Li Xiao-Ming, Han Ji-Chang, Li Juan
Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Apr;34(4):1081-4.
Hysperspectral inversion of soil salinity was researched in the present paper with the chosen study object of typical semiarid area in North Shaanxi Province. The studying sites were selected, the hyperspectral data were collected, and the soil samples were taken back for experiment analysis. The reflectance of soils (R), the logarithm of the reciprocal of the reflectance (Log(1/R)) and the continual removed reflectance (R(cr)) were used to research the soil salinity. The correlations between the hyperspectral character and soil salinity was studied to filter the characteristics bands. Then the partial least squares regression (PLSR) was used to study the inversion model of soil salinity with Matlab program, and the precision was compared with the verifying sites. The research result showed that the root mean square error (RMSE) of the inversion with R(cr) was the least (1.253 < 1.367 < 1.575), and its precision was the best; the correlation between the predicted value and the measured value was well (r2 = 0.761), the trend line was near y = x. In conclusion, the quantificational inversion model with the variables of R(cr) establised by PLSR was well, which will improve the survey efficiency of soil salinity.
本文以陕北典型半干旱地区为研究对象,开展了土壤盐分的高光谱反演研究。选取了研究地点,采集了高光谱数据,并带回土壤样本进行实验分析。利用土壤反射率(R)、反射率倒数的对数(Log(1/R))和连续统去除反射率(R(cr))来研究土壤盐分。研究了高光谱特征与土壤盐分之间的相关性,以筛选特征波段。然后利用Matlab程序,采用偏最小二乘回归(PLSR)研究土壤盐分反演模型,并与验证点进行精度比较。研究结果表明,利用R(cr)进行反演的均方根误差(RMSE)最小(1.253 < 1.367 < 1.575),精度最佳;预测值与实测值之间相关性良好(r2 = 0.761),趋势线接近y = x。综上所述,利用PLSR建立的以R(cr)为变量的定量反演模型效果良好,将提高土壤盐分的测量效率。