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遥感技术与应用  2021, Vol. 36 Issue (3): 473-488    DOI: 10.11873/j.issn.1004-0323.2021.3.0473
综述     
水质遥感监测的关键要素叶绿素a的反演算法研究进展
罗婕纯一1(),秦龙君1,毛鹏1,熊育久2,赵文利1,高辉辉1,邱国玉1()
1.北京大学环境与能源学院,广东 深圳 518055
2.中山大学土木工程学院,广东 广州 510275
Research Progress in the Retrieval Algorithms for Chlorophyll-a, a Key Element of Water Quality Monitoring by Remote Sensing
Jiechunyi Luo1(),Longjun Qin1,Peng Mao1,Yujiu Xiong2,Wenli Zhao1,Huihui Gao1,Guoyu Qiu1()
1.School of Environment and Energy,Peking University,Shenzhen 518055,China
2.School of Civil Engineering,Sun Yat-Sen University,Guangzhou 510275,China
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摘要:

叶绿素a浓度是表征水体富营养化程度的重要指标,通过遥感手段反演叶绿素a浓度是实现水体富营养化监测的一个有效途径,已衍生出了一系列叶绿素a浓度反演算法。这些算法各有所长,适用范围也各自有别。由于水体光学特征差异,盲目套用这些算法难以取得预期效果。为了推动水质遥感的进一步发展,从遥感反演的原理和数据源出发,对国内外利用遥感技术反演水体叶绿素a浓度的算法进行综述。根据算法结构设计的不同,将反演算法分为6大类,分别为荧光峰和反射峰算法、波段算法、指数算法、智能算法、基于水体分类的算法体系以及分析类算法,系统地梳理各类算法并分析算法特征。从算法适用的叶绿素a浓度区间和水体类型等角度出发,总结各类算法的适用范围,评述各类算法的优缺点,以期为环境和遥感工作者提供参考。主要结论如下:①Ⅱ类水体算法外推适应性较弱,应建立并补充实测数据集,研究各类水体光学特性异同点,构建基于水体分类的通用算法体系;②无人机技术与高光谱传感器的结合可为内陆水体水质监测提供新思路;③应结合机器学习算法与机理模型,发展物理原理约束的高精度反演模型。

关键词: 叶绿素a遥感水体水质富营养化    
Abstract:

Chlorophyll-a concentration is an important proxy for defining the tropic status of various bodies of water. Using remote sensing technology to retrieve chlorophyll-a concentration is an effective method for water eutrophication monitoring and a great number of algorithms for chlorophyll-a concentration retrieval are developed. These algorithms have different advantages and ranges of application. Because the optical characteristics vary in different bodies of water, it is hard to achieve desired results if blindly applying algorithms. In order to promote the further development of water quality remote sensing, the theory and data sources of remote sensing inversion are introduced.Then,domestic and foreign algorithms of retrieving chlorophyll-a concentration in water by remote sensing are summarized.The algorithms studied are categorized into six types by their architectural designs, namely: fluorescence peak and maximum peak algorithms, band algorithms, chlorophyll-a index algorithms,artificial intelligence algorithms,algorithm systems based on optical water types and analytical algori-thms.Each algorithm is presented systematically and its characteristics are analyzed.Then,all the aforementioned algorithms are compared regarding their applicable range of chlorophyll-a concentrations as well as water types.The applicability, merits and demerits of each category of algorithms are analyzed and concluded in order to provide reference for environmental and remote sensing researchers.The main conclusions are as follows:①the algorithm applicability for Case II waters is limited. More in-situ observations should be conducted to establish and supplement the database. Similarities and difference of various optical water types should be further studied to establish global algorithm systems based on optical water typologies; ②The combination of UAVs and hyperspectral sensors could provide new thoughts in monitoring inland water quality; ③Machine learning algorithms and mechanism models should be integrated to develop physical constrained models with high accuracy.

