Soil hyperspectral and soil organic carbon

Description

This study hypothesized that integrating multiscale wavelet decomposition (CWT/DWT) with machine learning (ML) enhances soil organic carbon (SOC) estimation in arid lakeside oases. Using 82 soil samples with VNIR spectra, CWT at scales 1-5 reduced noise by 19.21% vs DWT. CWT-1-CARS-RF achieved optimal accuracy (R²=0.79, RPD=2.23), with 49.04% R² and 58.23% RPD improvements via feature selection. Sensitive bands were 401-504 nm (visible) and 1638-2369 nm (NIR). Spatial validation showed 91.3% consistency, confirming robust SOC mapping via wavelet-ML synergy.

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