Szczegóły publikacji
Opis bibliograficzny
Harmonizing satellite thermal data with ground-based observations for climate long-term monitoring / Ewa GŁOWIENKA, Eva Savina Malinverni, Marsia Sanità, Krystyna MICHAŁOWSKA, Marcin KUCZA // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; ISSN 1682-1750. — 2025 — vol. XLVIII-M-7-2025, s. 127-132. — Bibliogr. s. 131-132, Abstr. — Publikacja dostępna online od: 2025-05-24. — 44th EARSeL symposium : 26–29 May 2025, Prague, Czech Republic / eds. Eva Matoušková, Lena Halounová
Autorzy (5)
- AGHGłowienka Ewa
- Malinverni Eva Savina
- Sanità Marsia
- AGHMichałowska Krystyna
- AGHKucza Marcin
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 160603 |
|---|---|
| Data dodania do BaDAP | 2025-06-24 |
| Tekst źródłowy | URL |
| DOI | 10.5194/isprs-archives-XLVIII-M-7-2025-127-2025 |
| Rok publikacji | 2025 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Abstract
Estimating land surface temperature (LST) in urban environments remains a complex task due to high surface heterogeneity and variations in material emissivity. This study compares LST products from four satellite sensors (Landsat 8 TIRS, ECOSTRESS, MODIS Terra, and Sentinel-3 SLSTR) with in-situ temperature measurements recorded by 27 ground-based sensors placed across diverse urban surface types in Kraków, Poland. To minimize cross-platform discrepancies, a harmonization workflow was applied, including spatial reprojection, temporal alignment within a ±15-minute window, and emissivity normalization. Residuals were assessed using standard validation metrics: RMSE, MAE, bias, and Pearson’s correlation coefficient. Among the examined datasets, Landsat 8 delivered the most consistent results (RMSE ≈ 7.7°C), while ECOSTRESS exhibited the highest positive bias and spatial variability. Residual errors were notably larger over vegetated areas, reflecting the effects of variable emissivity and canopy shading. Despite the observed errors, the harmonized dataset enables consistent comparisons between sensors and an effective thermal assessment of urban areas. The results highlight the methodological relevance of harmonization in thermal remote sensing, particularly in heterogeneous urban areas.