Key words: Chlorophyll-a    Remote sensing    Water body    Water quality    Eutrophication
收稿日期: 2020-04-20 出版日期: 2021-07-22
ZTFLH:  X87  
基金资助: 国家重点研发计划项目(YS2017YFGH000958);深圳市基础研究计划项目(JCYJ20180504165440088)
通讯作者: 邱国玉     E-mail: lomjekry@pku.edu.cn;qiugy@pkusz.edu.cn
作者简介: 罗婕纯一(1997-),女,江西宜春人,硕士研究生,主要从事水质遥感研究。E?mail: lomjekry@pku.edu.cn
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引用本文:

罗婕纯一,秦龙君,毛鹏,熊育久,赵文利,高辉辉,邱国玉. 水质遥感监测的关键要素叶绿素a的反演算法研究进展[J]. 遥感技术与应用, 2021, 36(3): 473-488.

Jiechunyi Luo,Longjun Qin,Peng Mao,Yujiu Xiong,Wenli Zhao,Huihui Gao,Guoyu Qiu. Research Progress in the Retrieval Algorithms for Chlorophyll-a, a Key Element of Water Quality Monitoring by Remote Sensing. Remote Sensing Technology and Application, 2021, 36(3): 473-488.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.3.0473        http://www.rsta.ac.cn/CN/Y2021/V36/I3/473

图1  卫星传感器接收水体及大气辐射信息示意图

星载

传感器

光谱范围及波段数空间分辨率 /m时间分辨率
CZCS6个波段0.43~12.50 μm8256 d
SeaWiFS8个波段0.41~0.87 μm1 1001 d
MODIS36个波段0.40~14.40 μm250、500、1 0002次/d
MERIS15个波段0.40~1.05 μm3003 d
VIIRS22个波段0.40~12.50 μm375、7504 h
GOCI8个波段0.40~0.88 μm5008次/d
HICO128个波段0.36~1.08 μm901~3 d
OLI9个波段0.43~2.30 μm15、3016 d
Hyperion220个波段0.40~2.50 μm3016 d
HSI128个波段0.45~0.95 μm1004 d
CCD4个波段0.43~0.90 μm302 d
WFV4个波段0.45~0.89 μm164 d
表1  星载传感器参数比较
图2  算法应用的叶绿素a浓度区间
图3  算法适用的水体类型

算法

类别

算法叶绿素浓度区间(mg/m3)水体所用波段/nm卫星传感器R2RMSE /(mg/m3)作者优势缺陷

荧光峰

算法

FLH1~15.30温哥华岛西海岸、波罗的海620、665、681.25、705、754MERIS0.70~0.90/[40]在很大程度上排除CDOM和水底的干扰;对大气校正误差不敏感荧光峰的位置受到叶绿素a及其他物质浓度的影响,其中FEA算法受影响较小
FLH0.40~4佛罗里达西南部沿海水域MODIS FLH 667、678、748MODIS0.85/[41]

FLH BLUE/FLH

VIOLET

30~80俄亥俄州西南部的温带水库BLUE(545、605、480);VIOLET(545、605、425)Landsat 8

BLUE:0.38;

VIOLET:0.55

/[28]
NFH0.10~350多国河湖560、675、700/0.93/[45]
FEA1~224.28珠江口671.02~752.43Hyperion/0.22(log10)[30]

反射峰

算法

MPH0.50~350南非西海岸、湖泊及水库max(681、709、753);885、664MERIS0.71/[46]在富营养化、表面水华爆发的水体中表现较好;对大气校正误差不敏感反射峰位置受叶绿素a浓度影响;MCI在贫营养水体中不太适用且受沉积物散射影响较大
MCI10~300安大略湖、伍兹湖、伊利湖681、709、753MERIS0.70~0.94/[47]

蓝绿

波段

比值法

OCx0.008~90全球多个大洋及海岸根据不同传感器和算法,所选波段有所不同SeaWiFs、CZCS、OTCS、MODIS、MERIS//[18]在大洋类水体中应用较为成熟在复杂二类水体中表现较差

近红外-红光

波段

比值法

2BDA0.63~65.51塔甘罗格湾、亚速海岸708、665MERIS0.973.65[51]

