| Meris Boreal Lakes Bio-Optical Model Specification | |
Details of the algorithm for the retrieval of water constituents are provided in an ATBD, which can be found at
http://www.brockmann-consult.de/beam-wiki/display/LAKES/Home
The following gives only a brief overview.
Although it is possible to perform the atmospheric correction and retrieval of water constituents in
one step with one neural network, we have separated these tasks in order not to involve a specific
bio-optical model in the atmospheric correction, so that the use of only the atmospherically
corrected data is independent from the optical properties of the water constituents as assumed in a
bio-optical model.
The retrieval of the water constituents from the directional water leaving radiance reflectances is
performed also with a neural network.
In this version of the processor we provide two alternatives, which can be selected by the parameter
file. In both cases the networks are trained with simulated directional radiance reflectances. Details
of the bio-optical model, the simulations and training procedure can be found in Doerffer & Schiller
(2006) and in the ATBD.
The first version is a combination of a backward and forward neural network. Input to the backward
network are the directional water leaving radiance reflectances of 8 MERIS bands (412 - 708 nm,
excluding the fluorescence band at 681 nm). Output of the network are 3 IOPs, i.e. (1) scattering of
all particles, (2) absorption of phytoplankton pigments and (3) absorption of Gelbstoff and bleached
particles, all three at MERIS band 2 (442 nm). These coefficients are also input to a forward neural
network which computes the water leaving radiance reflectances. The computed reflectances are
then compared with the measured ones (derived after atmospheric correction). In the case that the
sum of the squared deviations between both spectra surmounts a threshold of 4 a warning flag is
triggered. Furthermore the values of the agreement is also provided as a selectable product.
The advantage of this alternative is that it is rather fast.
The second selectable alternative is based on an optimization procedure. It uses only the forward
neural network. By modifying the three IOPs in the optimization loop an optimum agreement
between the simulated and measured spectrum of the directional water leaving radiance
reflectance is searched. If the deviation is below a certain minimum or if the maximum number of
iterations is reached, the three IOPs are provided as the result. Furthermore, the remaining Chi2
deviation is also provided.
Comparisons between both alternatives have shown that in most cases the remaining deviation is
significantly smaller when the optimization procedure is used instead of the backward neural
network. Also the results show less noise. However, this alternative requires significantly more
computation time.