紫色土和黄壤含水率的室内光谱反演

韩陈; 唐强; 韦杰

doi:10.13961/j.cnki.stbctb.2021.05.024

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紫色土和黄壤含水率的室内光谱反演

试验研究

- 紫色土和黄壤含水率的室内光谱反演
- Estimating Soil Moisture Content of Purple Soil and Yellow Soil Using Laboratory Spectral Conversion Models
- 水土保持通报 2021年41卷第5期页码：174-180
- 作者机构：
  
  1. 重庆师范大学地理与旅游学院,重庆,401331
  2. 西南大学地理科学学院,重庆,400715
  3. 重庆金佛山喀斯特生态系统国家野外科学观测研究站,重庆,400715
  4. 三峡库区地表过程与环境遥感重庆市重点实验室,重庆,401331
- 作者简介：
- 基金信息：
  
  重庆市杰出青年基金项目“三峡库区坡耕地埂坎优势流及失稳判据研究”（cstc2019jcyjjqX0025）;重庆英才青年拔尖人才项目（CQYC201905009）;四川省科技计划资助（2020YFQ000,020YJ0202）;重庆市教委科技项目（KJZD-K201800502）
- DOI：10.13961/j.cnki.stbctb.2021.05.024
  中图分类号： S151.9
- 纸质出版：2021
- 稿件说明：
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韩陈, 唐强, 韦杰. 紫色土和黄壤含水率的室内光谱反演[J]. 水土保持通报, 2021,41(5):174-180.

Han Chen, Tang Qiang, WEI Jie. Estimating Soil Moisture Content of Purple Soil and Yellow Soil Using Laboratory Spectral Conversion Models[J]. Bulletin of Soiland Water Conservation, 2021, 41(5): 174-180.
韩陈, 唐强, 韦杰. 紫色土和黄壤含水率的室内光谱反演[J]. 水土保持通报, 2021,41(5):174-180. DOI： 10.13961/j.cnki.stbctb.2021.05.024.

Han Chen, Tang Qiang, WEI Jie. Estimating Soil Moisture Content of Purple Soil and Yellow Soil Using Laboratory Spectral Conversion Models[J]. Bulletin of Soiland Water Conservation, 2021, 41(5): 174-180. DOI： 10.13961/j.cnki.stbctb.2021.05.024.

摘要

[目的] 选择西南地区代表性土类紫色土和地带性黄壤，分析其光谱信息，构建土壤水分反演模型，估测土壤含水率，为西南地区土壤水分快速监测提供方法依据。[方法] 通过室内配置紫色土和黄壤不同土壤含水率水平，运用地物光谱仪测量其光谱反射率，比较不同含水率条件下两类土壤的高光谱特征；采用多种数学变换和相关分析法提取特征波段，运用多元逐步回归（SMLR）和BP神经网络（BPNN）分别构建土壤含水率的高光谱估测模型。[结果] ①随土壤含水率的增加，紫色土和黄壤的光谱反射率均逐渐降低；在相同含水率条件下，紫色土的光谱反射率低于黄壤。②土壤含水率对可见光波段（380～760 nm）反射率的影响显著低于红外波段（760～2 500 nm）；均在1 400，1 900，2 200 nm附近存在明显水分吸收谷。③经数学变换的紫色土和黄壤光谱反射率均与土壤含水率存在极强的相关性。④基于BPNN建立的土壤水分反演模型整体优于SMLR模型。[结论] BPNN模型为西南地区紫色土和黄壤土壤含水率光谱反演的最优模型，能够快速准确估测紫色土和黄壤土壤水分状况。

Abstract

[Objective] Representative purple soil and zonal yellow soil in Southwest China were selected to analyze their spectral information and to estimate soil moisture content in order to provide a method basis for rapid soil moisture monitoring in Southwest China.[Methods] Different soil moisture content levels were established in two soil types in the laboratory

and spectral reflectance was measured by using a ground surface spectrometer. The hyperspectral characteristics were compared and analyzed

and the characteristic bands were extracted by various mathematical transformations and correlation analysis. Hyperspectral estimation models of soil moisture were then constructed by stepwise multiple linear regression (SMLR) and BP neural network (BPNN).[Results] ① The spectral reflectance of both purple soil and yellow soil decreased as soil moisture content increased

and the spectral reflectance of purple soil was lower than that of yellow soil under the same soil moisture content. ② The effect of soil moisture content on the reflectance of infrared wavelengths (760-2 500 nm) was stronger than the reflectance of visible wavelengths (380-760 nm)

and there were obvious water absorption valleys near 1 400

1 900 and 2 200 nm. ③ There was a strong correlation between spectral reflectance and soil moisture content of purple soil and yellow soil after mathematical transformation. ④ The soil moisture prediction model based on BPNN was superior to SMLR.[Conclusion] The BPNN model was the best model for estimating soil moisture content of purple soil and yellow soil in Southwest China. The BPNN model can quickly and accurately obtain soil water status of purple soil and yellow soil.

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references

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