基于不同光谱变换的土壤盐含量光谱特征分析

发布时间：2018-11-20 09:13

【摘要】：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。
[Abstract]:The microorganism remediation experiment of primary saline soil was followed. The soil salt content and spectral data were measured simultaneously. The spectral characteristics of saline soil were analyzed in detail on the basis of 34 spectral transformations. For the selected six spectral transformations, the spectral inversion PLSR (partial least squares regression) model of soil salt content was established by using the full band (400 ~ 1650 nm) and the best sensitive band. The results show that the correlation between the soil salt content and the spectral reflection data after smoothing is obviously enhanced, and the best sensitive band range is further focused. In this study, the optimal spectral transformation was obtained as derivative transformation, and the SGSD transform was the best model for predicting soil salt content based on the whole band. Compared with the original spectrum, the rmms of the model was improved from 0.537 and 1.928 to 0.823 and 1.256 respectively. SGSD (Log R) is an effective spectral transformation method for salt content prediction model based on optimal band. This study provides a method and data reference for further rapid quantitative analysis of salt content in saline soil.
【作者单位】：上海交通大学农业与生物学院和低碳农业研究中心;国家林业局上海城市森林生态系统国家定位观测研究站;都市农业(南方)重点实验室;
【基金】：高分国土资源遥感应用示范系统(一期)项目(04-Y30B01-9001-12/15) 国家自然基金(41471120) 社科重大项目(14ZDB139) 上海交大农工交叉项目(Agri-X2015004)资助
【分类号】：S156.4

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