基于Landsat 8 OLI数据的镜泊湖水体叶绿素a浓度反演

doi:10.14088/j.cnki.issn0439-8114.2021.23.035

湖北农业科学 ›› 2021, Vol. 60 ›› Issue (23): 157-162.doi: 10.14088/j.cnki.issn0439-8114.2021.23.035

基于Landsat 8 OLI数据的镜泊湖水体叶绿素a浓度反演

刘宇, 朱丹瑶

牡丹江师范学院历史与文化学院,黑龙江牡丹江 157012

收稿日期:2021-04-02 出版日期:2021-12-10 发布日期:2021-12-21
作者简介:刘宇（1985-）,男,辽宁丹东人,讲师,硕士,主要从事环境遥感研究及教学工作,（电话）18604532723（电子信箱）308391826@qq.com。
基金资助:
黑龙江省教育厅基本科研业务费项目（1355MSYQN007）; 牡丹江师范学院科研项目（QN2021005）

Inversion of Chl-a concentration in Jingpo lake based on Landsat 8 OLI data

LIU Yu, ZHU Dan-yao

College of History and Culture,Mudanjiang Normal University,Mudanjiang 157012,Heilongjiang,China

Received:2021-04-02 Online:2021-12-10 Published:2021-12-21

摘要/Abstract

摘要： 为选择适合镜泊湖的叶绿素a遥感监测模型,结合2018年7月实测叶绿素a浓度与同步Landsat 8 OLI影像,建立了叶绿素a浓度简单线性模型（SLR）与多元线性模型（MLR）,并根据拟合度和验证精度最优原则,评估了2种模型的预测效果。结果表明,包含4个波段的MLR模型（B₂、B₃、B₄和B₅波段）反演精度优于其他模型。从叶绿素a空间分布状况来看,镜泊湖中心地带为研究区的低值区,支流入湖口地带为研究区的高值区。

关键词: 镜泊湖, 叶绿素a, 遥感反演, 卫星影像, 湖泊水质

Abstract: In order to select the Chl-a remote sensing monitoring model suitable for Jingpo lake, combined with the measured Chl-a concentration in July 2018 and the synchronous Landsat 8 OLI image, this study established the Simple Linear Regression （SLR） and Multiple Linear Regression （MLR） of Chl-a concentration, and evaluated the prediction effects of the two models according to the principle of optimal fitting degree and verification accuracy. The results showed that the MLR model with four bands （B₂, B₃, B₄ and B₅ bands） has better inversion accuracy than other models. From the spatial distribution of Chl-a, the center of the lake is the low-value area, and the estuary of the river is the high-value area。

Key words: Jinpo lake, Chl-a, remote inversion, satellite imagery, lake water quality

中图分类号:

TP79

刘宇, 朱丹瑶. 基于Landsat 8 OLI数据的镜泊湖水体叶绿素a浓度反演[J]. 湖北农业科学, 2021, 60(23): 157-162.

LIU Yu, ZHU Dan-yao. Inversion of Chl-a concentration in Jingpo lake based on Landsat 8 OLI data[J]. HUBEI AGRICULTURAL SCIENCES, 2021, 60(23): 157-162.

