Using ERS scatterometer data effectively to relate large scale surface proc
esses to driving parameters such as vegetation type, weather, soil, etc., r
equires the data be referenced to a regular geographic grid. The averaging
involved may conceal systematic variation in sigma(0) in time, space, and a
cross incidence angle. The problems in identifying the sources of variation
at a given position are discussed and the relative magnitudes of the diffe
rent variability components are evaluated. Variability indices can be defin
ed at each gridpoint, of which the most useful appears to be the coefficien
t of variation after model-based correction for incidence angle effects. Im
ages of variability indicate that for about 75% of the land surface, the me
an sigma(0) images can be considered representative. However, some cover ty
pes exhibit continual, significant variability on short timescales, particu
larly grasslands and sandy deserts. Other cover types display seasonal vari
ation, which appears to be related to vegetation, snow cover, and the freez
e/thaw cycle. Variability measures also provide clear indications of occasi
onal data problems even where no data quality flags are set.