Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.still.2024.106311 |
| Title (Primary) | Mapping the soil C:N ratio at the European scale by combining multi-year Sentinel radar and optical data via cloud computing |
| Author | Wang, X.; Geng, Y.; Zhou, T.; Zhao, Y.; Li, H.; Liu, Y.; Li, H.; Ren, R.; Zhang, Y.; Xu, X.; Liu, T.; Si, B.; Lausch, A. |
| Source Titel | Soil & Tillage Research |
| Year | 2025 |
| Department | CLE |
| Volume | 245 |
| Page From | art. 106311 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Supplements | https://ars.els-cdn.com/content/image/1-s2.0-S016719872400312X-mmc1.docx |
| Keywords | Soil C:N ratio; Cloud computing; Sentinel; Soil mapping |
| Abstract | Spatial
information on the soil carbon-to-nitrogen (C:N) ratio is essential for
sustainable soil use and management. The unprecedented availability of
Sentinel optical and radar data on cloud computing platforms, such as
the Google Earth Engine (GEE), has created new possibilities for
developing soil prediction models from the local scale to the planetary
scale. However, there is a paucity of literature on the effects of
Sentinel sensor selection and integration and radar data utilization
strategies on mapping the C:N ratio. In this study, we explored the use
of multiyear Sentinel-1 radar and Sentinel-2 optical data obtained from
the GEE platform combined with the digital soil mapping (DSM) technique
to map the soil C:N ratio at the European scale. The performance of soil
prediction models, which were constructed using two modeling techniques
(random forest and support vector machine), derived under multiple
scenarios based on optical, radar and commonly used auxiliary data
(climatic and topographic variables) combined with the LUCAS 2018 soil
dataset, was evaluated by a cross-validation technique. The results
showed that the modeling performance varied with the selection and
integration of Sentinel observations, as well as the configuration of
the radar data. Models based on single polarization performed the worst
across all scenarios related to Sentinel-1, with cross-polarization
performing better than copolarization. Models that utilized Sentinel-1
data from ascending orbits outperformed those that utilized data from
descending orbits. The application of Sentinel-1 backscatter information
derived from different orbits and polarization modes resulted in
improved prediction accuracy. Our study also demonstrated the potential
of integrating multiyear Sentinel satellite data via the GEE to map the
continental-scale C:N ratio. The model based on Sentinel-1 data
outperformed the one built on Sentinel-2 data, whereas combining
Sentinel-2 optical data with Sentinel-1 radar data led to more accurate
predictions. The variable importance results indicated that optical data
and backscattering information from Sentinel observations are the most
important groups of variables for soil C:N ratio mapping compared to the
other variable groups (terrain and climate data). The digital soil maps
generated under the different scenarios exhibited detailed patterns
with significant spatial variation, with similar overall trends but
slightly different details. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29694 |
| Wang, X., Geng, Y., Zhou, T., Zhao, Y., Li, H., Liu, Y., Li, H., Ren, R., Zhang, Y., Xu, X., Liu, T., Si, B., Lausch, A. (2025): Mapping the soil C:N ratio at the European scale by combining multi-year Sentinel radar and optical data via cloud computing Soil Tillage Res. 245 , art. 106311 10.1016/j.still.2024.106311 |
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