基于离子光谱特征波段反射率的土壤碱化指标反演模型

以新疆奇台地区碱化土壤为研究对象,通过分析碱化土壤实测光谱反射率曲线与八大离子、pH、碱化指标相互间的相关关系,建立基于离子光谱特征波段反射率的各碱化指标一元及多元光谱反演模型,并对其精度进行验证。结果显示:Na^+、CO_3^2-、HCO_3^-含量与光谱反射率正相关,最高点的相关系数分别为0.710、0.798、0.749,而Ca²⁺、Mg²⁺含量与光谱反射率负相关,相关系数最高均不超过-0.370,反映出前3类离子含量与光谱反射率关系更为密切。SAR(钠吸附比)和ESP(碱化度)与Na+相关系数同为0.954,TA(总碱度)、RSC(残余碳酸钠)、pH与CO_3^2-的相关系数分别为0.946、0.949和0.953,总体上Na^+和CO_3^2-含量对各碱化指标的影响更大。各碱化指标与土壤光谱反射率的相关性TA>RSC>ESP>pH>SAR;其中TA与光谱反射率的相关系数达到0.863。碱化指标TA的离子光谱特征波段反射率反演模型精度最好,其R^2为0.703,比利用实测光谱反射率建立的pH反演模型的R^2高约14%,说明前者精度更高,能更好地反映研究区内土壤的碱化程度。利用离子光谱特征波段反射率实现对土壤碱化的预测会成为今后研究的重点。

Soil Alkalization Index Inversion Model Based on Spectral Reflectance of Ions Characteristics Band

The thesis takes the alkalized soil in Xinjiang Qitai area as the research object, and through the analysis of correlation between the measured spectral reflectance curves of alkalized soil and eight ions, pH, alkalization index, the unitary or polynary spectral inversion models based on the reflectivity of ionic spectral waveband are established, and the accuracy is verified. The results shows that:Na⁺, CO₃^2-, HCO₃^- content and spectral reflectance are positively correlated, and the correlation coefficients of the highest point are respectively 0.710, 0.798 and 0.749, while the Ca²⁺, Mg²⁺ content and spectral reflectance are negatively correlated with the highest correlation coefficient less than -0.370, which reflects that the relation between the ion concentration of first three kinds and spectral reflectivity is closer. The correlation coefficient between SAR (Sodium Adsorption Ratio), ESP (Exchange Sodium Percentage) and Na⁺ is 0.954, the correlation coefficients between TA (Total Alkalinity), RSC (Residual Sodium Carbonate), pH and CO₃^2- are respectively 0.946, 0.949 and 0.953. Therefore, Na⁺ and CO₃^2- have greater effect on alkalization index on the whole. The correlation between alkalization indexes and soil spectral reflectance is TA > RSC > ESP > pH > SAR, and the correlation coefficient between TA and spectral reflectance reaches 0.863. The inversion model of spectral reflectance of ion spectrum characteristic band of alkalization index is the best, and its R² is 0.703, which is 14 percent higher than the R² of the pH inversion model based on measured spectral reflectance, this shows that the former has higher accuracy and can better reflect the degree of soil alkalization in the research area. At the same time, the use of spectral reflectance of ion characteristic band to predict soil alkalization will become the focus of future research.