Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.5194/soil-8-587-2022 |
Licence  |
|
| Title (Primary) | Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms |
| Author | Sakhaee, A.; Gebauer, A.; Ließ, M.; Don, A. |
| Source Titel | Soil |
| Year | 2022 |
| Department | BOSYS |
| Volume | 8 |
| Issue | 2 |
| Page From | 587 |
| Page To | 604 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.3220/DATA20200203151139 |
| Supplements | https://soil.copernicus.org/articles/8/587/2022/soil-8-587-2022-supplement.pdf |
| Abstract | As the largest terrestrial carbon pool, soil organic carbon (SOC) has the potential to influence and mitigate climate change; thus, SOC monitoring is of high importance in the frameworks of various international treaties. Therefore, high-resolution SOC maps are required. Machine learning (ML) offers new opportunities to develop these maps due to its ability to data mine large datasets. The aim of this study was to apply three algorithms commonly used in digital soil mapping – random forest (RF), boosted regression trees (BRT), and support vector machine for regression (SVR) – on the first German agricultural soil inventory to model the agricultural topsoil (0–30 cm) SOC content and develop a two-model approach to address the high variability in SOC in German agricultural soils. Model performance is often limited by the size and quality of the soil dataset available for calibration and validation. Therefore, the impact of enlarging the training dataset was tested by including data from the European Land Use/Cover Area frame Survey for agricultural sites in Germany. Nested cross-validation was implemented for model evaluation and parameter tuning. Grid search and the differential evolution algorithm were also applied to ensure that each algorithm was appropriately tuned . The SOC content of the German agricultural soil inventory was highly variable, ranging from 4 to 480 g kg−1. However, only 4 % of all soils contained more than 87 g kg−1 SOC and were considered organic or degraded organic soils. The results showed that SVR produced the best performance, with a root-mean-square error (RMSE) of 32 g kg−1 when the algorithms were trained on the full dataset. However, the average RMSE of all algorithms decreased by 34 % when mineral and organic soils were modelled separately, with the best result from SVR presenting an RMSE of 21 g kg−1. The model performance was enhanced by up to 1 % for mineral soils and by up to 2 % for organic soils. Despite the ability of machine learning algorithms, in general, and SVR, in particular, to model SOC on a national scale, the study showed that the most important aspect for improving the model performance was to separate the modelling of mineral and organic soils. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=26660 |
| Sakhaee, A., Gebauer, A., Ließ, M., Don, A. (2022): Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms Soil 8 (2), 587 - 604 10.5194/soil-8-587-2022 |
|