胶州湾海域Landsat8/OLI数据处理中多种大气校正方法的评价  

Evaluation of Various Atmospheric Correction Methods in the Processing of Landsat8/OLI Data in Jiaozhou Bay

作　　者：刘晓燕 申辰[3] 崔文玺 杨倩 禹定峰 高皜 杨雷 周燕 赵新兴 LIU Xiao-yan;SHEN Chen;CUI Wen-xi;YANG Qian;YU Ding-feng;GAO Hao;YANG Lei;ZHOU Yan;ZHAO Xin-xing(Institute of Oceanographic Instrumentation,Qilu University of Technology(Shandong Academy of Sciences),Qingdao 266100,China;Department of Marine Technology,Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266010,China;Schoolof OceanTechnology Sciences,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250300,China)

机构地区：[1]齐鲁工业大学(山东省科学院)海洋仪器仪表研究所,山东青岛266100 [2]中国海洋大学信息科学与工程学部海洋技术系,山东青岛266010 [3]齐鲁工业大学(山东省科学院)海洋技术科学学院,山东济南250300

出　　处：《光谱学与光谱分析》2022年第8期2513-2521,共9页Spectroscopy and Spectral Analysis

基　　金：国家重点研发项目(2019YFC1408003);国家自然科学基金项目(41206165);山东省自然科学基金项目(ZR2019PD021);山东省科学院海洋仪器仪表研究所培养基金项目(HYPY202107);山东省大学生创新创业项目(S202010431116)资助。

摘　　要：海洋水色遥感研究中,精确的水体遥感反射比R_(rs)(λ)光谱数据是应用海洋光学卫星数据反演海洋生物地球物理参数的关键。实际工作中,遥感反射比是根据遥感仪器接收到的辐亮度经大气吸收和散射校正、太阳距离以及太阳高度角校正后计算出来的。因此对卫星传感器数据进行大气校正是我们得到精确的水体遥感反射比光谱数据的关键因素之一,也是海洋水色遥感研究中的一个重要问题。胶州湾是黄海西部的一个半封闭海湾,是北温带海湾生态系统的重要代表,该海域内规划了大范围的海洋牧场养殖区域,水体生物光学性质复杂。Landsat是美国NASA的陆地卫星计划,最初是为了观测陆地而研发,但是其高空间分辨率(30 m)的优势在海洋遥感监测中表现突出,使得其成为卫星遥感监测河流、湖泊、内陆环湾等水体不可忽略的数据源之一。基于QA(quality assurance)Score光谱质量评价体系对Landsat8/OLI数据处理中五种大气校正算法在胶州湾海域的大气校正结果进行了评价分析。五种大气校正算法分别是NASA(National Aeronautics and Space Administration)标准近红外大气校正算法(Seadas采用为默认大气校正算法,记为Seadas Default);Acolite默认大气校正算法—暗光谱拟合算法(dark spectrum fitting,记为Acolite DSF);以及Acolite指数外推算法(exponential extrapolation),根据算法中所使用波段的不同,分别记为Acolite SWIR,Acolite Red/NIR,Acolite NIR/SWIR。分析结果表明在胶州湾海域Seadas Default的大气校正算法得到的R_(rs)(λ)数据QA得分为1的概率(83.95%)要远大于Acolite DSF(49.66%),Acolite SWIR(4.13%),Acolite Red/NIR(7.25%),Acolite NIR/SWIR(1.38%)四种大气校正算法。Acolite DSF大气校正算法优于Acolite SWIR,Acolite Red/NIR,Acolite NIR/SWIR。应用MODIS/Aqua卫星数据对Seadas Default大气校正算法和Acolite DSF大气校正算法处理Landsat8/OLI卫星数据得到的R_(rs)(λ)�In ocean color remote sensing research,it is the key to obtainingthe accurate remote sensing reflectance spectrum(R_(rs)(λ))data to retrieve marine biogeophysical parameters from ocean optical satellite data.In practice,Ris calculated according to the radiance received by the remote sensing instrument after the correction of atmospheric absorption and scattering and the correction of solar distance and solar elevation angle.Therefore,the atmospheric correction of satellite data is one of the key factors for obtaining accurate water remote sensing reflectance spectral data,which is also an important problem in the research of ocean color remote sensing.Jiaozhou Bay is a semi-closed bay in the west of the Yellow Sea of China and an important representative of the northern temperate zone bay ecosystem.A large range of Marine ranching areas are planned in this sea area,and the water’s bio-optical properties are complex.Landsat is the Landsatellite program of NASA in the United States.It was initially developed to observe the land.However,its advantage of high spatial resolution(30 m)is outstanding in Marine remote sensing monitoring,which makes it become one of the data sources that can not be ignored for satellite remote sensing to monitor rivers,lakes,inland bays and other water bodies.Based on the Quality Assurance system-QA Score,we evaluate the results of five atmospheric correction algorithms in processingLandsat8/OLI data in Jiaozhou Bay.Those five atmospheric correction algorithms are NASA’s(National Aeronautics and Space Administration)standard near-infrared atmospheric correction algorithm(Seadas adopted it as the Default atmospheric correction algorithm,recorded as Seadas Default in this paper).Acolite default atmospheric correction algorithm-Dark Spectrum Fitting(recorded as Acolite DSF in this paper),and the Exponential extrapolation method of Acolite,which is recorded as Acolite SWIR,Acolite Red/NIR,Acolite NIR/SWIR respectively according to the different bands used in the Exponential extrapolation

关 键 词：二类水体 遥感反射比光谱 QA Score Seadas Acolite 胶州湾 

分 类 号：P733.3[天文地球—物理海洋学]