BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:EEG &\; CEO (Centre for Earth Observation) Talk: Reconstructing Landsat Archive 1997-2024+: Sun\, Clouds\, Snow\, Noise and Humans - Tomi slav Hengl\, OpenGeoHub Foundation DTSTART:20260320T130000Z DTEND:20260320T140000Z UID:TALK241951@talks.cam.ac.uk CONTACT:114742 DESCRIPTION:*Abstract*\n\nA serious obstacle to the total uptake of open E arth Observation data (Copernicus Sentinel images\, NASA’s Landsat and s imilar) in daily lives is the steep data analysis curve required to get fr om raw images to Analysis-Ready\, Decision-Ready/Relevant\, not to mention Forensics ready data. The combined complexity of high data volumes\, atmo spheric disturbances (clouds\, haze) and inconsistent coverage and diverse and complex signal physics (e.g. radar images vs optical images\; sudden changes in land use) has resulted in the number of EO data applications re maining rather marginal. For example\, in Europe\, it is estimated that on ly a small fraction of farmers and forest managers use Sentinel images for decision-making. The recently generated Google DeepMind AlphaEarth (10 m global for 2017–2025) and Tessera embeddings being complete\, consistent and ARD\, provide an opportunity to decrease the steep data processing cu rve and enable thousands of applications. In our work\, we have also consi stently focused on making EO data more ARD and more usable\, primarily by aggregating Landsat 1997–2025 values to bi-monthly (Consoli et al.\, 202 4). In the current approach (Landsat ARD global mosaics V2 monthly) develo ped a 4–step process to derive improved quality mosaics: (1) first\, we aggregate monthly reflectances across the whole time-frame (cca 30 years) and use these normalized values to detect outliers\, (2) we then derive mo nthly median values with filtered reflectances (already significantly redu ces clouds\, snow and noise)\, (3) we then gap-fill values using convoluti onal filter and consistent land cover classes\, and (4) we finally gap-fil l all remaining values using modeling. For these steps we use a data fusio n approach with annual ensemble land cover data at 30 m\, together with MO DIS EVI monthly (complete\, consistent) and geometric temperature (a funct ion of latitude and day of the year) as covariate layers to help improve g ap-filling. Although using embeddings seems to also solve the issues of cl ouds\, snow and noise\, the advantage of having monthly mosaics is that th ey are easier to interpret and trace back potential errors and artifacts. The resulting monthly global cloud free mosaics are then consistent\, gap- free\, should contain minimum artifacts and can be used directly for model ing and a diversity of land monitoring applications (from above-ground bio mass\, vegetation height\, yield and soil property mapping). We will prese nt some initial results and discuss how we could combine forces to make op en EO data reach more people\, enable more applications and save more live s.\n\n\n*Bio*\n\nTom has more than 25 years of experience as an environmen tal modeler\, data scientist and spatial analyst. Tom has a background in soil mapping and geo-information science (PhD at Wageningen University / I TC). He continuously runs hands-on-R training courses to promote use of Op en Source software for spatial analysis / spatial modeling purposes.\n\nHe is currently the project leader of the Open-Earth-Monitor project (https: //doi.org/10.3030/101059548) and Director at the OpenGeoHub foundation. To m is recipient of the Clarivate Highly Cited Researchers for 2021\, 2022\, 2023\, 2024 and 2025. Several of his paper have received the best paper a wards including the "Finding the right pixel size" (https://doi.org/10.101 6/j.cageo.2005.11.008)\, "Soil property and class maps of the conterminous USA" (https://doi.org/10.2136/sssaj2017.04.0122)\, his articles published in PeerJ are among top 10 most cited of all time\; his PLOS One paper (ht tps://doi.org/10.1371/journal.pone.0169748) is listed among the most cited in the field.\n\n LOCATION:Room GS15 at the William Gates Building and on Zoom: https://cl-c am-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&\;fr om=addon END:VEVENT END:VCALENDAR