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+, CO32-, 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 CO32- are respectively 0.946, 0.949 and 0.953. Therefore, Na+ and CO32- 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.
Zhu Yuechen
,
Xiong Heigang
,
Zhu Zhongpeng
,
Zhang Fang
. Soil Alkalization Index Inversion Model Based on Spectral Reflectance of Ions Characteristics Band[J]. Journal of Desert Research, 2018
, 38(2)
: 345
-351
.
DOI: 10.7522/j.issn.1000-694X.2016.00157
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