A new generation aerosol optical depth dataset based on AVHRR data over China from 1981 to 2000

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Yahui Che , Jie Guang , Yong Xue , Gerrit de Leeuw , Lu She , Linlu Mei , Xingwei He , Ling Sun , Zhengqiang Li
{"title":"A new generation aerosol optical depth dataset based on AVHRR data over China from 1981 to 2000","authors":"Yahui Che ,&nbsp;Jie Guang ,&nbsp;Yong Xue ,&nbsp;Gerrit de Leeuw ,&nbsp;Lu She ,&nbsp;Linlu Mei ,&nbsp;Xingwei He ,&nbsp;Ling Sun ,&nbsp;Zhengqiang Li","doi":"10.1016/j.rse.2025.114899","DOIUrl":null,"url":null,"abstract":"<div><div>The Advanced Very High Resolution Radiometer (AVHRR) series onboard the National Oceanic and Atmospheric Administration (NOAA) and the EUMETSAT Meteorological Operational Satellite (Metop) polar-orbiting satellites have provided continuous Earth observation data since 1979, which facilitates the development of long-term global climate data records. In this paper, a new version of the algorithm for the retrieval of the Aerosol Optical Depth (AOD) over Land (ADL v2.0) using AVHRR data is proposed with improved accuracy, in particular for high AOD values. The surface reflectance estimation scheme is based on a regression model established using simulated AVHRR reflectances spectrally transferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09/MYD09 product. To address limitations in retrieving high AOD, the surface reflectance is determined using the maximum Normalized Difference Vegetation Index (NDVI) during a certain period of time. To this end, a dynamic NDVI search window is proposed to identify the NDVI that is least affected by aerosols. ADL v2.0 has been applied to provide an AOD dataset covering Mainland China (70<sup>o</sup>-140°E, 15<sup>o</sup>-60<sup>o</sup>N) for the years from 1981 to 2000. This dataset has been evaluated by comparing with AOD data available from the application of the broadband extinction method (BEM) to ground-based solar radiation measurements and from the AVHRR Deep Blue (DB) AOD dataset. The AOD variations retrieved using the BEM data at seven stations (two in North China, two in Northeast China, one in East China, one in Central China, and one in the southwest mountainous region) are well reproduced by the ADL v2.0 algorithm. The comparison with the AVHRR DB AOD dataset shows good agreement with ADL v2.0 retrieval results even though with less valid retrievals for high AOD in Eastern China, Sichuan, and the Guanzhong Basin, as well as over North India.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114899"},"PeriodicalIF":11.1000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425725003037","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) series onboard the National Oceanic and Atmospheric Administration (NOAA) and the EUMETSAT Meteorological Operational Satellite (Metop) polar-orbiting satellites have provided continuous Earth observation data since 1979, which facilitates the development of long-term global climate data records. In this paper, a new version of the algorithm for the retrieval of the Aerosol Optical Depth (AOD) over Land (ADL v2.0) using AVHRR data is proposed with improved accuracy, in particular for high AOD values. The surface reflectance estimation scheme is based on a regression model established using simulated AVHRR reflectances spectrally transferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09/MYD09 product. To address limitations in retrieving high AOD, the surface reflectance is determined using the maximum Normalized Difference Vegetation Index (NDVI) during a certain period of time. To this end, a dynamic NDVI search window is proposed to identify the NDVI that is least affected by aerosols. ADL v2.0 has been applied to provide an AOD dataset covering Mainland China (70o-140°E, 15o-60oN) for the years from 1981 to 2000. This dataset has been evaluated by comparing with AOD data available from the application of the broadband extinction method (BEM) to ground-based solar radiation measurements and from the AVHRR Deep Blue (DB) AOD dataset. The AOD variations retrieved using the BEM data at seven stations (two in North China, two in Northeast China, one in East China, one in Central China, and one in the southwest mountainous region) are well reproduced by the ADL v2.0 algorithm. The comparison with the AVHRR DB AOD dataset shows good agreement with ADL v2.0 retrieval results even though with less valid retrievals for high AOD in Eastern China, Sichuan, and the Guanzhong Basin, as well as over North India.
基于AVHRR资料的新一代中国气溶胶光学深度数据集
美国国家海洋和大气管理局(NOAA)和EUMETSAT气象业务卫星(Metop)极地轨道卫星搭载的先进甚高分辨率辐射计(AVHRR)系列自1979年以来提供了连续的地球观测数据,促进了长期全球气候数据记录的发展。本文提出了一种利用AVHRR数据反演陆地气溶胶光学深度(AOD)的新算法(ADL v2.0),提高了算法的精度,特别是在AOD值较高的情况下。地表反射率估算方案基于中分辨率成像光谱仪(MODIS) MOD09/MYD09产品光谱传输的模拟AVHRR反射率建立的回归模型。为了解决反演高AOD的局限性,采用一定时期内最大归一化植被指数(NDVI)来确定地表反射率。为此,提出了一个动态NDVI搜索窗口来识别受气溶胶影响最小的NDVI。应用ADL v2.0提供了1981 - 2000年覆盖中国大陆(70 ~ 140°E, 150 ~ 60on)的AOD数据集。通过与宽带消光法(BEM)地面太阳辐射测量和AVHRR深蓝(DB) AOD数据集的AOD数据进行比较,对该数据集进行了评估。ADL v2.0算法能较好地再现华北2个、东北2个、华东1个、华中1个、西南山区1个站点的AOD变化。AVHRR DB AOD数据与ADL v2.0反演结果吻合较好,但对中国东部、四川、关中盆地和印度北部的高AOD反演效果较差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信