Estimating Soil Moisture Content of Purple Soil and Yellow Soil Using Laboratory Spectral Conversion Models

Han Chen; Tang Qiang; WEI Jie

doi:10.13961/j.cnki.stbctb.2021.05.024

您当前的位置：

首页 >

文章列表页 >

Estimating Soil Moisture Content of Purple Soil and Yellow Soil Using Laboratory Spectral Conversion Models

- Estimating Soil Moisture Content of Purple Soil and Yellow Soil Using Laboratory Spectral Conversion Models
- Bulletin of Soiland Water Conservation Vol. 41, Issue 5, Pages: 174-180(2021)
- 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：
- DOI：10.13961/j.cnki.stbctb.2021.05.024
  CLC： S151.9
- Published：2021
- 稿件说明：
移动端阅览
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：

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.

关键词

Keywords

references

Western A W, Grayson R B, Blöschl G, et al. Observed spatial organization of soil moisture and its relation to terrain indices[J]. Water Resources Research, 1999, 35(3):797-810.

Zhang Yongwang, Deng Lei, Yan Weiming, et al. Interaction of soil water storage dynamics and long-term natural vegetation succession on the Loess Plateau, China[J]. CATENA, 2016, 137:52-60.

李海萍, 庄大方, 熊利亚.北京周边沙源区沙化土地光谱特征初探[J].地理研究, 2002, 21(5):599-607.

李民赞, 郑立华, 安晓飞, 等.土壤成分与特性参数光谱快速检测方法及传感技术[J].农业机械学报, 2013, 44(3):73-87.

吴龙国, 王松磊, 何建国.基于高光谱技术的土壤水分无损检测[J].光谱学与光谱分析, 2018, 38(8):2563-2570.

姚艳敏, 魏娜, 唐鹏钦, 等.黑土土壤水分高光谱特征及反演模型[J].农业工程学报, 2011, 27(8):95-100.

Muller E, Décamps H. Modeling soil moisture-reflectance[J]. Remote Sensing of Environment, 2001, 76(2):173-180.

Lobell D B, Asner G P. Moisture effects on soil reflectance[J]. Soil Science Society of America Journal, 2002, 66(3):722-727.

于雷, 朱亚星, 洪永胜, 等.高光谱技术结合CARS算法预测土壤水分含量[J].农业工程学报, 2016, 32(22):138-145.

Gou Yu, Wei Jie, Li Jinlin, et al. Estimating purple-soil moisture content using Vis-NIR spectroscopy[J]. Journal of Mountain Science, 2020, 17(9):2214-2223.

彭杰, 向红英, 王家强, 等.基于野外实测高光谱数据的干旱区耕作土壤含水量反演研究[J].干旱地区农业研究, 2013, 31(2):241-246.

何挺, 王静, 程烨, 等.土壤水分光谱特征研究[J].土壤学报, 2006, 43(6):1027-1032.

张俊华, 贾科利.典型龟裂碱土土壤水分光谱特征及预测[J].应用生态学报, 2015, 26(3):884-890.

李治玲.生物炭对紫色土和黄壤养分、微生物及酶活性的影响[D].重庆:西南大学, 2016.

李仲明.中国紫色土(上册)[M].北京:科学出版社, 1991:40-79.

何毓蓉.中国紫色土(下册)[M].北京:科学出版社, 2003:8-18.

陈俊佳, 陈志彪, 陈志强, 等.不同水土保持措施对闽西紫色土速效养分及可蚀性的影响[J].水土保持学报, 2019, 33(1):45-50.

沈润平, 丁国香, 魏国栓, 等.基于人工神经网络的土壤有机质含量高光谱反演[J].土壤学报, 2009, 46(3):391-397.

刘焕军, 王翔, 张小康, 等.松嫩平原主要土壤类型含水量高光谱预测模型[J].土壤通报, 2018, 49(1):38-44.

赵明松, 谢毅, 陆龙妹, 等.基于高光谱特征指数的土壤有机质含量建模[J].土壤学报, 2021, 58(1):42-54.

Peng Jian, Shen Hong, He Sanwei, et al. Soil moisture retrieving using hyperspectral data with the application of wavelet analysis[J]. Environmental Earth Sciences, 2013, 69(1):279-288.

Stenberg B, Viscarra Rossel R A, Mouazen A M, et al. Visible and near infrared spectroscopy in soil science[M]//Advances in Agronomy. Amsterdam:Elsevier, 2010:163-215.

Steinberg A, Chabrillat S, Stevens A, et al. Prediction of common surface soil properties based on vis-NIR airborne and simulated EnMAP imaging spectroscopy data:Prediction accuracy and influence of spatial resolution[J]. Remote Sensing, 2016, 8(7):613.

张娜, 张栋良, 李立新, 等.基于高光谱的区域土壤质地预测模型建立与评价:以河套灌区解放闸灌域为例[J].干旱区资源与环境, 2014, 28(5):67-72.

Views

773

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Continuous Water Loss Comparisons Between Purple Soil and Yellow Soil at Various Temperatures

Impacts of rainfall and vegetation on soil erosion in Dabie Mountains forest reform area

Estimation of soil moisture and organic matter content in saline-alkaline farmland of Hetao Plain based on interpretable machine learning

Purple soil erosion resistance assessment using multi-model under different land-using types in middle reaches of Jialing River

Intelligent Evaluation of Loess Collapsibility

Related Author

Li Shasha

Wei Jie

Xu Wenxiu

Gou Yu

Luo Huajin

Li Gao

Luo Minxuan

Huo Zhitao

Related Institution

School of Geography and Tourism, Chongqing Normal University

Engineering Research Center for Pre-fabricated Land Consolidation, Chongqing Normal University

Changsha General Survey of Natural Resources Center， China Geological Survey， Ningxiang

Huangshan Observation and Research Station for Land-water Resources

College of Ecology and Environmental Science， Ningxia University

⁰