A scatterometer neural network sensor model with input noise
The ERS-2 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network-based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected as it significantly degrades the performance of the model. To account for this noise, a Bayesian framework is adopted. Gradient information is used with a non-linear optimisation algorithm to find the maximum a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model both by objective measures and when visualised in the target space.
scatterometer non-linear regression input uncertainty wind retrieval