汪洋^1,2,,
陈良富^1,,,
李莘莘¹,
王新辉³,
余超¹,
范萌¹
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

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


详细信息

作者简介: 汪洋, 男, 1990年生, 在读博士, 主要从事气溶胶卫星遥感反演等方面的研究.E-mail:wangyang01@radi.ac.cn

通讯作者: 陈良富, 男, 1965年生, 研究员, 主要从事大气遥感及其相关领域科研工作.E-mail:chenlf@radi.ac.cn

中图分类号: P407

收稿日期:2017-04-14
修回日期:2017-06-06
上线日期:2019-01-05

VIIRS aerosol opticaldepth (AOD) retrieval algorithm improvement in eastern China

WANG Yang^1,2,,
CHEN LiangFu^1,,,
LI ShenShen¹,
WANG XinHui³,
YU Chao¹,
FAN Meng¹
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


More Information

Corresponding author: CHEN LiangFu,E-mail:chenlf@radi.ac.cn

MSC: P407

--> Received Date: 14 April 2017
Revised Date: 06 June 2017
Available Online: 05 January 2019


摘要
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（0.6 < AOD_AERONET < 1和AOD_AERONET > 1）.统计结果表明，在较重污染天气条件下（AOD_AERONET>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.
关键词: AOD/
遥感/
反演算法/
VIIRS/
霾

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 < AOD_AERONET < 1 and AOD_AERONET > 1). For the heavy pollution conditions (AOD_AERONET>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.
Key words:AOD/
Remote sensing/
Retrieval algorithm/
VIIRS/
Haze



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