HY-1C/D卫星CZI数据监测湖泊藻华的适用性评价与方法选择

薛坤; 马荣华; 曹志刚; 胡旻琪; 李佳鑫

doi:10.11834/jrs.20232361

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HY-1C/D卫星CZI数据监测湖泊藻华的适用性评价与方法选择
Applicability evaluation and method selection in detecting cyanobacterial bloom using HY-1C/D CZI data for inland lakes
2023年27卷第1期页码：171-186
纸质出版日期： 2023-01-07 ，
DOI： 10.11834/jrs.20232361

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薛坤，马荣华，曹志刚，胡旻琪，李佳鑫.2023.HY-1C/D卫星CZI数据监测湖泊藻华的适用性评价与方法选择.遥感学报，27（1）： 171-186

Xue K，Ma R H，Cao Z G，Hu M Q and Li J X. 2023. Applicability evaluation and method selection in detecting cyanobacterial bloom using HY-1C/D CZI data for inland lakes. National Remote Sensing Bulletin， 27（1）：171-186
薛坤，马荣华，曹志刚，胡旻琪，李佳鑫.2023.HY-1C/D卫星CZI数据监测湖泊藻华的适用性评价与方法选择.遥感学报，27（1）： 171-186 DOI： 10.11834/jrs.20232361.

Xue K，Ma R H，Cao Z G，Hu M Q and Li J X. 2023. Applicability evaluation and method selection in detecting cyanobacterial bloom using HY-1C/D CZI data for inland lakes. National Remote Sensing Bulletin， 27（1）：171-186 DOI： 10.11834/jrs.20232361.

摘要

随着湖泊富营养化加剧，蓝藻水华频发暴发，藻华的卫星遥感监测面临着中高空间分辨率卫星过境时间长、中低空间分辨率卫星数据在小型湖泊监测能力不足的问题。海洋水色业务卫星海洋一号C星和D星（HY-1C/D）搭载的海岸带成像仪CZI（Coastal Zone Imager）具有50 m空间分辨率，3 d重访周期，是内陆中小型湖泊藻华遥感监测的重要数据源。本文构建了以CZI数据的绿光（560 nm）—近红外波段（825 nm）连线作为基线，红光波段（650 nm）到该基线的垂直距离作为藻华识别指数AFAH（Adjusted Floating Algae Height），并将其应用在4个典型的富营养化湖泊（太湖、巢湖、滇池、星云湖），评价了其在湖泊藻华遥感监测方面的适用性和不确定性。结果表明：（1）在无云的理想条件下4种藻华识别指数的藻华识别精度高于0.93；在非理性条件下的藻华识别方面，AFAH优于NDVI（Normalized Difference Vegetation Index）、DVI（Difference Vegetation Index）和VB-FAH（Virtual baseline floating macroAlgae Height），对太阳耀斑、云、气溶胶厚度敏感性较低；（2）采用最大梯度法确定了单景影像的AFAH的藻华提取阈值，太湖、巢湖、滇池、星云湖有藻华的数据共180景，AFAH藻华提取阈值的统计均值为0.041，标准差为0.013；（3）2019年7月到2021年7月期间4个湖泊的藻华发生频率空间分布结果与已有研究一致，藻华暴发频率大于5%的面积分别为609.05 km

、134.43 km

、20.91 km

、14.50 km

，主要集中在太湖竺山湾、梅梁湾、太湖西部，巢湖西部，滇池和星云湖分散状全湖分布。研究表明，AFAH可以应用于仅包含一个近红外波段的中高空间分辨率卫星，可拓展到多源卫星数据的藻华业务化监测，以提高观测频次。

