A partitioned conditioned Latin hypercube sampling method considering spatial heterogeneity in digital soil mapping
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| Pubblicato in: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 12851 |
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Nature Publishing Group
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| Accesso online: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-025-95631-5 |2 doi | |
| 035 | |a 3190032071 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 245 | 1 | |a A partitioned conditioned Latin hypercube sampling method considering spatial heterogeneity in digital soil mapping | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The design of sampling methods is crucial in digital soil mapping for soil organic carbon (SOC), as it directly affects prediction precision and reliability. While sampling methods based on environmental variables are widely used, the spatial heterogeneity of soil properties poses challenges by introducing variability in influential driving factors across subregions, potentially reducing prediction accuracy. To address this, a partitioned conditioned Latin hypercube sampling (PcLHS) method explicitly considering spatial heterogeneity is proposed. PcLHS first employs the regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) method to partition the study area into relatively homogeneous subregions. Key environmental variables are then identified using the Boruta and the Variance Inflation Factor method, followed by conditioned Latin hypercube sampling (cLHS) to select training points within each subregion. Finally, the selected training points are combined to form the complete training dataset. A case study on SOC sampling in northeastern France demonstrated that PcLHS consistently outperformed traditional sampling methods, achieving lower root mean square error (RMSE, 0.40–0.43), higher coefficient of determination (R2, 0.36–0.44), and improved concordance correlation coefficient (CCC, 0.58–0.63). Compared to other methods, PcLHS reduced RMSE by 4–11%, increased R2 by 18–46%, and improved CCC by 14–29%. These results highlight the necessity of considering spatial heterogeneity in soil sampling design and establish PcLHS as an effective method for SOC prediction in heterogeneous landscapes. | |
| 653 | |a Organic carbon | ||
| 653 | |a Heterogeneity | ||
| 653 | |a Predictions | ||
| 653 | |a Spatial heterogeneity | ||
| 653 | |a Sampling methods | ||
| 653 | |a Mapping | ||
| 653 | |a Soil properties | ||
| 653 | |a Methods | ||
| 653 | |a Training | ||
| 653 | |a Sampling | ||
| 653 | |a Correlation coefficient | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 12851 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3190032071/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3190032071/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |