Meris Case-2 Regional Algorithm Specification

Background Information

Details of the algorithms are provided in two ATBDs, for atmospheric correction (not yet released) and for the retrieval of water constituents. This gives only a brief overview.

Atmospheric Correction

The atmospheric correction is based on the principle idea that the path radiances as well as the transmittances of the first 9 MERIS bands (412 -708 nm) can be derived from the top of atmosphere directional radiance reflectance of 4 MERIS bands in the near infrared spectral range (708 - 870 nm). We assume that the variability of the path radiances and transmittances of all bands, for a standard atmosphere, depend on the aerosol types and concentrations in different layers of the atmosphere, on thin cirrus clouds, and on the specular reflectance of scattered light at the sea surface (sky light glitter) and on the specular reflectance of direct sun light at the sea surface (sun glint), both of which are wind dependent. By knowing the reflectance in the 4 NIR bands it is possible to determine the path radiance and transmittance of first 9 MERIS bands, which are used for water remote sensing. Since the absorption of pure water in the NIR bands is very high, the contribution of other water constituents to the total absorption can be neglected.
In turbid water the 4 NIR bands might also be affected by the scattering of particles in water. Thus an algorithm as we provide here for case 2 water has to take this effect into account.
The techniques we have used to determine the path radiances and transmittances from these 4 NIR bands is a neural network. It is trained by simulated top of standard atmosphere directional radiance reflectances. Input to the neural network are the reflectances in the 4 NIR bands as well as the solar and viewing zenith angel and the difference between solar and viewing azimuth angle. Output of the NN are the 9 path radiances and transmittances of the first 9 MERIS bands. This information can then be simply used to compute the directional water leaving radiance reflectances for these 9 bands. The NN is applied to the reflectances top of a standard atmosphere (TOSA). These reflectances are derived from the Top of Atmosphere (TOA) radiance reflectances by taking the difference between the standard and actual surface pressure into account to correct for the scattering of air molecules (Rayleigh scattering) of this correction layer. In the same way the difference between the standard and actual ozone concentration is used in this correction layer (details s. ATBD Atmospheric correction). The correction for these two deviations from a standard atmosphere is performed by an analytical algorithm. This algorithm converts TOA radiance reflectances into TOSA radiance reflectances, which are thin input to the NN.
Furthermore, we have included in this correction procedure an optional (s. parameter file) correction for the spectral deviations of the individual pixels of each camera due to the remaining misalignment of the cameras (in other papers called "smile correction"). This misalignment has mainly an effect on the path radiance calculation (scattering by air molecules) and on the solar irradiance at top of atmosphere.
A further, experimental, add on is a special neural network for taking the influence of the polarisation of light by air molecules into account for the atmospheric correction. Also this procedure can be optionally switched on by a parameter.
Output products of the atmospheric correction are the TOSA radiance reflectances, the path radiances, the transmittances and the water leaving radiances. The inclusion of all of these variables in the output product file can be selected in the parameter file band by band individually.

Retrieval of Water Constituents and IOPs

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.