An Improved DDV Algorithm for the Retrieval of Aerosol Optical Depth From NOAA/AVHRR Data
文献类型：期刊论文
| 作者 | Li, Ruibo3; Sun, Lin3; Yu, Huiyong3; Wei, Jing1 ; Tian, Xinpeng2
|
| 刊名 | JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
 |
| 出版日期 | 2021-01-19 |
| 页码 | 12 |
| 关键词 | AVHRR AOD MODIS VI product DDV algorithm |
| ISSN号 | 0255-660X |
| DOI | 10.1007/s12524-020-01301-6 |
| 通讯作者 | Sun, Lin(sunlin@sdust.edu.cn) |
| 英文摘要 | Aerosol Optical Depth (AOD) is one of the important parameters to characterize the physical properties of the atmospheric aerosol, which is used to describe the extinction characteristics of the aerosol, and also to calculate the aerosol content, to assess the degree of air pollution and to study aerosol climate effect. To study the historical change of aerosol in long-time series, the advanced very high resolution radiometer (AVHRR) data earliest used for aerosol research was used in this study. Due to the lack of shortwave infrared (SWIR) (center at 2.13 mu m) of the sensor, the relationship between the blue and red bands with SWIR cannot be provided, and the visible band used to calculate the normalized difference vegetation index (NDVI) contains the wavelength range of red and green, it is very difficult to calculate the accurate land surface reflectance (LSR). Therefore, based on the Dense Dark Vegetation algorithm (DDV), we propose to introduce mature MODIS vegetation index products (MYD13) to correct AVHRR NDVI, to support the estimation of AVHRR LSR, determine the relationship between corrected AVHRR NDVI and visible band LSR, and to carry out aerosol retrieval. The results show that about 63% of the data are within the error line, and there is a consistent distribution trend in the inter-comparison validation with MODIS aerosol products (MYD04). |
| 资助项目 | National Natural Science Foundation of China[41771408] ; Shandong Provincial Natural Science Foundation, China[ZR2017MD001] ; Shandong Provincial Natural Science Foundation, China[ZR2020QD055] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000608952300002 |
| 资助机构 | National Natural Science Foundation of China ; Shandong Provincial Natural Science Foundation, China |
| 源URL | [http://ir.yic.ac.cn/handle/133337/27498]  |
| 专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 烟台海岸带研究所_近岸生态与环境实验室 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 通讯作者 | Sun, Lin |
| 作者单位 | 1.Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA 2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Shandong, Peoples R China 3.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Ruibo,Sun, Lin,Yu, Huiyong,et al. An Improved DDV Algorithm for the Retrieval of Aerosol Optical Depth From NOAA/AVHRR Data[J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING,2021:12. |
| APA | Li, Ruibo,Sun, Lin,Yu, Huiyong,Wei, Jing,&Tian, Xinpeng.(2021).An Improved DDV Algorithm for the Retrieval of Aerosol Optical Depth From NOAA/AVHRR Data.JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING,12. |
| MLA | Li, Ruibo,et al."An Improved DDV Algorithm for the Retrieval of Aerosol Optical Depth From NOAA/AVHRR Data".JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING (2021):12. |
入库方式： OAI收割
来源：烟台海岸带研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。