考虑了悬浮物和黄色物质的影响,在二类水体中表现较佳

第三波段选取需满足一系列假设条件;不适用于高度浑浊的水体

2.27~200.81内布拉斯加州弗里蒙特湖区

MERIS:band7,9;

MODIS: band13,15

MERIS,

MODIS

MERIS:0.95;

MODIS:0.75

MERIS:3.6;MODIS:6.1[20]
2~100内布拉斯加州弗里蒙特湖区708、665MERIS0.96/[50]
3BDA0.63~65.51塔甘罗格湾、亚速海岸708、665、753MERIS0.955.02[51]
7.80~154.30太湖691.37、721.90、854.18Hyperion0.8713.93[54]
2.27~200.81内布拉斯加州弗里蒙特湖区band7、9、10MERIS0.953.30[20]
2~100内布拉斯加州弗里蒙特湖区708、665、753MERIS0.96/[50]
3~185荷兰和中国多湖704、672、776>0.95/[56]
3~185多个河湖及河口704、664、776MERIS0.96/[57]
4BDA4~158太湖662、693、740、7050.979.74[6]进一步消除了悬浮颗粒吸收和后向散射的影响对传感器光谱分辨率要求较高
表2  水体叶绿素a反演算法比较及适应性分析

算法

类别

算法叶绿素浓度区间(mg/m3)水体所用波段/nm卫星传感器R2RMSE /(mg/m3)作者优势缺陷

其他

指数法

Hu指数0.01~1078%的全球海洋443、555、670SeaWiFs, MODIS0.95/[21]受叶绿素a后向散射和非浮游植物吸收作用影响较小;受仪器噪声和大气校正误差影响较小主要针对叶绿素a浓度小于0.25mg/m3的海洋,在富营养水体中适用性有待验证
Yang指数22.98~318.60日本霞浦湖及中国滇池band7、9、10MERIS

日本:0.90

中国:0.91

日本:8.68

中国: 15.82

[59]较3BDA更适用于高浑浊水体,对传感器分辨率要求比4BDA更低受大气校正误差影响较大
NDCI1~60多个海湾和三角洲708、665MERIS0.954.83[60]在缺乏实测数据、偏远地区也可以使用受大气校正误差影响较大
SCI

春:0.03~3.10;

夏:0.88~31.50

长江河口

(春、夏)

620、681、560、665MERIS

春:0.72

夏:0.91

春:0.86夏:2.87[23]适用于高悬浮物浓度水体受大气校正误差影响大

智能

算法

ANN-BP2006:0.06~0.32;2008:0.03~8.84地中海东海岸/Hyperion2006:0.89 2008:0.962006:0.03 2008:0.45[31]在解决非线性问题上有优势,适用于光学组分复杂的水体对训练集的数据质量要求较高;算法复杂程度高
MLPNN0.02~70/412、443、488、531、547、667MODIS0.900.22(log10)[61]
ANN-BP0~120太湖b4/b3;b4/b2;b4/b1;b4/(b1+b2+b3)GF-1 WFV40.977.61[34]
LS-SVM15~75清河水库b5/b4Landsat OLI0.972.67[29]在解决小样本、非线性问题上具有优势研究样本量和采样周期均有限,算法普适性待进一步验证
LS-SVM1~64大伙房水库b2~b1环境卫星 CCD0.82/[33]
基于水体分类的算法体系动态变化算法体系0~1 000185个内陆和沿海水域//0.79/[63]样本数据覆盖13种水体,算法普适性高实际应用相对较复杂,需先判断水体类型,后选择推荐的相应算法
分析类算法GSM0.04~5大洋类/SeaWiFs0.820.19[67]具有明确的物理意义,普适性较高理论推导较复杂,参数准确获取难度高
QAA0.03~30/////[68]
/0~100太湖//0.99/[65]
/0.20~11.60珠江口、韩江河口、徐闻珊瑚礁保护区////[70]
表2  水体叶绿素a反演算法比较及适应性分析(续)
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