Comparison of Machine Learning and Geostatistical Methods on Mapping Soil Organic Carbon Density in Regional Croplands and Visualizing Its Location‐Specific Dominators via Interpretable Model DOI Open Access
Bifeng Hu,

Yanhong Geng,

Yi Lin

и другие.

Land Degradation and Development, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

ABSTRACT High‐precision soil organic carbon density (SOCD) map is significant for understanding ecosystem cycles and estimating storage. However, the current mapping methods are difficult to balance accuracy interpretability, which brings great challenges of SOCD. In present research, a total 6223 samples were collected, along with data pertaining 30 environmental covariates, from agricultural land located in Poyang Lake Plain Jiangxi Province, southern China. Furthermore, ordinary kriging (OK), geographically weighted regression (GWR), random forest (RF), empirical Bayesian (EBK), three hybrid models (RF‐OK, RF‐EBK, RF‐GWR), constructed. These used SOCD (soil density) study region high resolution m. After that, shapley additive explanations (SHAP) quantify global contribution spatially identify dominant factors that influence variation. The outcomes suggested compared single geostatistics model model, RF method emerged as most effective predictive showcasing superior performance (coefficient determination ( R 2 ) = 0.44, root mean squared error (RMSE) 0.61 kg m −2 , Lin's concordance coefficient (LCCC) 0.58). Using SHAP, we found properties contributed prediction (81.67%). At pixel level, nitrogen dominated 50.33% farmland, followed by parent material (8.11%), available silicon (8.00%), annual precipitation (5.71%), remaining variables accounted less than 5.50%. summary, our offered valuable enlightenment toward achieving between interpretability digital mapping, deepened spatial variation farmland

Язык: Английский

Comparison of Machine Learning and Geostatistical Methods on Mapping Soil Organic Carbon Density in Regional Croplands and Visualizing Its Location‐Specific Dominators via Interpretable Model DOI Open Access
Bifeng Hu,

Yanhong Geng,

Yi Lin

и другие.

Land Degradation and Development, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

ABSTRACT High‐precision soil organic carbon density (SOCD) map is significant for understanding ecosystem cycles and estimating storage. However, the current mapping methods are difficult to balance accuracy interpretability, which brings great challenges of SOCD. In present research, a total 6223 samples were collected, along with data pertaining 30 environmental covariates, from agricultural land located in Poyang Lake Plain Jiangxi Province, southern China. Furthermore, ordinary kriging (OK), geographically weighted regression (GWR), random forest (RF), empirical Bayesian (EBK), three hybrid models (RF‐OK, RF‐EBK, RF‐GWR), constructed. These used SOCD (soil density) study region high resolution m. After that, shapley additive explanations (SHAP) quantify global contribution spatially identify dominant factors that influence variation. The outcomes suggested compared single geostatistics model model, RF method emerged as most effective predictive showcasing superior performance (coefficient determination ( R 2 ) = 0.44, root mean squared error (RMSE) 0.61 kg m −2 , Lin's concordance coefficient (LCCC) 0.58). Using SHAP, we found properties contributed prediction (81.67%). At pixel level, nitrogen dominated 50.33% farmland, followed by parent material (8.11%), available silicon (8.00%), annual precipitation (5.71%), remaining variables accounted less than 5.50%. summary, our offered valuable enlightenment toward achieving between interpretability digital mapping, deepened spatial variation farmland

Язык: Английский

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