土壤湿度对近红外光谱反演剖面有机质精度的影响

Influence of soil moisture on the inversion accuracy of near-infrared spectra of organic matter

为深入分析土壤湿度对近红外光谱反演剖面土壤有机质(soil organic matter, SOM)精度的影响,该研究依据水分张力这一指标,将土壤划分为风干状态、1.500、0.330、0.100、0.033 MPa和饱和状态共6种湿度水平,在所选16个地点分别采集深度约150 cm剖面土壤芯柱为研究对象,采用7种方法对所测剖面土壤光谱吸光度进行光谱预处理,选择较佳的预处理方法。同时,采用连续投影算法(successive projection algorithm, SPA)和竞争性自适应重加权-连续投影算法(competitive adaptive reweighting-successive projection algorithm, CARS-SPA)筛选特征波长。构建基于全谱及特征波长的SOM近红外光谱反演模型,并将其与标准正态变量变化(standard normal variate, SNV)预处理方法相结合。结果表明:1)SPA-PLSR模型和CARS-SPA-PLSR模型的精度均优于基于全谱的PLSR模型;2)SNV-SPA-PLSR模型在饱和、风干状态下预测效果更好,而SNV-CARS-SPA-PLSR模型在水分张力分别为0.033、0.100、0.330和1.500 MPa时预测精度更高;3)不同土壤湿度水平近红外光谱“一对一”式预测SOM模型难以满足实际应用,经过对比研究,选用水分张力为1.500 MPa时构建的SNV-CARS-SPA-PLSR模型分别预测6组土壤湿度水平和混合样本集中SOM取得效果最好。该研究结果对各湿度水平下估算SOM含量有一定的指导作用,并为提高不同土壤湿度水平间剖面SOM近红外光谱反演模型的适用性提供参考。

Soil organic matter(SOM)is one of the essential components of soil and moisture to interfere with the detection.The soil moisture content by mass ratio and spectral parameters are often utilized to classify the moisture levels.But previous studies on the prediction of SOM content have focused mostly on the surface soil without covering the profile of depth range.This study aims to explore the influence of soil moisture on the inversion accuracy of the SOM profile.This study analyzed previously obtained near-infrared spectra were collected from the samples of the soil core column in the surface layer to about 150 cm underground.Each core was divided into subsamples with a height of 10 or 20 cm.The samples were gradually moistened to 6 groups of levels of soil moisture.According to the index of moisture tension,the air-dry state was defined as 1.500,0.330,0.100,0.033 MPa,and the saturated state in turn.The data was normalized and transformed to the absorbance.Each set of spectral data was processed by seven spectral preprocessing.Among them,the standard normal variate transformation(SNV)was achieved the best.Meanwhile,successive projection algorithms(SPA)and competitive adaptive reweighting-successive projection algorithms(CARS-SPA)were used to screen the characteristic wavelengths.The number of characteristic wavelengths was then reduced to 6-8 at each level of soil moisture.The inversion models of SOM were constructed to combine with SNV preprocessing using full spectrum and characteristic wavelengths.The results indicated that:1)The model accuracies of the SPA-PLSR and CARS-SPA-PLSR models were better than that of the PLSR model at six levels of moisture.2)The SNV preprocessing was also combined to determine the coefficient of determination in prediction(R2p)and root mean square error of the prediction set(RMSEP).Under the saturated state,R2p values of the SNV-SPA-PLSR and SNV-CARS-SPA-PLSR models were 0.664 and 0.651,respectively,while the RMSEP values were 1.095 and 1.131 g/kg,respectively.In the air-dry state,R2p values of the two models were 0.799 and 0.753,respectively,and RMSEP values were 0.759 and 0.848 g/kg,respectively,indicating the better prediction of the SNV-SPA-PLSR model.The SNV-CARS-SPA-PLSR model shared the higher accuracy of prediction when the moisture tension levels were 0.033,0.100,0.330,and 1.500 MPa.Specifically,R2p values increased from 0.699 to 0.846,whereas,the RMSEP values decreased from 1.013 to 0.620 g/kg.3)However,it was difficult to guarantee the same soil moisture level at the same depth in different locations of the field.The SOM calibration model was applied to the characteristic wavelengths on different datasets.The SNV-CARS-SPA-PLSR model was selected at the moisture tension of 1.500 MPa.The best performance was achieved for the organic matter in the six groups of soil moisture levels and mixed samples.The models can be expected to estimate the SOM content of the profile at various moisture levels.The findings can also provide a strong reference to improve the applicability of near-infrared spectra inversion models for the organic matter content at different levels of soil moisture.