IMT Neural Net
The Inverse Modelling Technique (IMT) uses an Inverse Radiative Transfer Model-Neural Network (IRTM-NN) to estimate from normalised water-leaving reflectance at OLCI bands:
b400 to b674, b709, θs, θv and Δϕ the log10 of
- the absorption coefficient of humic acids (detrital material)
- the absorption coefficient of fulvic acids (Gelbstoff material)
- the absorption coefficient of algal pigment (Chlorophyll)
- the scattering coefficient of total suspended matter
- the scattering coefficient of white particles.
The IMT considers the complex nature of water-leaving reflectance and its parameterisation avoids any iterative procedures. The multiple non-linear regression method in this approach leads to a high reduction in computing time and is therefore fast enough for operational mass production of Level-2 products, but it requires a careful and elaborate determination of the multiple coefficients (training phase). The IRTM-NN procedure is already programmed and requires only data input. The data outputs from this algorithm are water-inherent optical properties:
- total backscattering coefficient BBP443
- total absorption coefficient ATOT443
- phytoplankton absorption coefficient APH443
- coloured Detrital and Dissolved Material absorption coefficient ADG443
Further used to derive the water constituents:
- Chlorophyll (Chl2) concentration
- Total Suspended Matter (TSM) concentration.
A quality indicator (represented by the flag in the following figure), which measures the average difference between the reflectances observed and those derived from the water's inherent optical properties, through a forward model neural network.
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