| 利用Sentinel-3A OLCI可见光通道反演台湾岛气溶胶光学厚度 |
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| 引用本文： | 王峰,汪小钦,丁宇.利用Sentinel-3A OLCI可见光通道反演台湾岛气溶胶光学厚度[J].地球信息科学,2020,22(10):2038-2050. |
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| 作者姓名： | 王峰 汪小钦 丁宇 |
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| 作者单位： | 1.数字中国研究院（福建）,福州 3500032.福州大学卫星空间信息技术国家地方联合工程研究中心,福州 3501183.空间数据挖掘与信息共享教育部实验室,福州 350108 |
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| 基金项目： | 国家重点研发计划项目(2017YFB0504203);中央引导地方发展专项(2017L3012) |
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| 摘 要： | OLCI（Ocean Land Colour Instrument）作为MERIS（Medium Resolution Imaging Spectrometer）的后继升级版传感器,在气溶胶反演中存在潜在优势,但是目前利用OLCI数据进行气溶胶监测的研究较少。因此,本文针对OLCI多通道反射特征开发了OLCI云检测算法,并对传统查找表构建方法进行改进,根据观测几何特征提出动态查找表法,并通过光谱卷积方式等效转换MODIS和OLCI红蓝通道地表反射率并获取OLCI红蓝通道地表反射率固定关系,进而实现台湾岛550 nm处的AOD反演。与550 nm处AERONET level 2.0 AOD验证结果首先表明不同季节、不同站点的精度表现存在一定差异,其次相对于同期MOD04_3K AOD产品,本文反演结果与全球气溶胶自动观测网（AERONET）站点实测值之间表现出更显著 的相关性（R2=0.8199）,均方根误差（RMSE）从0.175下降到0.113,相对平均误差（RME）从33.6%下降到26.7%,且67.5%的OLCI AOD落在预测误差（EE）区间内,明显大于MOD04_3K AOD落在预测误差区间的百分比（55.7%）。此外,误差分析表明,当实际AOD值较低时,红蓝通道地表反射率之间关系的误判会导致较为明显的AOD反演相对误差。
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| 关 键 词： | OLCI 气溶胶光学厚度（AOD） 云识别 动态查找表 台湾岛 |
| 收稿时间： | 2019-07-24 |
Retrieval of Aerosol Optical Depth over Taiwan Island Using Visible Channels of Sentinel-3A OLCI |
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| WANG Feng,WANG Xiaoqin,DING Yu.Retrieval of Aerosol Optical Depth over Taiwan Island Using Visible Channels of Sentinel-3A OLCI[J].Geo-information Science,2020,22(10):2038-2050. |
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| Authors: | WANG Feng WANG Xiaoqin DING Yu |
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| Institution: | 1. The Academy of Digital China (Fujian), Fuzhou 350003, China2. National & Local Joint Engineering Research of satellite-spatial Information Technology, Fuzhou University, Fuzhou 350108, China3. Key Laboratory of Spatial Data Mining & Information Sharing of MOE, Fuzhou 350108, China |
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| Abstract: | OLCI (Ocean Land Colour Instrument) sensor, as a successor of MERIS (Medium Resolution Imaging Spectrometer) sensor, has better temporal resolution, spacial resolution and image width, so it has potential advantages in aerosol retrieval, but there are few studies on aerosol monitoring using OLCI data. In order to expand the application field and scope of Sentinel-3AOLCI image data, a high-precision OLCI cloud detection algorithm based on the multi-channel advantage and observation geometry characteristics of OLCI was first proposed, which can effectively and accurately detect thin and thick clouds. Secondly, dynamic lookup tables based on observed geometric features was constructed. Compared with the traditional lookup table, the efficiency of AOD retrieval using dynamic lookup table is improved significantly. The surface reflectance of MODIS and OLCI red and blue channels were also transformed in an equivalent manner through spectral convolution and obtain the fixed relation of surface reflectance of OLCI red and blue channels. Finally, OLCI Aerosol Optical Depth (AOD) retrieval at 550 nm over The Taiwan Island was carried out, and the spatial distribution of AOD we estimated is highly similar to MODIS (Moderate-resolution Imaging Spectroradiometer) AOD. The validation results of OLCI AOD and AERONET (AErosol Robotic Network) level 2.0 AOD at 550 nm show that the accuracy performance of different seasons and stations is different, and then compared with the same period MOD04_3K AOD at 550 nm products, the correlation between the retrieved OLCI AOD and the measured AOD at AERONET stations is more significant (R2=0.8199), the Root Mean Squared Error (RMSE) decreases from 0.175 to 0.113, the Relative Mean Error (RME) decreases from 33.6% to 26.7%, and 67.5% of OLCI AODS fall within Expected Error (EE), which is significantly greater than that of MOD04_3K AOD falling within the prediction error range (55.7%). Error analysis showed that when the actual AOD value is low, the misjudgment of the relationship between surface reflectance of red and blue channels will lead to relatively obvious relative error of AOD retrieval, which means that there are some potential difficulties in retrieving high-precision AOD in areas with good air quality, but for areas with severe pollution, OLCI AOD has significant precision advantage. |
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| Keywords: | OLCI Aerosol Optical Depth (AOD) cloud detection dynamic look up table Taiwan Island |
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