A study on the extracting soil moisture information by fusion of optical and radar data

Fan Yu; Yingshi Zhao; Qian Liu

doi:10.1117/12.832273

30 October 2009 A study on the extracting soil moisture information by fusion of optical and radar data

Fan Yu, Yingshi Zhao, Qian Liu

Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 749839 (2009) https://doi.org/10.1117/12.832273
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Water is a key variable in describing the water and energy exchanges between the land surface and atmosphere interfaces. In this paper a classifier is presented, which is based on integration of both active and passive remote sensing data and the Maximum Likelihood classification for inversion of soil moisture and this method is tested in Heihe river basin, a semi-arid area in the north-west of china. In the algorithm the wavelet transform and IHS are combined to integrate TM3, TM4, TM5 and ASAR data. The method of maximum distance substitution in local region is adopted as the fusion rule for prominent expression of the detailed information in the fusion image, as well as the spectral information of TM can be retained. Then the new R, G, B components in the fusion image and the TM6 is taken as the input to the Maximum Likelihood classification, and the output corresponds to five different categories according to different grades of soil moisture. The field measurements are carried out for validation of the method. The results show that the accuracy of completely correct classification is 66.3%, and if the discrepancy within one grade was considered to be acceptable, the precision is as high as 92.6%. Therefore the classifier can effectively be used to reflect the distribution of soil moisture in the study area.

Citation Download Citation

Fan Yu, Yingshi Zhao, and Qian Liu "A study on the extracting soil moisture information by fusion of optical and radar data", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749839 (30 October 2009); https://doi.org/10.1117/12.832273

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