热红外光谱的干旱区土壤含盐量遥感反演

doi:10.3964/j.issn.1000-0593(2019)04-1063-07

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摘要：干旱区土壤盐渍化已对生态环境构成严重威胁，通过遥感技术对土壤盐分含量进行定量反演具有重要意义。通过采集艾比湖流域的农田土壤和盐壳结晶，在室内配制成不同含盐量梯度（盐分占盐土比重：0.3%～30%）的土壤样品，利用102F FTIR光谱仪测量土壤样品的热红外光谱，并通过普朗克函数拟合得到土壤发射率数据。土壤发射率光谱曲线特征：不同含盐量土壤的发射率光谱曲线在形态和变化趋势上基本一致，发射率随含盐量增加而增大；盐分因子对Reststrahlen吸收特征有抑制作用，随着含盐量的增加，Reststrahlen吸收特征会减弱。通过发射率与含盐量相关性分析：土壤热红外发射率与盐分含量呈正相关关系，最大相关系数达到0.899，对应的波段为9.21 μm；8.2～10.5 μm是土壤盐分因子的最敏感波段。通过一元线性回归、多元逐步回归和偏最小二乘法建模分析比较，偏最小二乘法效果最佳，模型预测的R²达到0.958，RMSE为1.911%。选择ASTER，Landsat8和HJ-1B卫星传感器的热红外波段，进行发射率光谱模拟，通过相关性分析：ASTER的B10，B11和B12波段属于热红外光谱对盐分因子的敏感波段，与土壤含盐量相关性较高，相关系数分别为0.706，0.786和0.872。采用多元线性回归法建立基于ASTER热红外波段的土壤含盐量预测模型，模型预测的R²为0.833，RMSE为3.895%。结果表明，遥感传感器对土壤含盐量的预测能力，取决于传感器的光谱波段对盐分因子的敏感程度，通过卫星热红外遥感定量反演土壤含盐量是可行的，为干旱区土壤盐渍化遥感监测提供了新的途径和参考。

关键词：土壤；含盐量；热红外光谱；发射率；波段模拟

Abstract：The soil salinization has faced a serious threat to the ecological environment in arid areas, and it is of great significance to quantitative inversion of the salt content of soil by remote sensing technology. In this paper, we gathered the farmland soil and salt crystal in Ebinur lake watershed, to prepare into soil samples with different salt content (the proportion of salt and saline soil: 0.3%~30%) in the laboratory. We measured the thermal infrared emissivity spectral of soil samples using 102F FTIR spectrometer, and through the Planck function fitting to obtain soil emissivity data, and then used the Gaussian filter method for smoothing emissivity curve to eliminate the background and noise effects. The saline soil emissivity spectral curve features were as bellow. The emissivity spectrum curve of soil with different salt content was basically consistent in shape and change tendency, and with the increase of salt content, the value of emissivity increased. Soil salinity factors had inhibitory effect on Reststrahlen absorption characteristics, which would be weakened with the increase of salt content, that presented as the depth of the asymmetric absorption valleys decreased, but the position and width changed a little. Based on the correlation analysis of emissivity and salt content, we found that: It was positively correlated between thermal infrared emissivity and salt content of soil, with the maximum correlation coefficient being 0.899, and the corresponding waveband 9.21 μm; 8.2~10.5 μm was the most sensitive wave bands for soil salinity. Using monadic linear regression, multiple stepwise regression and partial least square method to construct the prediction model, the value of R² were respectively 0.863, 0.879 and 0.958, and RMSE were respectively 3.853%, 3.334% and 1.911%. It was proved that these three kinds of methods had certain prediction ability for salt content of soil, but partial least square was the best method. The thermal infrared wave bands of ASTER, Landsat8 and HJ-1B satellite sensors were chosen for the emissivity spectrum simulation according to the spectral response function of the sensor, and the correlation analysis results showed: ASTER’s B10, B11 and B12 bands are sensitive to the salt factor with thermal infrared spectroscopy and have a high correlation with soil salinity, and their correlation coefficient are up to 0.706, 0.786 and 0.872 respectively. Furthermore, the prediction model of soil salt content based on ASTER thermal infrared wavebands was established through the multiple linear regression method, R² and RMSE of the predicted model was 0.833 and 3.895%. At last, the results showed that: it is feasible to quantitatively inverse salt content of saline soil by satellite thermal infrared remote sensing, which will provide a new way and reference for the remote sensing monitoring of soil salinization in arid areas.

Key words：Soil; Salt content; Thermal infrared spectrum; Emissivity; Waveband simulation

收稿日期: 2018-03-05 修订日期: 2018-08-19

中图分类号:

TP79

基金资助: 西华师范大学博士科研启动基金项目(17E035)资助

作者简介: 夏军，1982年生，西华师范大学国土资源学院讲师 e-mail: xiajun5518@163.com

引用本文:

夏军，张飞. 热红外光谱的干旱区土壤含盐量遥感反演[J]. 光谱学与光谱分析, 2019, 39(04): 1063-1069.
XIA Jun, ZHANG Fei. A Study on Remote Sensing Inversion of Soil Salt Content in Arid Area Based on Thermal Infrared Spectrum. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1063-1069.

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