Researches On The Management And Sharing Of Marine And Atmospherical Satellite Remote Sensing Data

Satellite remote sensing has the advantages of wide range, real-time synchronization, all-time, all-weather, technical superiority, multi- wave bands image formation and so on. It can gather about 70-80% ocean and atmospheric environment parameter data, which can provide a brand-new data for the research, monitoring, development and protection of the ocean. Along with the fast development of ocean and atmosphere sciences and the abundant datasets in these fields, the management and the share of these data are getting more and more concern. In order to support the spatial analysis and the decision-making, how to effectively manage and share the ocean and atmospheric satellite remote sensing data has become an urgent issue. As a result of the multi-dimensional dynamic characteristic of the ocean and atmosphere environment, and the unique data acquisition method of satellite remote sensing technology, the marine and atmospheric satellite remote sensing data have the characteristics of extremely large volume, complexity of space and time sampling, diversify of product level. Meanwhile, different users have diversified demands. All these reasons render that the management and share of marine and atmospheric satellite remote sensing data is one of the key and hot topics in both international and domestic research fields. This dissertation discusses several important issues in the management and share of marine and atmospheric satellite remote sensing data, such as the metadata, the advanced application of genericl data formats, the establishment of data warehouse and related practical work. The main works of this dissertation are as follows:(1) according to the characteristics of marine and atmospheric satellite remote sensing data, the metadata of those data are designed based on the analysis and the comporation of the existing geographial spatial metadata standards and some marine metadata contents, and detailed introduction of the metadata of sea surface temperature dataset and the sea level wind field are given as the examples.(2) which has been widely applied in the distribution and share of spatial data, data formats content and data model structure of HDF and NetCDF are given in detail and the format conversion procedure with the C++ language are given at the end.(3) on the bases of thorough analysis of the spatial and temporal distribution...