Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.3389/fsoil.2021.770326 |
Licence  |
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| Title (Primary) | Topsoil texture regionalization for agricultural soils in Germany – an iterative approach to advance model interpretation |
| Author | Gebauer, A.; Sakhaee, A.; Don, A.; Poggio, M.; Ließ, M. |
| Source Titel | Frontiers in Soil Science |
| Year | 2022 |
| Department | BOSYS |
| Volume | 1 |
| Page From | art. 770326 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.17605/OSF.IO/9DGB6 |
| Keywords | Soil texture; Predictive soil mapping; Pedometrics; machine learning; optimization |
| Abstract | Site-specific spatially continuous soil texture data is required for
many purposes such as the simulation of carbon dynamics, the estimation
of drought impact on agriculture, or the modeling of water erosion
rates. At large scales, there are often only conventional polygon-based
soil texture maps, which are hardly reproducible, contain abrupt changes
at polygon borders, and therefore are not suitable for most
quantitative applications. Digital soil mapping methods can provide the
required soil texture information in form of reproducible site-specific
predictions with associated uncertainties. Machine learning models were trained in a nested cross-validation approach to predict the spatial distribution of the topsoil (0 to 30 cm) clay, silt, and sand contents in 100 m resolution. The differential evolution algorithm was applied to optimize the model parameters. High-quality nation-wide soil texture data of 2991 soil profiles was obtained from the first German agricultural soil inventory. We tested an iterative approach by training models on predictor datasets of increasing size, which contained up to 50 variables. The best results were achieved when training the models on the complete predictor dataset. They explained about 59% of the variance in clay, 75% of the variance in silt, and 77% of the variance in sand content. The RMSE values ranged between approximately 8.2 wt.% (clay), 11.8 wt.% (silt), and 15.0 wt.% (sand). Due to their high performance, models were able to predict the spatial texture distribution. They captured the high importance of the soil forming factors parent material and relief. Our results demonstrate the high predictive power of machine learning in predicting soil texture at large scales. The iterative approach enhanced model interpretability. It revealed that the incorporated soil maps partly substituted the relief and parent material predictors. Overall, the spatially continuous soil texture predictions provide valuable input for many quantitative applications on agricultural topsoils in Germany. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=25445 |
| Gebauer, A., Sakhaee, A., Don, A., Poggio, M., Ließ, M. (2022): Topsoil texture regionalization for agricultural soils in Germany – an iterative approach to advance model interpretation Front. Soil Sci. 1 , art. 770326 10.3389/fsoil.2021.770326 |
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