Copernicus programme provide a huge amount of image data for many applications around the globe. Besides data being free and open, Sentinels also holds great potential to use dense observation time-series in various dynamical Earth environment monitoring applications. The amount of EO data is extensive, therefore it is of high importance to develop automated tools to process and analyse the data. In the FGI, we have developed a toolkit (EODIE) to automatically process Sentinel satellite images, to extract time-series data (NDVI or other index, or SAR backscattering values). When the EO time-series data are combined with ancillary information (training data set), it is possible to design intelligent classification tools to automatically identify causes for environmental changes. Our objective is to use machine learning techniques, Random forest and Neural Networks based methods, and design a classification tool to be used in automatic identification of environmental changes. We aim to test the classification toolkit in the field of agriculture (crop species classification in Finland), forestry (forest thinning mapping in Finland), and coastal area monitoring (China). The proposed research connects the complementary experiences of the European and Chinese partners to develop innovative techniques for analysing EO time-series data. The research directly aims at advancing the scientific careers of young researchers involved with the project by providing good opportunities to write articles for top journals in the field of remote sensing. For example, the use of EODIE toolkit (Earth Observation Data Information Extractor) is already applied in the demonstration projects in agricultural mapping in Finland, and likely of interest to the scientific community in Dragon 5 context. The proposed research is partially funded by the organizations of the reseach teams and other outside funded projects.