Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation Abstract   Land degradation is a critical issue globally requiring immediate actions for protecting biodiversity and associated services provided by ecosystems that are supporting human quality of life. The latest Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services Landmark Assessment Report highlighted that human activities are considerably degrading land and threating the wellbeing of approximately 3.2 billion people.  In order to reduce and ideally reverse this prevailing situation, national capacities should be strengthened to enable effective assessments and mapping of their degraded lands as recommended by the United Nations Sustainable Development Goals (SDGs). The indicator 15.3.1 (xe2x80x9cproportion of land that is degraded over total land areaxe2x80x9d) requires regular data production by countries to inform and assess it through space and time. Earth Observations (EO) can play an important role both for generating the indicator in countries where it is missing, as well complementing or enhancing national official data sources.  In response to this issue, this paper presents an innovative, scalable and flexible approach to monitor land degradation at various scales (e.g., national, regional, global) using various components of the Global Earth Observation System of Systems (GEOSS) platform to leverage EO resources for informing SDG 15.3.1. The proposed approach follows the DataInformationKnowledge pattern using the Trends.Earth model ( http:trends.earth ) and various data sources to generate the indicator. It also implements additional components for model execution and orchestration, knowledge management, and visualization.  The proposed approach has been successfully applied at global, regional and national scales and advances the vision of (1) establishing data analytics platforms that can potentially support countries to discover, access and use the necessary datasets to assess land degradation; and (2) developing new capacities to effectively and efficiently use EObased resources.

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.