| Title: | CRITER 1.0 : a coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data |
|---|
| Authors: | ID Zupančič Muc, Matjaž (Author)
ID Zavrtanik, Vitjan (Author)
ID Barth, Alexander (Author)
ID Alvera-Azcárate, Aida (Author)
ID Ličer, Matjaž (Author)
ID Kristan, Matej (Author) |
|---|
| Files: |  URL - Source URL, visit https://doi.org/10.5194/gmd-18-5549-2025
 PDF - Presentation file, download (19,22 MB)
MD5: 27AD5E606B800A43285364843F1C304B
|
|---|
| Language: | English |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: |  NIB - National Institute of Biology
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | deep learning, reconstruction algorithms, satellite measurements |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Publication date: | 04.09.2025 |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 5549–5573 |
|---|
| Numbering: | Vol. 18, iss. 17 |
|---|
| PID: | 20.500.12556/DiRROS-23863  |
|---|
| UDC: | 004.8 |
|---|
| ISSN on article: | 1991-9603 |
|---|
| DOI: | 10.5194/gmd-18-5549-2025 |
|---|
| COBISS.SI-ID: | 252344067 |
|---|
| Note: | Soavtorji: Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan;
|
|---|
| Publication date in DiRROS: | 14.10.2025 |
|---|
| Views: | 251 |
|---|
| Downloads: | 122 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
