Spectral techniques such as visible-to-near-infrared (VIS–NIR) and mid-infrared (MIR) spectroscopies have been regarded as effective alternatives to laboratory-based methods for determining soil organic carbon (SOC). Research to explore the potential of the fusion of VIS–NIR and MIR absorbance for improving SOC prediction is needed, since each individual spectral range may not contain sufficient information to yield reasonable estimation accuracy. Here, we investigated two data fusion strategies that differed in input data, including direct concatenation of full-spectral absorbance and concatenation of selected predictors by optimal band combination (OBC) algorithm. Specifically, continuous wavelet transform (CWT) was adopted to optimize the spectral data before and after data fusion. Prediction models for SOC were developed using partial least squares regression. Results demonstrated that estimations for SOC using MIR absorbance (i.e., validation R² = 0.45–0.64) generally outperformed those using VIS–NIR (i.e., validation R² = 0.20–0.44). Compared to the raw absorbance counterparts, CWT decomposing could improve the prediction accuracy for SOC, for both the individual absorbance and the fusion of VIS–NIR and MIR absorbance. Among all the models investigated, the combinational use of VIS–NIR and MIR using OBC fusion at CWT scale of 1 yielded the optimal prediction, providing the highest validation R² of 0.66. This model with 10 selected spectral parameters as input is of small total data volume, large processing speed and efficiency, confirming the potential of OBC in fusing both types of spectral data. In summary, CWT decomposing and OBC strategy are powerful algorithms in analyzing the spectral data, and allow the VIS–NIR and MIR spectral fusion models to improve the SOC estimation.

可见光至近红外 (VIS-NIR) 和中红外 (MIR) 光谱等光谱技术已被认为是基于实验室的测定土壤有机碳 (SOC) 方法的有效替代方法。需要研究探索 VIS-NIR 和 MIR 吸光度融合改善 SOC 预测的潜力，因为每个单独的光谱范围可能不包含足够的信息来产生合理的估计精度。在这里，我们研究了输入数据不同的两种数据融合策略，包括全光谱吸光度的直接串联和通过最佳波段组合 (OBC) 算法选择的预测因子的串联。具体而言，采用连续小波变换（CWT）对数据融合前后的光谱数据进行优化。SOC 的预测模型是使用偏最小二乘回归开发的。结果表明，使用 MIR 吸光度估算 SOC（即验证R ² = 0.45–0.64) 通常优于使用 VIS-NIR 的那些（即验证R ² = 0.20–0.44）。与原始吸光度对应物相比，CWT 分解可以提高 SOC 的预测精度，无论是单个吸光度还是 VIS-NIR 和 MIR 吸光度的融合。在所有研究的模型中，在 CWT 规模为 1 的情况下，使用 OBC 融合的 VIS-NIR 和 MIR 的组合使用产生了最佳预测，提供了最高的验证R ²0.66。该模型以 10 个选定的光谱参数作为输入，总数据量小，处理速度和效率高，证实了 OBC 在融合两种光谱数据方面的潜力。总之，CWT 分解和 OBC 策略是分析光谱数据的强大算法，并且允许 VIS-NIR 和 MIR 光谱融合模型改进 SOC 估计。