参考文献 30

[1]	赵文宇,姜亮亮,焦键,等. 基于Landsat8数据的东道海子叶绿素a的遥感反演和监测研究[J]. 新疆环境保护,2017,39(3): 28-35.
[2]	宋月君,莫明浩,汪邦稳,等. 基于神经网络模型的水源地水质参数反演研究[J]. 中国农村水利水电,2011(2):98-100.
[3]	MOHAMMAD G, ASSEFA M, LAKSHMI R.A comprehensive review on water quality parameters estimation using remote sensing techniques[J]. Sensors,2016,16(8):1298-1310.
[4]	WILSON C.The rocky road from research to operations for satellite ocean-colour data in fishery management[J]. Ices journal of marine science,2011,68(4):677-686.
[5]	YANG X E, WU X, HAO H L, et al.Mechanisms and assessment of water eutrophication[J]. Journal of Zhejiang university science B, 2008, 9(3):197-209.
[6]	BEHRENFELD M J, O’MALLEY R T, SIEGEL D A, et al. Climate-driven trends in contemporary ocean productivity[J]. Nature, 2006, 444(7120):752-755.
[7]	冯驰, 金琦,王艳楠,等. 基于GOCI影像和水体光学分类的内陆湖泊叶绿素a浓度遥感估算[J].环境科学,2015,36(5):1557-1564.
[8]	RANDOLPH K, WILSON J, TEDESCO L, et al.Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin[J]. Rem sens environ,2008,112(11):4009-4019.
[9]	BLIX K, ELTOFT T.Machine learning automatic model selection algorithm for oceanic chlorophyll-a content retrieval[J]. Remote sensing,2018,10(5): 1-21.
[10]	O'REILLY J E,MARITORENA S,MITCHELL B G,et al. Ocean color chlorophyll algorithms for SeaWiFS[J]. Journal of geophysical research oceans,1998,103(C11):24937-24953.
[11]	汪星, 刘录三, 李黎, 等. 镜泊湖浮游藻类组成及其与环境因子的相关分析[J].中国环境科学,2015,35(11):3403-3413.
[12]	BABAN S M J. Detecting water quality parameters in the Norfolk Broads, UK, using Landsat imagery[J]. International journal of remote sensing,1993,14(7):1247-1267.
[13]	冯奇,程学军,沈欣,等. 利用Landsat 8 OLI进行汉江下游水体浊度反演[J]. 武汉大学学报(信息科学版),2017,42(5):643-647.
[14]	王小平,张飞,ABDUWASIT G,等. 艾比湖流域地表水水质指标与水体指数关系研究[J].环境科学学报,2017,37(3):900-909.
[15]	FERNANDA W, ENNER A, THANAN R, et al.Estimation of chlorophyll-a concentration and the trophic state of the barra bonita hydroelectric reservoir using OLI/Landsat-8 Images[J]. International journal of environmental research and public health,2015,12(9):10391-10417.
[16]	KIM H H, KO B C, NAM J Y.Predicting chlorophyll-a using Landsat 8 OLI sensor data and the non-linear RANSAC method-a case study of Nakdong river, South Korea[J]. Int J remote sens, taylor & francis, 2016,37: 3255-3271.
[17]	MATUS-HERNÁNDEZ, MIGUEL ÁNGEL, HERNÁNDEZ-SAAVEDRA, et al. Predictive performance of regression models to estimate chlorophyll-a concentration based on Landsat imagery[J]. PLoS one,2018,13(10):1-18.
[18]	MALAHLELA O E, OLIPHANT T, TSOELENG L T, et al.Mapping chlorophyll-a concentrations in a cyanobacteria- and algae-impacted Vaal Dam using Landsat 8 OLI data[J]. South African journal of science, 2018, 114(9/10):1-9.
[19]	LIM J, CHOI M.Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea[J]. Environmental monitoring and assessment,2015,187(6):384-400.
[20]	PATRA P P, DUBEY S K, TRIVEDI R K, et al.Estimation of chlorophyll-a concentration and trophic states in Nalban lake of East Kolkata Wetland, India from Landsat 8 OLI data[J]. Spatial information research, 2017:1-13.
[21]	李苗, 臧淑英, 吴长山. 基于TM影像的克钦湖叶绿素a浓度反演[J]. 农业环境科学学报, 2012, 31(12): 2473-2479.
[22]	BRIVIO P A, GIARDINO C, ZILIOLI E.Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images[J]. International journal of remote sensing,2001,22(2-3):487-502.
[23]	JOSHI I, D'SA E. Seasonal variation of colored dissolved organic matter in Barataria bay, Louisiana, using combined landsat and field data[J]. Remote sensing,2015,7(9):12478-12502.
[24]	DALU T, DUBE T, FRONEMAN P W, et al.An assessment of chlorophyll-a concentration spatio-temporal variation using Landsat satellite data, in a small tropical reservoir[J]. Geocarto international,2015,30(10):1130-1143.
[25]	HASSANI M,CHABOU M C,HAMOUDI M,et al.Index of extraction of water surfaces from Landsat 7 ETM+ images[J]. Arabian journal of geosciences,2015,8(6):3381-3389.
[26]	KALLIO K, ATTILA J, HÄRMÄ P, et al. Landsat ETM+ images in the estimation of seasonal lake water quality in boreal river basins[J]. Environ manage,2008,42(3):511-522.
[27]	YIN G, MARIETHOZ G, MCCABE M F.Gap-filling of Landsat 7 imagery using the direct sampling method[J]. Rem sens,2016,9(1):12-20.
[28]	TORBICK N, CORBIERE M.A multiscale mapping assessment of Lake Champlain cyanobacterial harmful algal blooms[J]. Int J environ res pub health,2015, 12(9): 11560-11578.
[29]	GITELSON A A, DALL’OLMO G, MOSES W, et al. A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation[J]. Rem sens environ,2008,112(9):3582-3593.
[30]	DALL’OLMO G, GITELSON A A. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters:Experimental results[J]. Appl Opt,2005,44(3):412-422.

基于Landsat 8 OLI数据的镜泊湖水体叶绿素a浓度反演

Inversion of Chl-a concentration in Jingpo lake based on Landsat 8 OLI data

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