Enhancing the Estimation and Mapping of Soil Cadmium by Using Geospatial Information-Guided Machine Learning and Principal Component Spectra

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Integrating machine learning with spectral data provides an effective and cost-efficient scheme for estimating cadmium (Cd) contents in soils compared with labor-intensive laboratory analyses. However, conventional machine learning–based spectral estimation methods often yield unsatisfactory performance because they rely on an unrealistic assumption that the functional relationships across geographically distinct subregions are homogeneous, thereby reducing the accuracy and stability of soil Cd estimation. To address this issue, a geospatial information-guided XGBoost (GIGS) model was developed, in which a spatial weighting module was incorporated into the XGBoost framework to account for spatial heterogeneity in functional relationships among sampling locations. The spatial heterogeneity of each soil spectral sample was quantified and assigned a corresponding spatial weight, which was subsequently incorporated during model calibration. The optimal principal component spectra (PCS) derived from spectral data were used as model inputs, with measured soil Cd content as the dependent variable, thereby establishing a robust spectral estimation model for soil Cd. Results indicated that the GIGS model exhibited satisfactory performance in the spectral estimation of soil Cd content, with R2, RMSE, and RPIQ values of 0.78, 0.04, and 2.02, respectively. Compared to commonly used spectral estimation models (e.g., XGBoost, random forest, support vector regression), the GIGS model achieved a maximum performance improvement of approximately 27.87% and a minimum improvement of approximately 16.42% (with reference to the R2 value). Five PCS of soil spectral data were extracted as predictors for the model. PCS-1 was found to be closely associated with iron oxides, while PCS-2 primarily reflects the spectral characteristics of clay minerals. The spectral bands at 600 nm and 815 nm contribute most strongly to PCS-3, which is linked to soil organic matter and indirectly reflects the soil Cd status. PCS-4 and PCS-5 represent mixed spectral information derived from materials associated with soil Cd. An integrated framework combining the GIGS model and PCS data developed in this study provides an accurate and reliable tool for spectral estimation and mapping of soil Cd, thereby supporting cost-effective soil management and environmental sustainability worldwide.

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