基于Sentinel-2影像的典型草甸型湖泊大气校正研究  

Atmospheric Correction Research for Sentinel-2 Imagery for Typical Meadow-Type Lakes

作　　者：孟飞 冯建飞 付萍杰 张家威 陈飞勇 MENG Fei;FENG Jianfei;FU Pingjie;ZHANG Jiawei;CHEN Feiyong(School of Surveying,Mapping and Geoinformation,Shandong Jianzhu University,Jinan 250101,China;School of Surveying and Spatial Information,Shandong University of Science and Technology,Qingdao 266000,China;Research Institute of Resources and Environment Innovation,Shandong Jianzhu University,Jinan 250101,China)

机构地区：[1]山东建筑大学测绘地理信息学院,济南250101 [2]山东科技大学测绘与空间信息学院,青岛266000 [3]山东建筑大学资源与环境创新研究院,济南250101

出　　处：《航天返回与遥感》2024年第6期96-112,共17页Spacecraft Recovery & Remote Sensing

基　　金：山东省引进顶尖人才“一事一议”项目(0031504);国家自然科学基金项目(42101388);济南市校融合发展战略工程项目(JNSX2023065);山东省高等学校“青创团队计划”项目(2022KJ201)。

摘　　要：针对一种遥感影像大气校正算法很难同时适用于不同类型湖面的光谱校正问题,文章选取南四湖作为研究区,收集2019—2022年该区域“哨兵二号”(Sentinel-2)卫星遥感影像,采集同期宽敞湖面和水生植被覆盖湖面的光谱数据,基于自适应权重算法,利用Acolite、Sen2Cor和C2RCC三种传统大气校正方法优势,构建了两种多光谱影像大气校正新框架——自适应加权平均大气校正算法(AWA-AC)和改进的自适应加权平均大气校正算法(IAWA-AC)。使用各算法对南四湖Sentinel-2影像进行大气校正试验,并对校正结果进行精度评价对比,结果表明:文章提出的影像大气校正新框架比传统单一算法效果更好,表现在研究时段区域内实测光谱和大气校正影像光谱的决定系数(R2)、均方根误差(RMSE)和平均无偏相对误差(AURE)等3个指标,最大提升值分别为79.75%、71.55%和70.43%。在无实测光谱数据推算R2的前提下,使用文中构建的IAWA-AC算法对遥感影像进行大气校正,能够获得较好的光谱保真度。Aiming at the problem that one remote sensing image atmospheric correction algorithm is difficult to be applied to the spectral correction of different types of lakes at the same time,we select Nansihu as the study area,collect Sentinel-2 images of the region from 2019 to 2022,collect the spectral data of spacious lakes and lakes covered by aquatic vegetation during the same period.Based on the adaptive weighting algorithm,two new frameworks for atmospheric correction of multispectral images,the Adaptive Weighted Average Atmospheric Correction Algorithm(AWA-AC)and the Improved Adaptive Weighted Average Atmospheric Correction Algorithm(IAWA-AC),are constructed by taking advantage of the advantages of the three traditional atmospheric correction methods,namely,Acolite,Sen2Cor and C2RCC.The results of the atmospheric correction experiments on the Sentinel-2 image of Nansi Lake using each algorithm and the evaluation of the comparative accuracies show that the new framework of atmospheric correction is more effective than the single traditional algorithm,and the new framework of atmospheric correction is better than the single traditional algorithm in terms of coefficient of determination(R^(2)),root mean square error(RMSE),and average unbiased relative error(AURE)of the measured and atmospherically corrected image spectra in the area during the time period of the study.The new framework proposed in this study has the maximum enhancement values of 79.75%,71.55%and 70.43%for the three metrics,respectively,compared with the single traditional atmospheric correction algorithm.In the absence of measured spectral data to derive R^(2),atmospheric correction of remotely sensed images using the IAWA-AC algorithm constructed in this study is able to obtain better spectral fidelity.

关 键 词：大气校正 Acolite算法 C2RCC算法 Sen2Cor算法 自适应加权平均 南四湖 “哨兵二号”卫星 

分 类 号：P237[天文地球—摄影测量与遥感]