摘要
Aerosol retrieval over land from satellite remotely sensed data remains internationally a difficult task. By using MODIS data, the Dark Dense Vegetation (DDV) algorithm aerosol distribution and properties retrieval over land has shown excellent competence. However, this algorithm is re-stricted to lower surface reflectance such as water bodies and dense vegetation, which limits its actual application, and is unable to be used for high reflective surface such as over urban areas. In this paper, we introduce a new aerosol retrieval model by exploiting the Synergy of TERRA and AQUA MODIS data (SYNTAM), which can be used for various ground surfaces, including for high reflective surface. Preliminary validations have been carried out by comparing with AERONET measured data, which shows good accuracy and promising potential. Further research work is undergoing.
Aerosol retrieval over land from satellite remotely sensed data remainsinternationally a difficult task. By using MODIS data, the Dark Dense Vegetation (DDV) algorithmaerosol distribution and properties retrieval over land has shown excellent competence. However,this algorithm is restricted to lower surface reflectance such as water bodies and dense vegetation,which limits its actual application, and is unable to be used for high reflective surface such asover urban areas. In this paper, we introduce a new aerosol retrieval model by exploiting theSynergy of TERRA and AQUA MODIS data (SYNTAM), which can be used for various ground surfaces,including for high reflective surface. Preliminary validations have been carried out by comparingwith AERONET measured data, which shows good accuracy and promising potential. Further research workis undergoing.
作者
TANG Jiakui1, XUE Yong1,2, YU Tong3, GUAN Yanning1, CAI Guoyin1 & HU Yincui1 1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China
2. Department of Computing, London Metropolitan University, 166-220 Holloway Road, London N7 8DB, UK
3. Beijing Environmental Monitor Center, Beijing 100044, China
基金
supported by the National Natural Science Foundation of China(Grant No.40471091)
the Ministry of Science and Technology of China(Grant No.2002BA904B07-2).