Abstract

Intense cyanobacterial blooms often occur in eutrophic lakes

satellite data with high spatial resolution often has low temporal frequency

and nearly daily revisiting satellite data has coarse spatial resolution

limiting the monitoring of floating blooms in small lakes. The Coastal Zone Imager (CZI) onboard HY-1C/1D satellite provides a new data source to monitor the cyanobacterial bloom of inland lakes with 50 m spatial resolution

revisit time of 3 d. An index

namely the Adjusted Floating Algae Height (AFAH)

is developed based on the difference between red band (

(650))

and a baseline between green band (560 nm) and NIR band (near infrared

825 nm). AFAH was then applied in four eutrophic lakes

for instance

Lake Taihu

Lake Chaohu

Lake Dianchi

and Lake Xingyun

and its advantages and uncertainties in monitoring cyanobacterial blooms were evaluated. The results showed that: (1) In cloudless conditions

AFAH

NDVI (Normalized Difference Vegetation Index)

DVI (Difference Vegetation Index)

and VB-FAH (Virtual baseline floating macroAlgae Height) have high accuracy larger than 0.93. AFAH has advantages over NDVI

EVI

and VA-FAH

as it is less sensitivity to the solar/viewing geometry

aerosol type and thickness

thin cloud

and sun glint. (2) The maximum gradient method is used to derive the AFAH threshold to extract bloom pixels

total of 180 images with cyanobacterial blooms are used to calculate the mean (0.041) and standard deviation (0.013) of AFAH threshold values. (3) The spatial distribution of cyanobacterial blooms from July

2019 to July

2021 in Lake Taihu

Lake Chaohu

Lake Dianchi

and Lake Xingyun is accordance with the previous studies. The areas with bloom frequency larger than 5% are 609.05 km

134.43 km

20.91 km

and 14.50 km

for Lake Taihu

Lake Chaohu

Lake Dianchi

and Lake Xingyun. The Zhushan Bay

Meiliang Bay

and western part of Lake Taihu

the western part of Lake Chaohu

and most part of Lake Dianchi and Lake Xingyun have high frequency of floating blooms. This study indicated that AFAH has good performance in detecting floating blooms using satellite data with only one NIR band

and multi-source satellite data should be combined in the following study in order to improve the temporal frequency of bloom monitoring.

关键词

湖泊水色遥感富营养化湖泊蓝藻水华HY-1C/D海岸带成像仪CZI光谱基线法

Keywords

water color remote sensingeutrophic lakescyanobacterial bloomHY-1C/D satelliteCoastal Zone Imager (CZI)baseline subtraction method

references

Cao M M. 2021. Remote Sensing Monitoring of Algal Blooms in Hulun Lake based on a Novel Spectral Index ABDI. Huhhot: Inner Mongolia Normal University

曹萌萌. 2021. 基于一种新的光谱指数ABDI的呼伦湖藻华遥感监测. 呼和浩特: 内蒙古师范大学

Cao Z G, Ma R H, Liu J Q and Ding J. 2021. Improved radiometric and spatial capabilities of the coastal zone imager onboard Chinese HY-1C satellite for inland lakes. IEEE Geoscience and Remote Sensing Letters, 18(2):193-197 [DOI: 10.1109/LGRS.2020.2971629http://dx.doi.org/10.1109/LGRS.2020.2971629]

de Lucia Lobo F, Nagel G W, Maciel D A, de Carvalho L A S, Martins V S, Barbosa C C F and de Moraes Novo E M L. 2021. AlgaeMAp: algae bloom monitoring application for inland waters in Latin America. Remote Sensing, 13(15): 2874 [DOI: 10.3390/rs13152874http://dx.doi.org/10.3390/rs13152874]

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, 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

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 [DOI: 10.11834/jrs.20221301http://dx.doi.org/10.11834/jrs.20221301]

Fang X, Duan H T, Cao Z G, Shen M and Ge X S. 2018. Remote monitoring of cyanobacterial blooms using multi-source satellite data: a case of Yuqiao Reservoir, Tianjin. Journal of Lake Sciences, 30(4): 967-978

