Automatic Estimation of Soil Biochar Quantity via Hyperspectral Imaging

IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Lei Tong, Jun Zhou, S. Bai, Chengyuan Xu, Y. Qian, Yongsheng Gao, Zhihong Xu
{"title":"Automatic Estimation of Soil Biochar Quantity via Hyperspectral Imaging","authors":"Lei Tong, Jun Zhou, S. Bai, Chengyuan Xu, Y. Qian, Yongsheng Gao, Zhihong Xu","doi":"10.4018/978-1-4666-9435-4.CH011","DOIUrl":null,"url":null,"abstract":"Biochar soil amendment is globally recognized as an emerging approach to mitigate CO2 emissions and increase crop yield. Because the durability and changes of biochar may affect its long term functions, it is important to quantify biochar in soil after application. In this chapter, an automatic soil biochar estimation method is proposed by analysis of hyperspectral images captured by cameras that cover both visible and infrared light wavelengths. The soil image is considered as a mixture of soil and biochar signals, and then hyperspectral unmixing methods are applied to estimate the biochar proportion at each pixel. The final percentage of biochar can be calculated by taking the mean of the proportion of hyperspectral pixels. Three different models of unmixing are described in this chapter. Their experimental results are evaluated by polynomial regression and root mean square errors against the ground truth data collected in the environmental labs. The results show that hyperspectral unmixing is a promising method to measure the percentage of biochar in the soil.","PeriodicalId":54004,"journal":{"name":"International Journal of Agricultural and Environmental Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Agricultural and Environmental Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-4666-9435-4.CH011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 1

Abstract

Biochar soil amendment is globally recognized as an emerging approach to mitigate CO2 emissions and increase crop yield. Because the durability and changes of biochar may affect its long term functions, it is important to quantify biochar in soil after application. In this chapter, an automatic soil biochar estimation method is proposed by analysis of hyperspectral images captured by cameras that cover both visible and infrared light wavelengths. The soil image is considered as a mixture of soil and biochar signals, and then hyperspectral unmixing methods are applied to estimate the biochar proportion at each pixel. The final percentage of biochar can be calculated by taking the mean of the proportion of hyperspectral pixels. Three different models of unmixing are described in this chapter. Their experimental results are evaluated by polynomial regression and root mean square errors against the ground truth data collected in the environmental labs. The results show that hyperspectral unmixing is a promising method to measure the percentage of biochar in the soil.
利用高光谱成像技术自动估算土壤生物炭的数量
生物炭土壤改良剂是全球公认的一种减少二氧化碳排放和提高作物产量的新兴方法。由于生物炭的耐久性和变化会影响其长期功能,因此对施用后土壤中生物炭的定量研究非常重要。在本章中,提出了一种土壤生物炭自动估算方法,该方法通过对相机拍摄的可见光和红外光波长的高光谱图像进行分析。将土壤图像视为土壤和生物炭信号的混合,然后采用高光谱解调方法估计每个像元上的生物炭比例。最终的生物炭百分比可以通过取高光谱像元比例的平均值来计算。本章描述了三种不同的分解模型。他们的实验结果通过多项式回归和均方根误差对环境实验室收集的真实数据进行评估。结果表明,高光谱解调是一种很有前途的测量土壤中生物炭百分比的方法。
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
International Journal of Agricultural and Environmental Information Systems
International Journal of Agricultural and Environmental Information Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.70
自引率
0.00%
发文量
10
×
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