ArticleSensors (Basel, Switzerland)2025
Estimating Soil Arsenic Contamination by Integrating Hyperspectral and Geochemical Data with PCA and Optimizing Inversion Models.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Soil arsenic (As) contamination presents serious threats to ecosystems and human health, necessitating the development of accurate and efficient monitoring techniques. This study introduces a novel multi-source data fusion approach to enhance the hyperspectral inversion of soil arsenic concentrations by integrating dimensionality-reduced spectral data with soil components significantly correlated with arsenic (e.g., Cd, Cr, Cu, Ni, Pb, Zn, S, and total Fe
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