Mapping the soil C:N ratio at the European scale by combining multi-year Sentinel radar and optical data via cloud computing

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.