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REMOTE SENSING FOR LAND & RESOURCES    2011, Vol. 23 Issue (2) : 1-8     DOI: 10.6046/gtzyyg.2011.02.01
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Advances in the Study of the Application of the MODIS Data to China’s Terrestrial Science
 QIAO Zhi, SUN Xi-Hua
(College of Population, Resources and Environment, Shandong Normal University, Jinan 250014, China)
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Abstract  

In order that people grasp the elementary knowledge of MODIS data application status in China’s terrestrial science, this paper describes the achievements acquired by Chinese scholars in the MODIS data processing, the remote sensing information extraction and the application of productive practices, with an aim of expressing the continuous improvement and the difficulties in the study of terrestrial science based on the MODIS data. The result could provide the theoretical support for further enrichment and improvement of the MODIS data application. At the end of the paper, three measures are put forward for further practicability of MODIS data, i.e., the improvement of MODIS data source quality, the perfection of the information extraction methods, and the construction of the MODIS data analysis models.

Keywords Artificial neural networks      Multilayer feedforward neural networks      Imaging spectral      Pattern recognition     
: 

TP 79

 
Issue Date: 17 June 2011
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HE Yong-qiang
YAO Guo-qing
Cite this article:   
HE Yong-qiang,YAO Guo-qing. Advances in the Study of the Application of the MODIS Data to China’s Terrestrial Science[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 1-8.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.02.01     OR     https://www.gtzyyg.com/EN/Y2011/V23/I2/1
[1] HE Yong-qiang, YAO Guo-qing . THE STRUCTURES OF ARTIFICIAL NEURAL NETWORKS USED FOR IMAGING SPECTRAL DATA PATTERN RECOGNITION[J]. REMOTE SENSING FOR LAND & RESOURCES, 2004, 16(3): 23-27.
[2] Gan Fuping, Wang Runsheng, Wang Yongjiang, Fu Zhengwen. THE CLASSIFICATION METHOD BASED ON REMOTE SENSING TECHNIQUES FOR LAND USE AND COVER[J]. REMOTE SENSING FOR LAND & RESOURCES, 1999, 11(4): 40-45.
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