房旭, 段洪涛, 曹志刚, 沈明, 葛小三. 2018. 基于多源卫星数据的小型水体蓝藻水华联合监测——以天津于桥水库为例. 湖泊科学, 30(4): 967-978 [DOI: 10.18307/2018.0410http://dx.doi.org/10.18307/2018.0410]

Feng L. 2021. Key issues in detecting lacustrine cyanobacterial bloom using satellite remote sensing. Journal of Lake Sciences, 33(3): 647-652

冯炼.卫星遥感解译湖泊蓝藻水华的几个关键问题探讨. 2021. 湖泊科学, 33(3):647-652 [DOI:10.18307/2021.0301http://dx.doi.org/10.18307/2021.0301]

GDAL/OGR Contributors. 2022. GDAL/OGR Geospatial Data Abstraction software Library. Open Source Geospatial Foundation. URL https://github.com/OSGeo/gdal/blob/master/CITATION[DOI: 10.5281/zenodo.5884351http://dx.doi.org/10.5281/zenodo.5884351]

Gower J, King S, Borstad G and Brown L. 2005. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 26(9): 2005-2012 [DOI: 10.1080/01431160500075857http://dx.doi.org/10.1080/01431160500075857]

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]

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 P, 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]

Hu L, Gan S, Yuan X P, Li R B and Bi R. 2021. Study on the spatial distribution characteristics of cyanobacteria bloom in Dianchi Lake based on GF-5. Laser and Infrared, 51(2): 237-243

胡琳, 甘淑, 袁希平, 李绕波, 毕瑞. 2021. 基于GF-5的滇池蓝藻水华空间分布特征研究. 激光与红外, 51(2): 237-243 [DOI: 10.3969/j.issn.1001-5078.2021.02.019http://dx.doi.org/10.3969/j.issn.1001-5078.2021.02.019]

Hu M Q, Zhang Y C, Ma R H and Zhang Y X. 2018. Spatial and temporal dynamics of floating algal blooms in Lake Chaohu in 2016 and their environmental drivers. Environmental Science, 39(11): 4925-4937

胡旻琪, 张玉超, 马荣华, 张壹萱. 2018. 巢湖2016年蓝藻水华时空分布及环境驱动力分析. 环境科学, 39(11): 4925-4937 [DOI: 10.13227/j.hjkx.201801057http://dx.doi.org/10.13227/j.hjkx.201801057]

Li Y M, Zhao H, Bi S and Lyu H. 2022. Research progress of remote sensing monitoring of case II water environmental parameters based on water optical classification. National Remote Sensing Bulletin, 26(1): 19-31

李云梅, 赵焕, 毕顺, 吕恒. 2022. 基于水体光学分类的二类水体水环境参数遥感监测进展. 遥感学报, 26(1): 19-31 [DOI: 10.11834/jrs.20221212http://dx.doi.org/10.11834/jrs.20221212]

Liu H Q, Ren H K, Niu X X and Xia P. 2021. Extraction of cyanobacteria bloom in Chaohu Lake based on Sentinel-2 remote sensing images. Ecology and Environmental Sciences, 30(1): 146-155

刘海秋, 任恒奎, 牛鑫鑫, 夏萍. 2021. 基于Sentinel-2遥感影像的巢湖蓝藻水华提取方法研究. 生态环境学报, 30(1): 146-155 [DOI: 10.16258/j.cnki.1674-5906.2021.01.017http://dx.doi.org/10.16258/j.cnki.1674-5906.2021.01.017]

Liu S J, Shi Y F, Zhai J Y, Zhou S X and Ji Z Y. 2021. Annual variations of Microcystis density and their relationships with water quality indices in Xingyun Lake. Environmental Chemistry, 40(7): 2064-2072

刘绍俊, 施艳峰, 翟竟余, 周书祥, 吉正元. 2021. 星云湖微囊藻密度周年变化及其与水质指标的关系. 环境化学, 40(7): 2064-2072 [DOI: 10.7524/j.issn.0254-6108.2020030401http://dx.doi.org/10.7524/j.issn.0254-6108.2020030401]

