20.500.12556/DiRROS-23863 CRITER 1.0 a coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data Satellite observations of sea surface temperature (SST) are essential for accurate weather forecasting and climate modeling. However, these data often suffer from incomplete coverage due to cloud obstruction and limited satellite swath width, which requires development of dense reconstruction algorithms. The current state of the art struggles to accurately recover high-frequency variability, particularly in SST gradients in ocean fronts, eddies, and filaments, which are crucial for downstream processing and predictive tasks. To address this challenge, we propose a novel two-stage method CRITER (Coarse Reconstruction with ITerative Refinement Network), which consists of two stages. First, it reconstructs low-frequency SST components utilizing a Vision Transformer-based model, leveraging global spatio-temporal correlations in the available observations. Second, a UNet type of network iteratively refines the estimate by recovering high-frequency details. Extensive analysis on datasets from the Mediterranean, Adriatic, and Atlantic seas demonstrates CRITER's superior performance over the current state of the art. Specifically, CRITER achieves up to 44 % lower reconstruction errors of the missing values and over 80 % lower reconstruction errors of the observed values compared to the state of the art. deep learning reconstruction algorithms satellite measurements globoko učenje rekonstrukcijski algoritmi satelitske meritve true false true Angleški jezik Slovenski jezik Neznano 2025-10-14 07:48:42 2025-10-14 07:48:43 2025-10-24 03:55:11 0000-00-00 00:00:00 2025 0 0 Soavtorji: Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan; str. 5549–5573 iss. 17 Vol. 18 Sept. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2025-09-04 004.8 1991-9603 10.5194/gmd-18-5549-2025 252344067 https://doi.org/10.5194/gmd-18-5549-2025 1 73a66cc4-a8c1-11f0-9bb7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=35391 RAZ_Zupancic_Muc_Matjaz_2025.pdf RAZ_Zupancic_Muc_Matjaz_2025.pdf 1 27AD5E606B800A43285364843F1C304B bb8235c488be01a98dace57a212a83c38fe50002620ecc0100a1b8331bf3b875 84900446-a8c1-11f0-9bb7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=35392 Nacionalni inštitut za biologijo 0 0 0