Possibility of Using a Satellite-Based Detector for Recording Cherenkov Light from Ultrahigh-Energy Extensive Air Showers Penetrating into the Ocean Water

We have estimated the reflected component of Cherenkov radiation, which arises in developing of an extensive air shower with primary energy of 10^20 eV over the ocean surface. It has been shown that, under conditions of the TUS experiment, a flash of the reflected Cherenkov photons at the end of the fluorescence track can be identified in showers with zenith angles up to 20 degrees.Comment: 5 pages, 3 figures. This preprint corrects errors which appeared in the English version of the article published in Bull. Rus. Acad. Sci. Phys., 2011, Vol. 75, No. 3, p. 381. The original russian text was published in Izv. RAN. Ser. Fiz., 2011, Vol. 75, No. 3, p. 41

Neural network training: using non-logarithmic or logarithmic training data for the inversion of ocean colour spectra?

A bio?optical model coupled with the radiative transfer model Hydrolight was used to create 18,000 synthetic ocean colour spectra corresponding to open ocean and coastal waters. The bio?optical model took into account the optical properties of the three oceanic constituents, chlorophyll?a, suspended non?chlorophyllous particles and coloured dissolved organic matter (CDOM) as well as of normal seawater. The resulting spectra were input into multilayer perceptron neural network algorithms with the aim of computing the original concentrations of chlorophyll?a, non?chlorophyllous particles and CDOM initially input into the bio?optical model. The process of training the neural networks is essential for the accuracy of the inversion the neural net performs on the coupled bio?optical and radiative transfer models. The objective of this paper is to investigate the performance difference of a neural network trained with untransformed as opposed to logarithmically transformed dat

A comparison of multi-layer perceptron and multilinear regression algorithms for the inversion of synthetic ocean colour spectra

Artificial radiance sets were used as inputs to Multi-layer Perceptron and multilinear regression algorithms to study their retrieval capabilities for optically active constituents in sea water. The radiative transfer model Hydrolight was used to produce 18,000 artificial reflectance spectra representing various case 1 and case 2 water conditions. The remote sensing reflectances were generated at the Medium Resolution Imaging Spectrometer (MERIS) wavebands 412, 442, 490, 510, 560, 620, 665 and 682 nm from randomly generated triplet combinations of chlorophyll a, non-chlorophyllous particles and CDOM (Coloured Dissolved Organic Matter) concentrations. These reflectances were contaminated with different noise terms, before they were used to assess the performance of multilayer perceptron and multilinear regression algorithms. The potential of both algorithms for retrieving optically active constituents was demonstrated with the neural network showing more accurate results for case 2 scenarios

Prioritizing ocean colour channels by neural network input reflectance perturbation

The radiative transfer model Hydrolight was used to produce 18000 artificial reflectance spectra representing case 1 and case 2 water conditions. Remote sensing reflectances were generated at the MERIS wavebands 412, 442, 490, 510, 560, 620, 665 and 682nm from randomly generated triplet combinations of chlorophyll a, non-chlorophyllous particles and coloured dissolved organic matter concentrations. These spectra were used to train multilayer perceptron neural network algorithms to perform the inversion from input reflectances to these three optically active substances. A method is proposed that establishes the neural network output error sensitivity towards changes in the individual input reflectance channels. From the output error produced for each reflectance change, a hypothesis about the importance of each band can be made. Results suggest a strong weight associated to the 620nm band for the estimation of all three substances

Retrieval of optically active parameters from oceanic reflectance spectra using multi-layer perceptron and K-Nearest neighbour algorithms

Artificial radiance sets, generated with the use of a biooptical and radiative transfer model Hydrolight, corresponding to Case 1 and Case 2 waters, are used as inputs to Multi-layer Perceptron and K-NN algorithms to study the algorithm’s retrieval capabilities for optically active constituents in the water. The radiative transfer model Hydrolight has been used to produce 18,000 artificial reflectance spectra representing various Case 1 and Case 2 water conditions. The remote sensing reflectances were generated at the MERIS wavebands 412, 442, 490, 510, 560, 620, 665 and 682nm from randomly generated triplet combinations of phytoplankton, non-chlorophyllous particles and CDOM concentrations. These reflectances were then used to assess the performance of the KNearest Neighbour and the Multilayer Perceptron algorithms, which were compared to some more traditional band ratio regression algorithms that had been a popular choice for CZCS and SeaWiFS imagery. The objective of the work was to establish the best kind of algorithm for this type of application