SemantiX: a cross-sensor semantic EO data cube to open and leverage AVHRR time-series and essential climate variables with scientists and the public

Long time series of essential climate variables (ECVs) derived from satellite data are key to climate research. ECVs comprise a representative set of physical, chemical, or biological variables or a group of linked variables that critically contribute to the characterisation of Earth’s climate system’s state, interactions and developments (Bojinski et al. 2014). They are a critical, independent source of information to compare with climate model results and can also be used to directly detect and monitor changes in our environment. One of the longest European time series (1981-now) of Advanced Very High Resolution Radiometer (AVHRR) imagery will be compiled and archived by the Remote Sensing Research Group at University of Bern. Until now, AVHRR imagery has only been accessible via sequential access, requiring a significant time investment and expert knowledge to find relevant data for analysis. SemantiX is a new project to establish, complement and expand AVHRR time series using Copernicus Sentinel-3 A/B imagery and make them and derived ECVs accessible using a semantic Earth observation (EO) data cube. To the best of our knowledge, SemantiX will establish the first EO data cube based on semantically-enriched AVHRR imagery and has the potential to open the AVHRR archive and derived ECVs to a wider audience. Data cube technologies are a game changer for how EO imagery are stored and accessed, but more importantly in how they establish reproducible analytical environments for queries and information production and in how they can better represent multi-dimensional systems. A semantic EO data cube is a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. (Augustin et al. 2019). Such a tool facilitates easier data access for scientists and, in this case, will join the backend of a smartphone application providing visualisation targeted to non-expert users. Offering analysis ready data (i.e., calibrated and orthorectified AVHRR data) in a data cube along with semantic enrichment reduces barriers to conducting spatial analysis through time based on user-defined AOIs and improves data access by enabling queries of image content instead of being limited to querying imagery based on when images were acquired and the area covered. The proposed data cube of AVHRR and Sentinel-3 imagery, and derived-information including selected ECVs (i.e., snow cover extent, lake surface water temperature, vegetation dynamics) will be linked to a mobile citizen science smartphone application. For the first time, various target groups will have a new, direct and interactive access point and simplified access to EO imagery and derived information, including ECVs. Scientists from disciplines unrelated to remote sensing, students (i.e., the next generation of scientists) as well as interested members of the public will have direct access to long EO data time series for a variety of applications and location-based access through the mobile citizen science application. SemantiX runs from August 2020-2022 funded by the Austrian Research Promotion Agency (FFG) under the Austrian Space Applications Programme (ASAP 16) (project # 878939) in collaboration with the Swiss Space Office (SSO). This contribution presents a prototypical semantic EO data cube containing a short, temporal subset of AVHRR imagery (updated after Hüsler et al. 2011). The AVHRR time-series has been semantically-enriched using the Satellite Image Automatic Mapper (SIAM). SIAM applies a fully automated, spectral rule-based routine based on a physical-model to assign spectral profiles to colour names with known semantic associations; no user parameters are required, and the result is application-independent (Baraldi et al. 2010). Existing probabilistic cloud masks generated by the Remote Sensing Research Group (Musial et al. 2014) are also included in the semantic EO data cube as additional data-derived information to support spatio-temporal semantic queries. This prototypical implementation is a very first step towards the overall objective of combining climate-relevant AVHRR time series with Sentinel-3 imagery for the Austrian-Swiss alpine region, a European region that is currently experiencing serious changes due to climate change that will continue to create challenges well into the future. REFERENCES Augustin, H., Sudmanns, M., Tiede, D., Lang, S., & Baraldi, A. 2019: Semantic Earth Observation Data Cubes. Data, 4(3), 102. https://doi.org/10.3390/data4030102 Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., & Blonda, P. 2010: Automatic Spectral-Rule-Based Preliminary Classification of Radiometrically Calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 Imagery—Part I: System Design and Implementation. IEEE Transactions on Geoscience and Remote Sensing, 48(3), 1299–1325. https://doi.org/10.1109/TGRS.2009.2032457 Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., & Zemp, M. 2014: The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy. Bulletin of the American Meteorological Society, 95(9), 1431–1443. https://doi.org/10.1175/BAMS-D-13-00047.1 Hüsler, F., Fontana, F., Neuhaus, C., Riffler, M., Musial, J., & Wunderle, S. 2011: AVHRR Archive and Processing Facility at the University of Bern: A comprehensive 1-km satellite data set for climate change studies. EARSeL EProceedings, 10(2), 83–101. Musial, J. P., Hüsler, F., Sütterlin, M., Neuhaus, C., & Wunderle, S. 2014: Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery. Atmospheric Measurement Techniques, 7(3), 799–822. https://doi.org/10.5194/amt-7-799-2014