Visualizing and labeling dense multisensor earth observation time series: The EO Time Series Viewer Abstract   Multispectral spaceborne sensors with different spatial resolutions produce Earth observation (EO) time series (TS) with global coverage. The interactive visualization and interpretation of TS is essential to better understand changes in landuse and landcover and to extract reference information for model calibration and validation. However, available software tools are often limited to specific sensors or optimized for applicationspecific visualizations. To overcome these limitations, we developed the EO Time Series Viewer, a free and open source QGIS plugin for userfriendly visualization, interpretation and labeling of multisensor TS data. The EO Time Series Viewer (i) combines advantages of spatial, spectral and temporal data visualization concepts that are so far not available in a single tool, (ii) provides maximum flexibility in terms of supported data formats, (iii) minimizes the userinteractions required to load and visualize multisensor TS data and (iv) speedsup labeling of TS data based on enhanced GIS vector tools and formats.

Toward Spatioxe2x80x93Spectral Analysis of Sentinel2 Time Series Data for Land Cover Mapping Modern earth observation (EO) systems produce huge volumes of images with the objective to monitor the earth surface. Due to the high revisit time of EO systems, such as Sentinel2 constellation, satellite image time series (SITS) is continuously produced allowing to improve the monitoring of spatiotemporal phenomena. How to efficiently analyze SITS considering both spectral and spatial information is still an open question in the remote sensing field. To deal with SITS classification, in this letter, we propose a spatioxe2x80x93spectral classification framework that leverages the mathematical morphology to extract spatial characteristics from SITS data and combines them with the already available spectral and temporal information. Experiments carried out on two study sites characterized by different heterogeneous land covers have demonstrated the significance of our proposal and the value to combine spatial as well as spectral information in the context of SITS land cover classification.