Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction

IF 1.7 4区 化学 Q4 BIOCHEMICAL RESEARCH METHODS
Nan Zhou, Jin Hong, Bo Song, Shichao Wu, Yichen Wei, Tao Wang
{"title":"Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction","authors":"Nan Zhou, Jin Hong, Bo Song, Shichao Wu, Yichen Wei, Tao Wang","doi":"10.1155/2024/8180765","DOIUrl":null,"url":null,"abstract":"The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model’s prediction power and spectral characteristic information. (2) The feature band selection method’s prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1155/2024/8180765","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model’s prediction power and spectral characteristic information. (2) The feature band selection method’s prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content.
基于 VIS-NIR 光谱和土壤含水量预测模型构建的特征变量选择
土壤水分对水文循环、地表能量平衡和水资源管理都有重大影响。由于表层土壤水分控制着蒸散和降雨渗透的物理过程,因此它是一个重要的气候变量。在这项工作中,使用可见光-近红外(VIS-NIR)波段来比较和分析不同水分浓度的黄土样本的光谱。调查研究了土壤水分含量的变化如何影响土壤光谱的响应。研究人员使用遗传算法 (GA)、区间组合优化 (ICO) 和竞争性自适应加权采样 (CARS) 从全波段光谱数据中过滤特征变量。为了预测土壤表面黄土的含水量,研究人员创建了偏最小二乘回归(PLSR)、支持向量机(SVM)和随机森林(RF)等模型。研究结果表明(1) 最可靠的频谱预处理技术是一阶导数(FD),它能显著增强模型的预测能力和频谱特征信息。(2)特征波段选择法对土壤含水量的预测效果通常优于全谱数据。(3)将遗传算法(GA)与 FD 预处理光谱相结合,建立了准确度最高的土壤含水率随机森林(RF)预测模型。这些结果可为如何利用 VIS-NIR 测量土壤含水量提供新的认识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Spectroscopy
Journal of Spectroscopy BIOCHEMICAL RESEARCH METHODS-SPECTROSCOPY
CiteScore
3.00
自引率
0.00%
发文量
37
审稿时长
15 weeks
期刊介绍: Journal of Spectroscopy (formerly titled Spectroscopy: An International Journal) is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of spectroscopy.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信