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Proceedings Paper

Quantitative method study of sand-dust information using Terra/MODIS data
Author(s): Haiping Li; Liya Xiong; Dafang Zhuang
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Paper Abstract

With the successful launching of the new generation satellite of EOS Terra, its Moderate Resolution Imaging Spectroradiometer (MODIS) data has more advantages in comprehensive research of land, ocean and atmosphere. ALso it can be used in disaster monitoring and losing assessment. This article focuses on the discussion of using Terra/MODIS data to study sand-dust information quantitative retrieval method. The conclusion is that MODIS data can be used to study the quantitative retrieval of sand-dust information and the study scenario has certain feasibility. The study contains of selecting the characteristic spectrum bands of sand-dust information; separating sand-dust information from the background and enhancing the weak information of floating dust, raising dust, etc. Theory basis of quantitative retrieval method are remote sensing quantitative method, atmosphere radiative transforming and visibility theories. Data pre-processing is also necessary. Retrieving of sand-dust aerosol optic thickness is more difficult and also the key problem to resolve. Case study is necessary and should be used to support the quantitative method. The study can provide the theory basis and technique support for sand-dust disaster forecasting monitoring and preventing.

Paper Details

Date Published: 14 July 2003
PDF: 10 pages
Proc. SPIE 4890, Ecosystems Dynamics, Ecosystem-Society Interactions, and Remote Sensing Applications for Semi-Arid and Arid Land, (14 July 2003); doi: 10.1117/12.465459
Show Author Affiliations
Haiping Li, Institute of Geographic Sciences and Natural Resources Research, CAS (China)
Liya Xiong, Institute of Geographic Sciences and Natural Resources Research, CAS (China)
Dafang Zhuang, Institute of Geographic Sciences and Natural Resources Research, CAS (China)


Published in SPIE Proceedings Vol. 4890:
Ecosystems Dynamics, Ecosystem-Society Interactions, and Remote Sensing Applications for Semi-Arid and Arid Land
Xiaoling Pan; Wei Gao; Michael H. Glantz; Yoshiaki Honda, Editor(s)

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