Quantitative Spectral Estimation of Soil Salinity Based on Optimum Spectral Indices

GUO Peng; LI Hua; CHEN Hongyan; LIU Yaqiu; GAI Yuefeng; REN Tao

doi:10.13961/j.cnki.stbctb.2018.03.031

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Quantitative Spectral Estimation of Soil Salinity Based on Optimum Spectral Indices

- Quantitative Spectral Estimation of Soil Salinity Based on Optimum Spectral Indices
- Bulletin of Soiland Water Conservation Vol. 38, Issue 3, Pages: 193-199(2018)
- 作者机构：
  
  1. 山东农业大学资源与环境学院,山东,泰安,271018
  2. 山东菏泽水利工程总公司,山东,菏泽,274000
  3. 山东凯文科技职业学院,山东,济南,250200
  4. 山东颐通土地房地产评估测绘有限公司,山东,济南,250000
  5. 山东省泰安市农业局,山东,泰安,271018
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2018.03.031
  CLC：
- Published：2018
- 稿件说明：
移动端阅览
GUO Peng, LI Hua, CHEN Hongyan, et al. Quantitative Spectral Estimation of Soil Salinity Based on Optimum Spectral Indices[J]. Bulletin of Soiland Water Conservation, 2018, 38(3): 193-199.
DOI：

GUO Peng, LI Hua, CHEN Hongyan, et al. Quantitative Spectral Estimation of Soil Salinity Based on Optimum Spectral Indices[J]. Bulletin of Soiland Water Conservation, 2018, 38(3): 193-199. DOI： 10.13961/j.cnki.stbctb.2018.03.031.

摘要

[目的

]

探索基于光谱指数的盐渍土盐分估测的最佳技术路线，为研究区土壤盐分定量、快速遥感监测提供理论基础和技术参考。[方法

]

以山东省垦利县为研究区，野外采样，获取盐分及其主要离子（Cl

，Na

，Ca

）含量及高光谱数据；然后采用2种思路：①先选取敏感波段，进而构建常见的5种光谱指数；②先任意两波段组合构建光谱指数，进而筛选敏感光谱指数。最后皆采用随机森林方法（random forest，RF）构建土壤盐分及其主要离子的光谱模型。[结果

]

基于筛选的敏感亮度指数（1 750，1 620 nm）的RF模型精度最高，作为研究区土壤盐分的最佳估测模型，亮度指数作为最佳光谱指数；思路②明确的特征光谱范围涵盖思路①筛选的敏感波段，更有利于光谱特征分析；思路②建模的结果明显优于思路①；确定最佳技术路线为：任意波段两两组合构建光谱指数后，利用相关分析筛选土壤盐分及其主要离子的敏感光谱指数，进而构建其RF模型。[结论

]

该技术路线适用于黄河三角洲地区土壤盐渍化信息的有效提取。

Abstract

[Objective] To explore the best technical route for salt salinity estimation based on spectral indices in order to provide theoretical basis and technical reference for the quantitative calculation and rapid remote sensing monitoring of soil salinity in the study area.[Methods] Taking Kenli County of Shandong Province as the study area

samples were collected in the field

the content of soil salt and its main ions(Cl-

Na+

Ca2+)were measured

and the hyperspectra were obtained. Two different methods were used to select the sensitive spectral indices. The first one was to select the sensitive bands of salt and its major ions and then to build five spectral indices. The second one was to combine any two bands and to construct the five spectral indices

and the sensitive spectral indices were then filtered. The random forest(RF) method was used to build quantitative hyperspectral models of soil salinity and ions contents.[Results] The RF model of brightness spectral indices(1 750

1 620 nm)exhibited the best precision

thus it was the best estimation model of soil salinity in the study area

and the brightness spectral index was the best spectral index. The characteristic spectral range based on the second method covered the selected sensitive bands based on the first method

thus was more conducive to the spectral characteristics analysis. Meanwhile

the salt prediction model built based on the second method was better than that on the first one. Therefore

the best technical route was to construct the spectral indices by combination of any two bands firstly

then to select the sensitive spectral index of soil salinity and its main ions by correlation analysis

finally to build the RF model.[Conclusion] The technical route is suitable for the extraction of soil salinization information in the Yellow River delta.

关键词

Keywords

references

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