Radiometric Cross-Calibration of Spaceborne Scatterometers - First Results

摘要

The main application of a scatterometer is the determination of the wind speed and direction at the sea surface. This is achieved by measuring the radar backscattering coefficient in three different directions and inverting these measurements using a geophysical model function (GMF). The scientific value of the data is directly related to the quality of the radiometric calibration. There are currently two european C-band scatterometers operating, one on-board the ERS-2 spacecraft launched in 1995 and the other on-board METOP-A, launched in 2006. The similarity of the two scatterometers is an opportunity to ensure the continuity of more than 15 years of global scatterometer measurements. To achieve the consistency of the backscattering coefficients data sets, required for long-term climate studies, an accurate cross-calibration is vital. The cross-calibration is made possible since the two spacecrafts operate simultaneously from 2006 up to now. As the backscattering coefficients measured by the scatterometers depend on acquisition time, location on the ground and on the geometry of the measurements (incidence and look angle), a direct comparison of measurements made by both instruments is practically impossible. In particular cases, models can be used to cope with measurement differences. On the rain forest, assumed to he timeinvariant, homogeneous and isotropic, the backscattering coefficient depends only on the incidence angle, and the constant gamma model can be used to cope with the incidence angle effects. On some ice covered areas (e.g. Greenland and Antarctica), assuming that the ice surface is isotropic, the ice line model can be used. It is a function of incidence angle and ice age and depends on the location. On the ocean, which is inherently not stable in time, the CMOD5 GMF is used. CMOD5 relates the observed backscatter to the geophysical parameters which are the wind speed and wind direction. Using the last model, measurement biases can be assessed making simultaneous observations unnecessary. In this article, we present a cross-calibration methodology and present first results.