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Soil roughness plays an essential role in the reflection of the incoming radar signal at the soil surface and is, therefore, highly important in the retrieval of the soil moisture information from the backscattered radar signal. However, soil roughness, generally described by means of the root mean square (rms) height and the correlation length, remains difficult to measure correctly and is, furthermore, found to be highly variable. In order to overcome these difficulties, Verhoest et al. suggested the use of possibility distributions to reflect possible values of roughness parameters for a given roughness state of an agricultural field. These distributions were then further used to retrieve the soil moisture information. Nevertheless, as they estimated the possibility distributions by brute force, without taking into account any interactivity between the roughness parameters, rather wide distributions of retrieved soil moisture content were obtained. This paper first tries to independently estimate the possibility distributions for both roughness parameters on the basis of a synthetically generated roughness data set. Next, the interactivity between the rms height and the correlation length is taken into account through the identification of a joint possibility distribution by means of a possibilistic clustering algorithm. When applied to actual synthetic aperture radar data, the results show that a narrower, i.e., more specific, possibility distribution of the soil moisture content is obtained when the possibilistic retrieval procedure is performed based on the joint possibility distributions.