[article] | Titre : |
A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT |
| Type de document : |
Article/Communication |
| Auteurs : |
Shengli Tao, Auteur ; Zurui Ao, Auteur ; Jean-Pierre Wigneron, Auteur ; Sassan Saatchi, Auteur ; Philippe Ciais, Auteur ; Jérôme Chave, Auteur ; Thuy Le Toan, Auteur ; Pierre-Louis Frison  , Auteur ; et al., Auteur |
| Année de publication : |
2023 |
| Article en page(s) : |
pp 1577 - 1596 |
| Note générale : |
bibliographie
Data description paper |
| Langues : |
Anglais (eng) |
| Descripteur : |
[Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] bande Ku
[Termes IGN] fusion de données
[Termes IGN] image radar moirée
[Termes IGN] régression
[Termes IGN] série temporelle
|
| Résumé : |
(auteur) Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions. |
| Numéro de notice : |
A2023-097 |
| Affiliation des auteurs : |
UGE-LASTIG+Ext (2020- ) |
| Thématique : |
IMAGERIE |
| Nature : |
Article |
| nature-HAL : |
ArtAvecCL-RevueIntern |
| DOI : |
10.5194/essd-15-1577-2023 |
| Date de publication en ligne : |
12/04/2023 |
| En ligne : |
https://doi.org/10.5194/essd-15-1577-2023 |
| Format de la ressource électronique : |
URL article |
| Permalink : |
https://documentation.ensg.eu/index.php?lvl=notice_display&id=103215 |
in Earth System Science Data > vol 15 n° 4 (2023) . - pp 1577 - 1596
[article]
|  |
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externesA global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT in Earth System Science Data, vol 15 n° 4 (2023)
Deep learning for wildfire progression monitoring using SAR and optical satellite image time series (2021)
