| 基于VIIRS数据的中国东部气溶胶光学厚度反演算法改进 |
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| 引用本文： | 汪洋,陈良富,李莘莘,王新辉,余超,范萌.基于VIIRS数据的中国东部气溶胶光学厚度反演算法改进[J].地球物理学报,2019,62(1):49-62. |
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| 作者姓名： | 汪洋 陈良富 李莘莘 王新辉 余超 范萌 |
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| 作者单位： | 1. 中国科学院遥感与数字地球研究所遥感科学国家重点实验室, 北京 100101;2. 中国科学院大学, 北京 100049;3. 北京市环境保护监测中心, 北京 100048 |
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| 基金项目： | 国家自然科学基金重大研究计划项目（91543128）和国家科技支撑计划（2014BAC21B00）资助. |
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| 摘 要： | VIIRS(Visible Infrared Imaging Radiometer Suite)作为MODIS(The MODerate resolution Imaging Spectroradiometer)的后继传感器,可在全球范围内实现对气溶胶的连续时空监测.卫星反演的气溶胶光学厚度(Aerosol Optical Depth,AOD)是研究地球能量收支平衡、气候效应和空气质量的重要大气参数.但在中国重污染天气情况下,现有的VIIRS陆地气溶胶产品存在一定不足.因此,本研究改进云识别方法,优化像元筛选,约束气溶胶类型选择,实现重污染情况下AOD的反演.基于地基AERONET(AErosol RObotic NETwork)的验证结果表明,相比NOAA(National Oceanic and Atmospheric Administration)产品,改进后的反演结果克服了反演值偏低的问题,且表现出更好的相关性,RMSE从0.236下降到0.219.为验证在重污染条件下改进算法的适用性和准确性,本文对比了两种污染条件下的反演结果(0.61).统计结果表明,在较重污染天气条件下(AODAERONET>1),相比NOAA的AOD产品,本文结果的反演率从32.3%提升为68.8%,回归分析的斜率提高为0.80,相关系数达到0.76,均方根误差为0.307,在增加反演量的同时保证了反演的精度.
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| 关 键 词： | AOD 遥感 反演算法 VIIRS 霾 |
| 收稿时间： | 2017-04-14 |
VIIRS aerosol opticaldepth (AOD) retrieval algorithm improvement in eastern China |
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| WANG Yang,CHEN LiangFu,LI ShenShen,WANG XinHui,YU Chao,FAN Meng.VIIRS aerosol opticaldepth (AOD) retrieval algorithm improvement in eastern China[J].Chinese Journal of Geophysics,2019,62(1):49-62. |
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| Authors: | WANG Yang CHEN LiangFu LI ShenShen WANG XinHui YU Chao FAN Meng |
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| Institution: | 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;2. University of the Chinese Academy of Sciences, Beijing 100049, China;3. Beijing Municipal Environmental Monitoring Centre, Beijing 100048, China |
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| Abstract: | As the MODIS (MODerate resolution Imaging Spectroradiometer) successor, VⅡRS (Visible Infrared Imaging Radiometer Suite) can achieve on a global scale continuous monitoring of aerosols. As a mature product of aerosol remote sensing, the Aerosol Optical Depth (AOD) is an important atmospheric parameter in energy budget and climate change researches. But because of heavy aerosol loading, the application of VⅡRS AOD product from NOAA (National Oceanic and Atmospheric Administration) is limited in China. By using a new cloud mask method, improving the pixels selection process, widening the range of AOD inversion, and constraining the aerosol model selection, we improved the retrieval algorithm and enhanced the performance in haze area. Based on validation with ground-based AERONET (AErosol RObotic NETwork) observation, our AOD product overcame the underestimation of satellite retrieval result and had better correlation and smaller root mean square error (RMSE) than that of NOAA. In order to verify its performance in polluted air condition, we compared them in two degrees of pollution (0.6 < AODAERONET < 1 and AODAERONET > 1). For the heavy pollution conditions (AODAERONET>1), availability proportion of data increased effectively from 32.3% to 68.8%. Validation results showed that the slope of the regression analysis, correlation coefficient, and the root mean square error (RMSE) was enhanced to 0.80, 0.76 and 0.307 respectively. In the conclusion, the accuracy of AOD products was guaranteed on the basis of increasing of data coverage. |
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| Keywords: | AOD Remote sensing Retrieval algorithm VIIRS Haze |
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