“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

李旭文; 姜晟; 张悦; 王甜甜; 蔡琨; 丁铭; 纪轩禹

引用本文:	李旭文，姜晟，张悦，王甜甜，蔡琨，丁铭，纪轩禹.“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[J].环境监控与预警,2019,11(5):59-65
	LI Xu-wen, JIANG Sheng, ZHANG Yue, WANG Tian-tian, CAI Kun, DING Ming, JI Xuan-yu.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[J].Environmental Monitoring and Forewarning,2019,11(5):59-65

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“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
李旭文，姜晟，张悦，王甜甜，蔡琨，丁铭，纪轩禹
江苏省环境监测中心，江苏南京 210019

摘要:

利用“哨兵-3”卫星OLCI影像数据，基于其619，665，681，709，753和885 nm中心波长对应的6个波段构建的最大特征峰高度（MPH）算法，采用SNAP 7.0遥感专业软件，计算了典型日期太湖MPH算法得到的叶绿素a浓度、浮藻区、藻水混悬区、水草区的分布。结果表明：（1）MPH算法能够精确地识别太湖水草和蓝藻；（2）MPH算法能够提取稠密铺集水表层的“浮藻区”,并区分出藻密度较小、水华现象轻微~轻度、蓝藻主要浸没在水面以下的“藻水混悬区”。与MODIS、VIIRS等常用的蓝藻水华遥感传感器相比，OLCI展现了更出色、更精细化的水生态遥感监测能力，可提高蓝藻水华预警预报水平。

关键词: 哨兵-3 海洋和陆地颜色仪最大特征峰高度 SNAP软件蓝藻水华太湖

DOI：

分类号:X837

文献标识码:B

基金项目:国家水体污染控制与治理科技重大专项基金资助项目（2017ZX07302-003）；2018年太湖水污染治理省级专项资金科研课题（TH2018304）；江苏省环境监测科研基金资助项目(1619，1901，1913)

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

LI Xu-wen, JIANG Sheng, ZHANG Yue, WANG Tian-tian, CAI Kun, DING Ming, JI Xuan-yu

Jiangsu Provincial Environmental Monitoring Center, Nanjing,Jiangsu 210019,China

Abstract:

Using the new “sentinel-3” satellite OLCI image, based on six bands 619，665，681，709，753，885 nm wavelengths,respectively the“largest characteristic peak height” MPH algorithm was constructed,and implemented in the SNAP 7.0 remote sensing information processing software,to calculate the date of 2019 typical MPH algorithm results in Taihu chlorophyll a concentration, floating algae zone, suspended algae water area, water plants area. The results show that :(1) The MPH algorithm can accurately identify floating vegetation and cyanobacteria in Taihu Lake. (2) The MPH algorithm is capable of extracting the “Floating Cyanobacteria” that densely accumulates at the water surface, and distinguishes the “algae/water suspension area” with small algae density, slight water bloom phenomenon, and mild cyanobacteria immersed below the water surface. Compared with the commonly used cyanobacterial water bloom remote sensing sensors such as MODIS and VIIRS, OLCI has demonstrated an excellent and more refined water ecological remote sensing monitoring capability, which can improve the cyanobacterial bloom warning and forecasting level.

Key words: Sentinel-3 OLCI MPH SNAP software Cyanobacteria blooms Lake Taihu