基于可见光谱的不同质地土壤有机质快速测定

摘要

A total of 156 soil samples with different textures (sand soil (51) , clay soil (54) and land soil (51)) were collected, and the spectra of all soil samples were scanned with spectrophotometer ( ASD FieldSpec3) from 325 to 2 500 nm. Orthogonal signal correction ( OSC ) was applied to eliminate the influence of the textures. Soil organic matter (SOM) prediction models of different textural soil samples were then obtained by using partial least square analysis (PLS) and OSC - PLS. The result showed that when the calibration sample was clay and land soil, the correlation coefficients of PLS and OSC - PLS model were 0. 809 and 0. 823; when the calibration sample was sand and land soil, the correlation coefficients were 0.837 and 0.734; and when the calibration sample was clay and sand soil, the correlation coefficients were 0. 887 and 0. 823 , respectively. SOM content of another textural soil samples were predicted by using above models, the result showed that the predictive correlation coefficients of PLS and OSC - PLS to sand soil were 0. 572 and 0. 864; to clay soil were 0. 555 and 0. 540; and to land soil were 0. 643 and 0. 721 , respectively. The results indicate that OSC can eliminate the influence of texture and improve the prediction precision and solidity of the model.%在可见光区域内对不同质地土壤(粘土、砂土、壤土)共156个样本的光谱特性进行了研究,并建立了不同质地土壤间有机质含量的互测模型.为了消除土壤质地对有机质含量预测的影响,引入了正交信号处理( OSC)谱图预处理方法.结果表明:粘土和壤土作为建模样本建立的土壤有机质偏最小二乘(PLS)和OSC-PLS校正模型的相关系数分别为0.809和0.823;砂土和壤土分别为0.837和0.734;粘土和砂土相应值分别为0.887和0.823.采用上述模型对另一质地土壤有机质含量进行预测,砂土的相关系数分别为0.572和0.864;粘土的相应值分别为0.555和0.540;壤土的相应值分别为0.643和0.721.预测效果说明OSC预处理可提高不同质地间土壤有机质的互预测能力.