Estimationofwater column parameters with a maximum likelihood approach

In this article, we use a well-known reflectance model of a water column for estimating the model parameters (depth and concentrations of different water constituents) with a maximum likelihood approach. Tested on simulated data, the method performs well, especially for depths between a few meters and about 10m, and a SNR greater than 10dB. Moreover, we calculate the Cramér-Rao lower bounds in order to assess the performances of this estimation process. We show that the variances of the estimators come closer to these CRBs when the number of training pixels grows. Moreover, it turns out that the ML estimates of C_φ, Ccdom and C_Nap are efficient even for low sample sizes.