Bio-optical algorithms for European seas: Performance and applicability of neural-net inversion schemes

This report presents and discusses the application of Multi Layer Perceptron (MLP) neural networks to derive Chlorophyll-a concentration (Chl-a), absorption of the yellow substance at 412 nm (ays(412)) and concentration of the total suspended matter (TSM) from remote sensing reflectance RRS values. MLPs were developed on the basis of data collected within the framework of the Coastal Atmosphere and Sea Time Series (CoASTS) and Bio-Optical mapping of Marine Properties (BiOMaP) programs carried out by the Institute for Environment and Sustainability (IES) of the Joint Research Centre (Ispra, Italy). Investigated oceanographic regions include the Eastern Mediterranean Sea, the Ligurian Sea, the Northern Adriatic Sea, the Western Black Sea, the English Channel and the Baltic Sea. The study verifies the applicability of MLPs to retrieve ocean color data products in each basin. For instance, the highest accuracy in retrieving Chl-a has been found in the Easter Mediterranean Sea and the Ligurian Sea (14 and 25%, respectively). In the case of ays(412), the MLP is the most performing in the waters of the English Channel and the Baltic Sea (14 and 13%). Instead, the TSM retrieval is the most accurate in the Black Sea and at the Acqua Alta Oceanographic Tower (14 and 19%). To enhance mission specific ocean color resuls, MLP coefficients are also computed applying band-shift corrections to produce RRS spectra at wavelengths matching those of SeaWiFS, MODIS and MERIS. Resulting tables of MLP parameters are reported to permit independent applications of neural networks presented in this study. 1 Overview This report presents and discusses the application of Multi Layer Perceptron (MLP) neural networks to derive Chlorophyll-a concentration Chl-a, absorption of the yellow substance at 412 nm ays(412) and concentration of the total suspended matter TSM from remote sensing reflectance RRS spectral values. In agreement with methods developed in former investigations (D’Alimonte and Zibordi, 2003; D’Alimonte et al., 2004; Kajiyama et al., 2011) the MLPs applied in this study use RRS at individual wavelengths as input for bio-optical non-linear models applicable to selected European seas. In-situ data for MLP development and performance assessment are those collected in the Coastal Atmosphere and Sea Time Series (CoASTS 1998–2009, Zibordi et al., 2004) and Bio-Optical mapping of Marine Properties (BiOMaP 2000–2010, Zibordi et al., 2011) programs carried out by the Joint Research Centre (JRC). 1 Davide D’Alimonte, Giuseppe Zibordi, Jean-François Berthon, Elisabetta Canuti and Tamito Kajiyama Investigated oceanographic regions include the Eastern Mediterranean Sea (EMED, comprising areas of the southern Adriatic Sea), the Ligurian Sea (LIGS), the Northern Adriatic Sea (VADR, with both samples from the Acqua Alta Oceanographic Tower AAOT and geographically distributed field measurements NADR), the Western Black Sea (BLKS), the English Channel (ECHN) and the Baltic Sea (BLTS). Complementary analyses are undertaken with the NASA Bio-Optical Marine Data Set, NOMAD (Werdell and Bailey, 2005). Enhancing the MLP applications to remote sensing data for different ocean color missions is a major concern of the present study. For this purpose distinct MLP coefficients are computed applying band-shift corrections to produce RRS spectra at wavelengths matching those of SeaWiFS, MODIS and MERIS (Zibordi et al., 2011). Section 2 of this document reviews methods applied in MLP development and validation. The MLP performance to retrieve data products in the selected European seas is discussed in Section 3, together with MLP parameter tables. An analysis of cross-basin algorithm applicability is addressed in Section 4. Section 5 concludes the report with a result summary and remarks for improving the retrieval of ocean color products in optically complex coastal waters. 2 Methods As explained in Section 2.1, MLP “may be viewed as a practical vehicle for performing a nonlinear inputoutput mapping of general nature” (Haykin, 1994). Data pre-processing schemes applied in the present study to improve MLP performances (e.g. Bishop, 1995) are discussed in Section 2.2. Methods for MLP architecture selection and performance assessment are presented in Section 2.3. The description of the band-shift correction to compute RRS values at central wavelengths relevant to space-born sensors is given Section 2.4. 2.1 Multi Layer Perceptron Neural Network The Multi Layer Perceptron neural network (Figure 1) consists of successive layers of units—with each unit in one layer connected to each unit of the next layer (Bishop, 1995; Nabney, 2001)—and can represent any continuous input-output mapping (Haykin, 1994). Let y=f(x) denote the MLP mapping, where input vector x has entries xi for i = 1, . , d (d is the dimension of the input space). In the present analysis, xi quantifies the spectral remote sensing reflectance RRS at λ= 412, 442, 491, 511, 555, and 665 nm (or corresponding central wavelengths for mission-specific applications). Distinct algorithms are developed to retrieve Chl-a, ays(412) and TSM, so the MLP output y is a scalar. The MLP hidden units aj are linear combinations of the input values with the network weights: aj = b (1) j + d ∑