“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
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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。
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.
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.
引文
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[13] MATTHEWS M W,ODERMATT D. Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters[J]. Remote Sensing of Environment,2015,156(156):374-382.
[2]温新龙，景元书，张娣．常见卫星传感器在蓝藻水华监测中的应用进展[J]．环境科学与技术，2014(1):81-87.
[3] BINDING C E,GREENBERG T A,BUKATA R P,et al. The MERIS MCI and its potential for satellite detection of winter diatom blooms on partially ice-covered Lake Erie[J]. Journal of Plankton Research,2012,34(6):569-573.
[4]李云梅，黄家柱，韦玉春，等．湖泊富营养化状态的地面高光谱遥感评价[J]．环境科学，2006(9):1770-1775.
[5] LIAO L B,WEISS S,MILLS S,et al. Suomi NPP VIIRS daynight band on-orbit performance[J]. Journal of Geophysical Research Atmospheres,2013,118(22):12705–12718.
[6] KUTSER T. Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters[J]. International Journal of Remote Sensing,2009,30(17):4401-4425.
[7]马荣华，唐军武，段洪涛，等．湖泊水色遥感研究进展[J]．湖泊科学，2009,21(2):143-158.
[8]程春梅．顾及时空差异性的太湖水体中叶绿素a浓度的遥感估算实验研究[D]．南京:南京师范大学，2014.
[9] SALEM S I,STRAND M H,HIGA H,et al. Evaluation of MERIS chlorophyll-a retrieval processors in a complex turbid lake Kasumigaura over 10-year mission[J]. Remote Sensing,2017(9):1022.
[10] MATTHEWS M W,BERNARD S,ROBERTSON L. An algorithm for detecting trophic status(chlorophyll-a),cyanobacterialdominance,surface scums and floating vegetation in inland and coastal waters[J]. Remote Sensing of Environment,2012,124(124):637-652.
[11] GOWER J,DOERFFER R,BORSTAD G. Interpretation of the685 nm peak in water-leaving radiance spectra in terms of fluorescence,absorption and scattering,and its observation by MERIS[J]. International Journal of Remote Sensing,1999,20(9):1771-1786.
[12] GOWER J,KING S,BORSTAD G,et al. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer[J]. International Journal of Remote Sensing,2005,26(9):2005-2012.
[13] MATTHEWS M W,ODERMATT D. Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters[J]. Remote Sensing of Environment,2015,156(156):374-382.
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