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• W4200400075 endingPage "115648" @default.
• W4200400075 startingPage "115648" @default.
• W4200400075 abstract "• 3D spectral model developed to fingerprint loess soils using principal component analysis. • Sensor fusion improves the performance of machine-learning-based models. • Spectral modeling provides alternative to mineral/geochemical pedogenic proxies. • Loess deposits from various climatic zones were influenced by the Asian monsoons. • Spectral modeling facilitates large-scale investigation of environmental changes. Loess deposits are important records of the evolution of the soil environment and pedogenic weathering. Changes in pedogenic weathering conditions through space and time, as well as discrimination/sourcing of loess-derived soils, are important scientific issues in the soil (paleosol) community. Here, 502 soil samples from four loess chronosequences representing different climatic zones of China were investigated using portable X-ray fluorescence (PXRF) and visible to near-infrared reflectance (VNIR) in combination with previously published mineralogic and magnetic datasets. A spectral modeling approach was employed to discriminate loess-derived soils from different regions of China. We developed a three-dimensional model to fingerprint loess-derived soils using principal component analysis (PCA), showing that soils accumulating under varying climatic conditions were effectively discriminated with sensor fusion data. Six key soil mineralogic and magnetic attributes serving as pedogenic-weathering proxies were analyzed and predicted with conventional methods and chemometric models. Predictive models were constructed with machine learning methods, including partial least squares regression (PLSR), random forest (RF), and Cubist algorithms. The results indicate that the Cubist algorithm works better than PLSR and RF in predicting pedogenic-weathering proxies. The cross-validation results indicate that, although models derived from single sensors (i.e., PXRF or VNIR) work well in predicting pedogenic-weathering proxies, the sensor fusion approach is superior with regard to accuracy and robustness of results in most cases. We suggest that the combined elemental and secondary-mineral information provided by the fused PXRF-VNIR datasets can yield high-accuracy models in soil (paleosol) investigations. The sensor fusion models reveal that pedogenic processes in the loess chronosequences are diversified both spatially and temporally in different climate zones of China. Our results suggest that spectral modeling can be an alternative to geochemical, mineralogic and magnetic pedogenic-weathering proxies, and that it has great potential for investigating soil-forming conditions and pedogenic-weathering evolution, especially when large-area and/or high-resolution analysis is required." @default.
• W4200400075 created "2021-12-31" @default.
• W4200400075 creator A5003360183 @default.
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• W4200400075 date "2022-03-01" @default.
• W4200400075 modified "2023-10-16" @default.
• W4200400075 title "Pedogenic-weathering evolution and soil discrimination by sensor fusion combined with machine-learning-based spectral modeling" @default.
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• W4200400075 doi "https://doi.org/10.1016/j.geoderma.2021.115648" @default.
• W4200400075 hasPublicationYear "2022" @default.
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