The earth observation sensors onboard the Nimbus and the NOAA satellites have provided an enormous amount of information about the sea surface temperature and the ocean colour. Ongoing and future satellite missions such as ERS, SEASTAR, ENVISAT, and EOS, are dedicated to integrate earth observation data from multiple channels and sensors.
The present marine geoid models only represents the global signatures of the geoid adequately, and they cannot be used for detailed studies of the ocean dynamics. In order to make further progress in the analysis of the ocean dynamics a more accurate geoid information is required. Until then the total flow and, consequently, the total heat transport of the ocean currents cannot be estimated. Therefore, both geodesists and oceanographers support a dedicated gravity field satellite mission, such as the GOCE mission that ESA is currently preparing.
The goal of the GEOSONAR project is to develop methods for integrating multi sensor and multi channel satellite data for improved recovery of the sea level height. This will be carried out at regional scales (10-20 km) in the North Atlantic region as well as at local scales (3-5 km) in the Danish seas. Hereby, the understanding of the ocean, its state, and its dynamics will be improved. In turn, this will lead to enhanced ocean tides modelling, sea level forecasting and storm surge warning. Furthermore, Denmark will contribute to the success of EU COST action 40 that is currently being signed. An important goal is also to prepare for the dedicated gravity mission and develop methods for enhanced analysis of the gravity field, so that Denmark can play a central role in the future determination of the geoid, the sea level, and possible effects of Global Change.
Geostatistical methods as kriging are based on the autocovariance or the semivariogram of the data to be interpolated. The usual spatial autocovariance can be extended to allow for temporal autocovariance also. Markov random field (MRF) theory based methods are also candidates for this type of analysis.
In order to exploit the collected multi-source data fully it is tempting to resample coarse scale data to the grid of the fine scale data. This can be done by means of geostatistical methods as kriging or by means of techniques based on MRF theory. As opposed to analyses of the individual groups of data statistical analyses of the joint multi-source data can reveal otherwise uncovered cross dependencies.
Classification of the relevant signals can be done by means of various classical forms of discriminant analysis. Also, spatial or contextual extensions to the classical methods and non-parametric classifiers such as Owen-Hjort-Mohn discriminant analysis and classification and regression trees (CART), a binary decision tree based method, can be applied. The discrimination will be based on optimal subsets of directly measured variables and derived variables (such as principal components and spatial extensions, texture variables etc.).
The multi-temporal analysis in this context consists primarily of canonical correlations analysis (CCA). Two- or multi-set CCA can be used to find variates that show decreasing similarity over either time or spectral wavelength. This can be used to find structures that change little over time (maximum similarity variates) or structures that change much over time (minimum similarity variates). Possible post-processing to find areas of high spatial coherence in these new similarity variates may be useful. Also the classifiers mentioned under heading 3) can benefit from the results of a multi-temporal analysis or the classifier itself can incorporate an autoregressive temporal part.
The results of the work will include better understanding of the nature of the physical and biological phenomena under study, and of the mathematical methods applied. Furthermore, the results will consist of healthy algorithms implemented as computer programs and the final end products from computer runs on the project data. These results can be used directly by the partners in the project, and the methods with examples from the project can be published in the international scientific literature.