Luo X C, Hang X, Cao Y, Hang R R and Li Y C. 2019. Dominant meteorological factors affecting cyanobacterial blooms under eutrophication in Lake Taihu. Journal of Lake Sciences, 31(5): 1248-1258

罗晓春, 杭鑫, 曹云, 杭蓉蓉, 李亚春. 2019. 太湖富营养化条件下影响蓝藻水华的主导气象因子. 湖泊科学, 31(5): 1248-1258 [DOI: 10.18307/2019.0512http://dx.doi.org/10.18307/2019.0512]

Matthews M W, Bernard S and Robertson L. 2012. An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sensing of Environment, 124: 637-652 [DOI: 10.1016/j.rse.2012.05.032http://dx.doi.org/10.1016/j.rse.2012.05.032]

Matthews M W and Odermatt D. 2015. Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sensing of Environment, 156: 374-382 [DOI: 10.1016/j.rse.2014.10.010http://dx.doi.org/10.1016/j.rse.2014.10.010]

McFeeters S K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]

Oyama Y, Matsushita B and Fukushima T. 2015. Distinguishing surface cyanobacterial blooms and aquatic macrophytes using Landsat/TM and ETM+ shortwave infrared bands. Remote Sensing of Environment, 157: 35-47 [DOI: 10.1016/j.rse.2014.04.031http://dx.doi.org/10.1016/j.rse.2014.04.031]

Qi G H, Ma X S, He S Y and Wu P H. 2021. Long-term spatiotemporal variation analysis and probability prediction of algal blooms in Lake Chaohu (2009—2018) based on multi-source remote sensing data. Journal of Lake Sciences, 33(2): 414-427

祁国华, 马晓双, 何诗瑜, 吴鹏海. 2021. 基于多源遥感数据的巢湖水华长时序时空变化(2009—2018年)分析与发生概率预测. 湖泊科学, 33(2): 414-427 [DOI: 10.18307/2021.0204http://dx.doi.org/10.18307/2021.0204]

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 L, Hu C M, Visser P M and Ma R H. 2018. Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations. Limnology and Oceanography, 63(4): 1711-1726 [DOI: 10.1002/lno.10802http://dx.doi.org/10.1002/lno.10802]

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]

Qiu Z F, Li Z X, Bilal M, Wang S Q, Sun D Y and Chen Y L. 2018. Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images. Optics Express, 26(21): 26810-26829 [DOI: 10.1364/OE.26.026810http://dx.doi.org/10.1364/OE.26.026810]

Rouse J W, Haas R H, Schell J A and Deering D W. 1974. Monitoring vegetation systems in the Great Plains with ERTS//3rd Earth Resources Technology Satellite-1 Symposium. Washington, D.C.: NASA: 309-317

Shen F, Tang R G, Sun X R and Liu D Y. 2019. Simple methods for satellite identification of algal blooms and species using 10-year time series data from the East China Sea. Remote Sensing of Environment, 235: 111484 [DOI: 10.1016/j.rse.2019.111484http://dx.doi.org/10.1016/j.rse.2019.111484]

Son Y B, Min J E and Ryu J H. 2012. Detecting massive green algae (Ulva prolifera) blooms in the Yellow Sea and East China Sea using Geostationary Ocean Color Imager (GOCI) data. Ocean Science Journal, 47(3): 359-375 [DOI: 10.1007/s12601-012-0034-2http://dx.doi.org/10.1007/s12601-012-0034-2]

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]

Vermote E F, Tanré D, Deuze J L, Herman M and Morcette J J. 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 675-686 [DOI: 10.1109/36.581987http://dx.doi.org/10.1109/36.581987]

Wang J H, He L Q S, Yang C, Dao G H, Du J S, Han Y P, Wu G X, Wu Q Y and Hu H Y. 2018. Comparison of algal bloom related meteorological and water quality factors and algal bloom conditions among lakes Taihu, Chaohu, and Dianchi (1981—2015). Journal of Lake Sciences, 30(4): 897-906

