干旱区生态环境知识资源中心

Using ERS scatterometer data effectively to relate large scale surface processes to driving parameters such as vegetation type, weather, soil, etc., requires the data be referenced to a regular geographic grid. The averaging involved may conceal systematic variation in sigma(0) in time, space, and across incidence angle. The problems in identifying the sources of variation at a given position are discussed and the relative magnitudes of the different variability components are evaluated. Variability indices can be defined at each gridpoint, of which the most useful appears to be the coefficient of variation after model-based correction for incidence angle effects. Images of variability indicate that for about 75% of the land surface, the mean sigma(0) images can be considered representative. However, some cover types exhibit continual, significant variability on short timescales, particularly grasslands and sandy deserts. Other cover types display seasonal variation, which appears to be related to vegetation, snow cover, and the freeze/thaw cycle. Variability measures also provide clear indications of occasional data problems even where no data quality flags are set.