2026-10-08, 16:45–17:30 (Europe/Amsterdam), Rooms 12+14
One of the most significant deliverables of the OEMC project are global, cloud-less Landsat monthly time series from 2000–2025 at 30 m resolution. The Landsat global mosaics (V1) are explained in detail in Consoli et al. (2025; https://peerj.com/articles/18585/). The Landsat V2 is at the order of magnitude more ambitious aiming at monthly products in 16bit format and will significantly less artifacts. The pipeline uses a four-step process for improved quality, including gap-filling using spatial and temporal neighbours, data fusion and final gap filling using global models. The results of cross-validation show improvements in accuracy in consistency. Major project challenges include needing 1PB of storage and securing post-2025 commercial services. Landsat V2 can also be used to derive embeddings for 2000-2025.
Landsat bimonthly is available via https://browser.stac.dataspace.copernicus.eu/collections/opengeohub-landsat-bimonthly-mosaic-v1.0.1?.language=en
Open-Earth-Monitor Cyberinfrastructure (Grant agreement ID: 101059548)
Tom has more than 25 years of experience as an environmental modeler, data scientist and spatial analyst. Tom has a background in soil mapping and geo-information science (PhD at Wageningen University / ITC). He continuously runs hands-on-R training courses to promote use of Open Source software for spatial analysis / spatial modeling purposes. He is currently the project leader of the Open-Earth-Monitor project (https://doi.org/10.3030/101059548) and Director at the OpenGeoHub foundation. Tom is recipient of the Clarivate Highly Cited Researchers for 2021, 2022, 2023, 2024 and 2025. Several of his paper have received the best paper awards including the "Finding the right pixel size" (https://doi.org/10.1016/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 (https://doi.org/10.1371/journal.pone.0169748) is listed among the most cited in the field.
Sajed Sarabandi is a software engineer and junior researcher specializing in remote sensing at the OpenGeoHub Foundation. He holds a Master’s degree in Computer Science from Leiden University.