Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Journal of Remote Sensing》 2012-03
Add to Favorite Get Latest Update

HJ-1 terrestrial aerosol data retrieval using deep blue algorithm

WANG Zhongting 1 , LI Qing 1 , WANG Qiao 1 , LI Shenshen 2 , CHEN Liangfu 2 , ZHOU Chunyan 1 , ZHANG Lijuan 1 , XU Yongjun 3 1. Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China; 2. Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing 100101, China; 3. China University of Geosciences, Beijing 100083, China  
Aerosol is an important index in atmosphere monitoring. Disadvantages exist when monitoring aerosol from HJ-1 data by dark dense vegetation (DDV) or contrast reduction algorithm. In this paper, based on the algorithm which was developed by Hsu, et al.(2004), the deep blue algorithm is applied to CCD/HJ-1. First, the database of land surface reflectance is built from MODerate-resolution Imaging Spectroradiometer (MODIS) spectral reflectance product. Second, after analyzing relationship between CCD camera reflectance and MODIS, the reflectance of MODIS are corrected to CCD camera of HJ-1. Third, aerosol optical depth (AOD) is retrieved from apparent reflectance in the first band of CCD/HJ-1. Finally, AODs over Beijing area are retrieved from December 2008 to October 2009, and the results are validated by ground-based measurement of Beijing station in the PHOtométrie pour le Traitement Opérational de Normalisation Satellitaire (PHOTONS) network included in the worldwide Aerosol Robotic Network (AERONET). The validation and discussions show that, when AODs are greater than 0.5, the accuracy of deep blue algorithm can satisfy the aerosol monitoring using HJ-1 data, and aerosol model can greatly influence the results.
【Fund】: 国家重点基础研究发展计划(973计划)(编号:2010CB950801)~~
【CateGory Index】: X513;X87
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved