Continuous monitoring of phytopigment concentrations and sea surface temperature in the ocean by space-borne
methods makes possible to estimate ecological condition of biocenoses in critical areas. In the papers of the
authors (Shevyrnogov A.P., Vysotskaya G.S., Gitelzon J.I. 1996) existence of zones, which are quasi-stationary
with similar seasonal dynamics of chlorophyll concentration at surface layer of ocean, was shown. Results were
obtained on the base of processing of time series of satellite images SeaWiFS. It was shown that fronts and
frontal zones coincide with dividing lines between quasi-stationary areas, especially in areas of large oceanic
streams. The usage of the seasonal dynamics gives a possibility to circumvent influence of high-frequency
component in investigation of dynamics of spatial distribution of surface streams. In addition, an analyses of
unstable ocean productivity phenomena, stood out time series of satellite images, showed existence of areas
with different types of instability in the all Global ocean. They are observed as adjacent nonstationary zones of
different size, which are associated by different ways with known oceanic phenomena. It is evident that
dynamics of a spatial distribution of biological productivity and sea surface temperature can give an additional
knowledge of complicated picture of surface oceanic layer hydrology.
The time series of various parameters of satellite imagery (NDVI/EVI, temperature) during the growing season were
considered in this work. This means that satellite images were considered not like a number of single scenes but like
temporal sequences. Using time series enables estimating the integral phenological properties of vegetation. The basis of
the developed technique is to use one of the methods of transformation of the multidimensional space in order to get the
principal components. The technique is based on considering each dimension of the multidimensional space as satellite
imagery for a specific date range. The technique automatically identifies spatial patterns of vegetation that are similar by
phenology and growing conditions. Subsequent analysis allowed identification of the belonging of derived classes.
Thus, the technique of revealing the spatial distribution of different dynamical vegetation patterns based on the
phenological characteristics has been developed. The technique is based on a transformation of the multidimensional
space of states of vegetation. Based on the developed technique, areas were obtained with similar interannual trends.