Wide and deep network-based spectral fusion of LIBS–NIRS for enhancing soil organic carbon estimation accuracy

Abstract

Rapid and accurate soil organic carbon (SOC) estimation is essential for assessing soil fertility and health. Although near-infrared spectroscopy (NIRS) has demonstrated tremendous application potential in SOC estimation, its inherent characteristics of weak absorption signals and overlapping spectral bands limit prediction accuracy and reliability. Here, we propose a spectral fusion technique that enhances estimation accuracy by integrating elemental information from laser-induced breakdown spectroscopy (LIBS) with molecular information from NIRS. We designed a Wide and Deep (W&D) dual-branch spectral fusion network for LIBS and NIRS information integration: the wide branch processes LIBS elemental features through a shallow linear layer, the deep branch extracts NIRS molecular features through a convolutional neural network, and both branches are collaboratively trained to achieve complementary feature fusion. Experimental results demonstrate that element–molecule feature fusion significantly improves SOC prediction accuracy. Compared to the optimal single-spectrum model, the W&D model reduces prediction errors by 0.168 g kg⁻¹ (29.07%) and 0.187 g kg⁻¹ (29.00%) on the test and external test sets, respectively. SHAP interpretability analysis reveals that effectively integrating elemental features (C, CN, and K) from LIBS with functional group features (C–H and O–H) from NIRS is the key driver of accuracy improvement. This study provides a novel solution for accurate SOC estimation and highlights the tremendous potential of spectral fusion techniques in soil property sensing.