Variability in ERS scatterometer measurements over land

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.