Estimation of sea surface spectrum under non-stationary conditions

One of the goals of ESA´s SMOS mission is the measurement of global sea surface salinity maps using MIRAS, a synthetic aperture interferometric L-band radiometer with full-polarimetric capabilities. To do so, sea state effects on the brightness temperature must be compensated, but coincident measurements of wind speed and/or significant wave height are uncommon. The objective of this work is to estimate the sea surface spectrum at a given time and location from the previous temporal and spatial evolution of the wind conditions. To do so, a neural network has been trained to estimate the spectrum parameters (height variance 2>, spectral deviation σ_x,y and spectral peak of swell k_xm,ym, wind velocity V₁₀´) from the measured spectra. Different measured spectra have then been used to validate the network design. Finally, the error in the L-band brightness temperature computed from the actual and the estimated spectra is studied.