Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1016/j.still.2025.107034 |
Lizenz  |
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| Titel (primär) | Multi-frequency SAR and optical data integration for continental-scale digital mapping of soil chemical properties across Europe |
| Autor | Zhou, T.; Geng, Y.; Li, H.; Zhang, H.; Li, H.; Liu, J.; Li, S.; Liu, T.; Pan, J.; Si, B.; Lausch, A. |
| Quelle | Soil & Tillage Research |
| Erscheinungsjahr | 2026 |
| Department | CLE |
| Band/Volume | 258 |
| Seite von | art. 107034 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Supplements | Supplement 1 |
| Keywords | digital soil mapping; cloud computing; multi-sensor; earth observation; machine learning |
| Abstract | Timely and accurate spatial information on soil properties is essential for addressing global challenges, including climate change, food security, and ecosystem degradation. Despite advances in digital soil mapping (DSM), current approaches remain limited by reliance on optical satellite data and insufficient exploration of synthetic aperture radar (SAR) potential at continental scales. Here, we advance DSM by integrating multi-frequency SAR and optical satellite observations to map four key soil chemical properties—soil organic carbon, pH, extractable potassium, and total nitrogen—across Europe. Eleven scenarios with different data integrations, combined with two machine learners (support vector machine and random forest algorithms) and measurements from the LUCAS 2018 soil module, were employed to construct prediction models. The results confirm that continental-scale DSM is feasible using long-term optical and SAR observations. For all soil properties, C-band Sentinel-1 outperformed L-band PALSAR-1/2, and the integration of multi-frequency SAR data achieved prediction accuracies comparable to or even exceeding those of optical data, with R² improvements of approximately 29 %–87 % compared with using only L-band backscatter bands. The joint use of radar and optical observations produced the best performance, improving predictions of all soil properties compared to using optical data alone, with R² values ranging approximately from 0.31 to 0.60—highest for soil pH and lowest for soil total nitrogen. The relative importance of SAR features in the predictions varied with specific polarization modes and band frequencies, and radar indices were found to be more influential in models than backscatter bands. The generated soil property maps showed spatial patterns consistent with previous efforts based on multi-source environmental data. This study demonstrates that multi-frequency SAR data can both substitute for and complement optical data in DSM, offering new insights and practical directions for future model development. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=31764 |
| Zhou, T., Geng, Y., Li, H., Zhang, H., Li, H., Liu, J., Li, S., Liu, T., Pan, J., Si, B., Lausch, A. (2026): Multi-frequency SAR and optical data integration for continental-scale digital mapping of soil chemical properties across Europe Soil Tillage Res. 258 , art. 107034 10.1016/j.still.2025.107034 |
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