The primary measurement objective of the Advanced Scatterometer ASCAT, a spaceborne real aperture C band
radar, is the determination of wind fields at the ocean surface. Unlike AMI instruments on-board ERS satellites,
ASCAT uses long transmit pulses with linear frequency modulation (chirps) allowing the application of low peak
transmission power while retaining a high SNR. A pulse-compression is performed on the received signal.
This paper will focus on the impact of the use of pulse compression in particular on the location accuracy
of the samples in presence of external perturbations. An eventual location error has important consequences on
the normalization as well as on the geolocation of the measured data.
From the beginning of its mission, in 1995, the ERS-2 satellite has recorded an important
set of data. The performance and accuracy of its instrument provide precious
information for the scientific community. The experience acquired during
10 years has led the European Space Agency (ESA) to plan a reprocessing activity of the
entire set of the available scatterometer data.
This reprocessing activity will use the enhanced on-ground processing1
and calibration2 chains.
In this paper, the calibration strategy for the scatterometer data reprocessing
from the beginning of the mission is presented.
It consists in looking for
a calibration area (rainforest, ocean or ice) which would allow a highly accurate tuning of the
antenna patterns (already tuned within the specifications).
Although other uses have emerged, ERS scatterometer data is operationally used to measure wind speed at the surface of the oceans. The wind speed and direction can indeed be inverted from the measured backscattering coefficients provided the measurements were performed over sea. While a land-mask can be used to reject measurements made over land, operational constraints make the use of an externally-provided ice-mask unpractical. It is thus desirable to discriminate between measurements made over sea and measurements made over ice using the backscattering coefficients alone. Due to
operational constraints, a temporal averaging of the measurements
is not feasible. Several methods have been proposed to discriminate between sea and ice. These are based on measuring the distance
between the measurements made and a model. An ice model and a wind model are available. Measurements located far from the ice model were most likely not performed over ice and similarly, measurements close to the wind model were most likely performed over sea. However, for particular values of the incidence angles, these models are very close to each other, which leads to classification errors. In this paper, we propose to enhance the criterion of the distance to the wind model by taking into account the wind direction. This permits a better discrimination between ice-and sea-measurements. The enhanced criterion is implemented using a neural-network. The other methods proposed in the literature are also implemented in the same neural-network framework, which permits an easy comparison of their relative performances. Finally, the various methods are combined in a Bayesian framework.