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.