The scientific work describes the algorithms for processing the multispectral water coastal imagery from satellite sensing data with the aim of identifying the phytoplankton population of a spotted structure: determining the contour, distributing color gradation and as a result - determining the concentration of phytoplankton distribution inside the zones and mass centers. Such characteristics let determine the speed of changing contours spots and their concentration, the mass center shift as a consequence of the water masses movement and the processes of phytoplankton growing and dying. All these may be done on the base of the processed image series of the same water area over different time (different dates). The combination of LBP and neural network methods are observed as algorithms for image processing and the results of computer experiments are presented.

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The study is devoted to the analysis of satellite observations data assimilation to discover the necessary information for developing and verifying mathematical models of hydrodynamics and biological shallowwater kinetics. The use of satellite earth sensing data is taken to enhance information base. The possible use of neural networks with optical flow computation is considered in the study. The objective of the study is to develop a software tool used to identify the initial conditions in mathematical modeling of hydrobilogical shallow-water processes.

The article shows an application of satellite sensing data method in geoenvironmental monitoring of water surface. It is expected to apply combination of LBP and neural network approaches for detection and identification objects of natural and anthropogenic origin. The applying of satellite images, the implementation and operation of the filtration method and satellite sensing data assimilation in real or near-real time are considered to detect the blooming areas and their coordinates. The research demonstrates the need and possibility to apply neural approach and the theory of deep learning for solving the tasks. The results of computer experiments are presented basing on the images from satellites Resurs-P, WorldView and Landsat over the Azov sea area.