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跟踪初生盐渍土壤的微生物修复实验,采用同步实测得土壤盐含量和光谱数据,详细分析了基于34种光谱变换,修复过程中盐渍土的光谱特征。对于选取的6种光谱变换,采用全波段(400~1650 nm)和分析获得的最佳敏感波段分别建立了土壤盐含量的光谱反演PLSR(partial least squares regression)模型。研究表明,光谱变换处理使土壤盐含量与平滑后的光谱反射数据的相关性明显增强,且最佳敏感波段范围进一步聚焦。本研究得到最佳光谱变换为导数变换,基于全波段的土壤盐含量预测模型以SGSD变换效果最好,与原始光谱相比,模型的r、RMSEP分别从0.537和1.928改善到0.823和1.256。而SGSD(Log R)是基于最佳波段所建立的盐含量预测模型的有效光谱变换方法,该研究为进一步实现盐渍土中盐含量快速定量分析提供了方法和数据参考。
Tracing the experiment of microbial remediation of primary saline soils, the soil salt content and spectral data were simultaneously measured. The spectral characteristics of saline soils were analyzed in detail based on 34 spectral transformations. For the six spectral transformations selected, the PLSR (partial least squares regression) model of soil salt content was established using the full band (400 ~ 1650 nm) and the optimal sensitivity band analyzed. The results show that the correlation between the soil salt content and the smoothed spectral reflectance data is significantly enhanced by the spectral transformation process, and the best sensitive wavelength range is further focused. In this study, the best spectral transformation was obtained as the derivative transformation. The soil salinity content prediction model based on the whole band was the best with SGSD. Compared with the original spectrum, the r and RMSEP improved from 0.537 and 1.928 to 0.823 and 1.256, respectively. The SGSD (Log R) is an effective spectral transformation method based on the salt content prediction model established in the best band. This study provides a method and data reference for further rapid quantitative analysis of salt content in saline soils.