectance of the water body[Dissertation]. Nanjing: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 2016. [薛坤. 巢湖藻类垂向分布对水体遥感反射比的影响研究[学位论文]. 南京: 中国科学院南京地理与湖泊研究所, 2016. ]
|
| [108] |
Xue K, Zhang YC, Duan HT et al. Variability of light absorption properties in optically complex inland waters of Lake Chaohu, China. Journal of Great Lakes Research, 2017, 43(1): 17-31. DOI:10.1016/j.jglr.2016.10.006 |
| [109] |
Xue K, Zhang YC, Ma RH et al. An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters. Limnology and Oceanography: Methods, 2017, 15(3): 302-319. DOI:10.1002/lom3.10158 |
| [110] |
Uitz J, Claustre H, Morel A et al. Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll. Journal of Geophysical Research: Oceans, 2006, 111(C8): C08005. DOI:10.1029/2005JC003207 |
| [111] |
Zhang YX, Zhang YC, Zhou W et al. Inherent optical properties of typical cyanobacteria in eutrophic lakes. J Lake Sci, 2018, 30(6): 1681-1692. [张壹萱, 张玉超, 周雯等. 富营养化湖泊典型水华蓝藻的固有光学特性. 湖泊科学, 2018, 30(6): 1681-1692. DOI:10.18307/2018.0620] |
| [112] |
Utermhl H. Zur vervollkommung der quantitativen phytoplankton-methodik. Mitteilungen Internationale Verein Limnologie Stuttgard, 1958, 9: 263-272. DOI:10.1080/05384680.1958.11904091 |
| [113] |
Aldrich J, Cullis CA. RAPD analysis in flax: Optimization of yield and reproducibility using klenTaq 1 DNA polymerase, chelex 100, and gel purification of genomic DNA. Plant Molecular Biology Reporter, 1993, 11(2): 128-141. DOI:10.1007/BF02670471 |
| [114] |
Abaychi JK, Riley JP. The determination of phytoplankton pigments by high-performance liquid chromatography. Analytica Chimica Acta, 1979, 107: 1-11. DOI:10.1016/S0003-2670(01)93190-3 |
| [115] |
Gao HF, Jiao NZ. Research progress on marine phytoplankton biomass and community composition determined from algal pigment analyses. Marine Sciences, 1997, 21(3): 51-54. [高洪峰, 焦念志. 通过藻类色素分析估测海洋浮游植物生物量和群落组成的研究进展. 海洋科学, 1997, 21(3): 51-54.] |
| [116] |
Ma WQ. Research on Microcystis bio-optical properties and remote sensing recognition[Dissertation]. Nanjing: Nanjing Normal University, 2012. [马万泉. 微囊藻生物光学特性与遥感识别研究[学位论文]. 南京: 南京师范大学, 2012. ]
|
| [117] |
Dai HL. Scattering characteristics and theoretical simulation of main algae species in freshwater lakes[Dissertation]. Nanjing: Nanjing Normal University, 2013. [戴红亮. 淡水湖泊主要藻种的散射特性和理论模拟研究[学位论文]. 南京: 南京师范大学, 2013. ]
|
| [118] |
Dai HL, Lv H, Li YM et al. A theoretical modeling of light absorption and scattering properties about Microcystis aeruginosa. Spectroscopy and Spectral Analysis, 2013, 33(6): 1462. [戴红亮, 吕恒, 李云梅等. 铜绿微囊藻吸收和散射特性理论模拟. 光谱学与光谱分析, 2013, 33(6): 1462. DOI:10.3964/j.issn.1000-0593(2013)06-1462-06] |
| [119] |
Lv H, Dai HL, Li YM et al. Simulating the light absorption and scattering properties of Microcystis aeruginosa using a two-layered spherical geometry. Acta Optica Sinica, 2013, 33(12): 1229002. [吕恒, 戴红亮, 李云梅等. 铜绿微囊藻吸收和散射特性两层球形模型模拟. 光学学报, 2013, 33(12): 1229002. DOI:10.3788/AOS201333.1229002] |
| [120] |
Wang Y. Study on the estimation of the proportion of Microcystis aeruginosa in Lake Taihu based on absorption spectrum[Dissertation]. Nanjing: Nanjing Normal University, 2014. [王瑜. 基于吸收光谱的太湖铜绿微囊藻比例估算研究[学位论文]. 南京: 南京师范大学, 2014. ]
|
| [121] |
Wang YC, Lu KH. Harm and control about cyanobacterial bloom. Chinese Journal of Fisheries, 2004, 17(1): 90-94. [王扬才, 陆开宏. 蓝藻水华的危害及治理动态. 水产学杂志, 2004, 17(1): 90-94. DOI:10.3969/j.issn.1005-3832.2004.01.019] |
| [122] |
Zhang M, Zhang YC, Yang Z et al. Spatial and seasonal shifts in bloom-forming cyanobacteria in Lake Chaohu: Patterns and driving factors. Phycological Research, 2016, 64(1): 44-55. DOI:10.1111/pre.12112 |
| [123] |
Yang DT, Pan DL. Progress in the research on cyanobacteria remote sensing. Remote Sensing for Land & Resources, 2006(4): 1-5. [杨顶田, 潘德炉. 蓝藻的卫星遥感研究进展. 国土资源遥感, 2006(4): 1-5. DOI:10.3969/j.issn.1001-070X.2006.04.001] |
| [124] |
Chu Q, Zhang YC, Ma RH et al. MODIS-based remote estimation of absorption coefficients of an inland turbid lake in China. Remote Sensing, 2020, 12(12): 1940. DOI:10.3390/rs12121940 |