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Medium- to low-resolution active microwave sensors such as spaceborne scatterometers and wide-swath mode synthetic aperture radars have great potential as tools for long-term monitoring over land and ice. Their large area coverage and low data cost make them ideal for measuring mesoscale land-surface changes. One approach to the interpretation of such data is to employ a theoretical backscatter model that can be inverted to estimate some representative surface parameters. To optimize such a method, it is necessary to use scattering models that are applicable to large (>1 km) footprints that inevitably contain a range of surface characteristics. This paper investigates the validity of a number of surface scattering models for such a task. The analysis is carried out both from a theoretical modeling approach as well as through comparison with real data from the European Remote Sensing (ERS) scatterometer over nonvegetated areas. Modified models that incorporate surface heterogeneity through probability distributions are also introduced. It is shown than the ERS scatterometer data are better represented using these modified models than the standard theoretical models. Good results are also obtained using the semiempirical model by Oh et al. (1992) that seems to implicitly incorporate target heterogeneity.