Wind scatterometers are radar systems designed specifically to measure the normalized radar backscatter coefficient (O) of the ocean's surface in order to determine the near-surface wind vector. Postlaunch calibration of a wind scatterometer can be performed with an extended-area natural target such as the Amazon tropical rain forest. Rain forests exhibit a remarkably high degree of homogeneity in their radar response over a very large area though some spatial and temporal variability exist. The authors present a simple technique for calibrating scatterometer data using tropical rain forests, Using a polynomial model for the rolloff of O with incidence angle, the technique determines gain corrections to ensure consistency between different antennas and processing channels. Corrections for the time varying instrument gain are made consistent with a seasonally fixed rain forest response; however, without ground stations or aircraft flights, it is difficult to uniquely distinguish between seasonal variations in the rain forest and slow variations of the system gain. Applying the corrections, the intrinsic variability of the O of the rain forest is estimated to be ±0.15 dB, which is the limit of the accuracy of calibration using the rain forest. The technique is illustrated with Seasat scatterometer (SASS) data and applied to ERS-1 Active Microwave Instrument scatterometer (Escat) data. Gain corrections of up to several tenths of a decibel are estimated for SASS. Corrections for Escat data are found to be very small, suggesting that Escat data is well calibrated.
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