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Journal of Applied Remote Sensing

Identification of saline-alkali soil based on target decomposition of full-polarization radar data
Author(s): Yangyang Li; Kai Zhao; Xingming Zheng; Jianhua Ren; Yanling Ding; Lili Wu
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Paper Abstract

The potential of C-band polarimetric synthetic aperture radar data for the discrimination of saline-alkali soils in the western Jilin Province, China, is shown. This area is one of the three saline-alkali landscapes in the world; the presence of saline-alkali soils severely restricts the development of local farming and limits the land use. It is extremely important to identify saline-alkali landscapes accurately and effectively. Radar remote sensing is one of the most promising approaches for saline-alkali soil identification due to the sensitivity of radar data to the dielectric and geometric characteristics of objects, its weather-independent imaging capability, and its potential to acquire subsurface information, independent of the frequency band. Full polarimetric radar data from the RADARSAT-2 satellite were used. We focused on target decomposition theory and the statistical classification approach using a Wishart distribution to identify saline-alkali soils. The precise validation of the classification results is based on 129 ground sampling points. The results indicate that the polarimetric classifications using the H-α¯ method performed poorly, with Kappa values of approximately 0.29. The classification method based on Freeman-Durden decomposition showed better results, with Kappa values of approximately 0.54 and an overall accuracy of 68.22%. The best result was achieved using an input of anisotropy, with Kappa values of approximately 0.62 and an overall accuracy of 74.42%. The validity of the anisotropy approach implies that the scattering randomness of saline-alkali soil is very strong, which reflects the complex scattering characteristics of saline-alkali landscapes. Further study of the scattering characteristics of saline-alkali soil is necessary.

Paper Details

Date Published: 5 December 2014
PDF: 16 pages
J. Appl. Remote Sens. 8(1) 083511 doi: 10.1117/1.JRS.8.083511
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Yangyang Li, Northeast Institute of Geography and Agroecology (China)
Dezhou Univ. (China)
Kai Zhao, Northeast Institute of Geography and Agroecology (China)
Xingming Zheng, Northeast Institute of Geography and Agroecology (China)
Jianhua Ren, Northeast Institute of Geography and Agroecology (China)
Univ. of Chinese Academy of Sciences (China)
Yanling Ding, Northeast Institute of Geography and Agroecology (China)
Univ. of Chinese Academy of Sciences (China)
Lili Wu, Northeast Institute of Geography and Agroecology (China)
Univ. of Chinese Academy of Sciences (China)


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