王菁晗, 何吕奇姝, 杨成, 刀国华, 杜劲松, 韩亚平, 吴光学, 吴乾元, 胡洪营. 2018. 太湖、巢湖、滇池水华与相关气象、水质因子及其响应的比较(1981—2015年). 湖泊科学, 30(4): 897-906 [DOI: 10.18307/2018.0403http://dx.doi.org/10.18307/2018.0403]

Wilson R T. 2013. Py6S: a Python interface to the 6S radiative transfer model. Computers and Geosciences, 51: 166-171 [DOI: 10.1016/j.cageo.2012.08.002http://dx.doi.org/10.1016/j.cageo.2012.08.002]

Wynne T T, Stumpf R P, Tomlinson M C and Dyble J. 2010. Characterizing a cyanobacterial bloom in Western Lake Erie using satellite imagery and meteorological data. Limnology and Oceanography, 55(5): 2025-2036 [DOI: 10.4319/lo.2010.55.5.2025http://dx.doi.org/10.4319/lo.2010.55.5.2025]

Xing Q G and Hu C M. 2016. Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: application of a virtual baseline reflectance height technique. Remote Sensing of Environment, 178: 113-126 [DOI: 10.1016/j.rse.2016.02.065http://dx.doi.org/10.1016/j.rse.2016.02.065]

Xue K, Ma R H, Duan H T, Shen M, Boss E and Cao Z G. 2019. Inversion of inherent optical properties in optically complex waters using Sentinel-3A/OLCI images: a case study using China’s three largest freshwater lakes. Remote Sensing of Environment, 225: 328-346 [DOI: 10.1016/j.rse.2019.03.006http://dx.doi.org/10.1016/j.rse.2019.03.006]

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]

Yue A, Zeng Q W and Wang H J. 2020. Remote sensing long-term monitoring of cyanobacterial blooms in yuqiao reservoir. Remote Sensing Technology and Application, 35(3): 694-701

岳昂, 曾庆伟, 王怀警. 2020. 于桥水库蓝藻水华遥感长时序监测研究. 遥感技术与应用, 35(3): 694-701 [DOI: 10.11873/j.issn.1004-0323.2020.3.0694http://dx.doi.org/10.11873/j.issn.1004-0323.2020.3.0694]

Zhang M, Yang Z and Shi X L. 2019. Expansion and drivers of cyanobacterial blooms in Lake Taihu. Journal of Lake Sciences, 31(2): 336-344

张民, 阳振, 史小丽. 2019. 太湖蓝藻水华的扩张与驱动因素. 湖泊科学, 31(2): 336-344 [DOI: 10.18307/2019.0203http://dx.doi.org/10.18307/2019.0203]

Zhu G W, Qin B Q, Zhang Y L, Xu H, Zhu M Y, Yang H W, Li K Y, Min S, Shen R J and Zhong C N. 2018. Variation and driving factors of nutrients and chlorophyll-a concentrations in northern region of Lake Taihu, China, 2005—2017. Journal of Lake Sciences, 30(2): 279-295

朱广伟, 秦伯强, 张运林, 许海, 朱梦圆, 杨宏伟, 李宽意, 闵屾, 沈睿杰, 钟春妮. 2018. 2005—2017年北部太湖水体叶绿素a和营养盐变化及影响因素. 湖泊科学, 30(2): 279-295 [DOI: 10.18307/2018.0201http://dx.doi.org/10.18307/2018.0201]

Zong J M, Wang X X, Zhong Q Y, Xiao X M, Ma J and Zhao B. 2019. Increasing outbreak of cyanobacterial blooms in large lakes and reservoirs under pressures from climate change and anthropogenic interferences in the middle-lower Yangtze River Basin. Remote Sensing, 11(15): 1754 [DOI: 10.3390/rs11151754http://dx.doi.org/10.3390/rs11151754]

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