AbstractThe algorithm to derive the concentrations of coastal (case 2) water constituents from the Medium Resolution Imaging Spectrometer (European Space Agency satellite ENVISAT) is based on neural network (NN) technology. The NN not only transforms water leaving radiance reflectances with high efficiency into concentrations but also checks if its input is in the domain of reflectance spectra which were simulated for the training of the NN. Two NNs are trained with simulated reflectances: (1) invNN to emulate the inverse model (reflectances, geometry) /spl rarr/ concentrations and (2) forwNN to emulate the forward model (concentrations, geometry) /spl rarr/ reflectances. The invNN is used to obtain an estimate of the concentrations. These concentrations are fed into the forwNN, and the derived reflectances are compared with the measured reflectances. Deviations above a threshold are flagged. The paper describes a further improvement: the result obtained by invNN is used as a first guess to start a minimization procedure, which uses the forwNN iteratively to minimize the difference between the calculated reflectances and the measured ones. The procedure is very fast as it takes advantage of the Jacobian which is a byproduct of the NN calculation.