• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

土壤有机碳量化技术:评估

Quantification techniques of soil organic carbon: an appraisal.

作者信息

Kanagaraj Avinash, Kaliappan Sathiya Bama, Shanmugam Thenmozhi, Alagirisamy Bharani, Ramalingam Kumaraperumal

机构信息

Department of Soil Science and Agricultural Chemistry, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India.

Department of Environmental Science, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India.

出版信息

Anal Sci. 2025 Jun;41(6):759-776. doi: 10.1007/s44211-025-00746-4. Epub 2025 Mar 11.

DOI:10.1007/s44211-025-00746-4
PMID:40067604
Abstract

This review provides an overview of the analytical methods utilized across laboratory, field, landscape, and regional scales for assessing soil organic carbon (SOC) in agricultural soils. The significance of soil depth in SOC estimation underscores the importance of selecting appropriate sampling designs, soil depths, analytical methods, and baseline selection methods for accurate soil carbon stock estimation. Traditional methods such as wet digestion and dry combustion (DC) remain prevalent in routine laboratory analysis, with DC considered the standard reference method, surpassing wet digestion in accuracy and reliability. Recent advancements in spectroscopic techniques enable SOC measurement both in laboratory settings and in situ, even at greater depths. Aerial spectroscopy, which employs multispectral and hyperspectral sensors, unmanned aerial vehicles (UAVs), or satellites, facilitates surface SOC measurement. While the current precision levels of these techniques may be limited, forthcoming hyperspectral sensors with enhanced signal‒to‒noise ratios are expected to significantly increase the prediction accuracy. Furthermore, at the global level, satellite remote sensing techniques have considerable potential for SOC estimation. Regardless of whether traditional or novel approaches are utilized, the selection of SOC determination depends on available resources and research requirements, each of which plays a distinct role in soil carbon and climate research. This paper provides an overview of various scale-dependent techniques for measuring SOC in agricultural soil, along with its potential limitations.

摘要

本综述概述了在实验室、田间、景观和区域尺度上用于评估农业土壤中土壤有机碳(SOC)的分析方法。土壤深度在SOC估算中的重要性突出了选择合适的采样设计、土壤深度、分析方法和基线选择方法对于准确估算土壤碳储量的重要性。传统方法如湿消化法和干燃烧法(DC)在常规实验室分析中仍然普遍使用,其中DC被视为标准参考方法,在准确性和可靠性方面超过湿消化法。光谱技术的最新进展使得能够在实验室环境和原位进行SOC测量,甚至在更深的深度也是如此。航空光谱学利用多光谱和高光谱传感器、无人机(UAV)或卫星,便于进行地表SOC测量。虽然这些技术目前的精度水平可能有限,但即将推出的具有更高信噪比的高光谱传感器有望显著提高预测准确性。此外,在全球范围内,卫星遥感技术在SOC估算方面具有相当大的潜力。无论采用传统方法还是新方法,SOC测定方法的选择取决于可用资源和研究要求,它们在土壤碳和气候研究中各自发挥着不同的作用。本文概述了用于测量农业土壤中SOC的各种尺度相关技术及其潜在局限性。

相似文献

1
Quantification techniques of soil organic carbon: an appraisal.土壤有机碳量化技术:评估
Anal Sci. 2025 Jun;41(6):759-776. doi: 10.1007/s44211-025-00746-4. Epub 2025 Mar 11.
2
Current and emerging methodologies for estimating carbon sequestration in agricultural soils: A review.当前和新兴的农业土壤碳固存估算方法:综述。
Sci Total Environ. 2019 May 15;665:890-912. doi: 10.1016/j.scitotenv.2019.02.125. Epub 2019 Feb 11.
3
Total nitrogen levels as a key constraint on soil organic carbon stocks across Australian agricultural soils.总氮水平是澳大利亚农业土壤中土壤有机碳储量的关键限制因素。
Environ Res. 2025 Aug 15;279(Pt 2):121825. doi: 10.1016/j.envres.2025.121825. Epub 2025 May 9.
4
A new baseline of organic carbon stock in European agricultural soils using a modelling approach.利用建模方法建立欧洲农业土壤有机碳存量的新基线。
Glob Chang Biol. 2014 Jan;20(1):313-26. doi: 10.1111/gcb.12292. Epub 2013 Aug 23.
5
Combining Laser-Induced Breakdown Spectroscopy and Visible Near-Infrared Spectroscopy for Predicting Soil Organic Carbon and Texture: A Danish National-Scale Study.结合激光诱导击穿光谱和可见近红外光谱预测土壤有机碳和质地:一项丹麦全国范围的研究
Sensors (Basel). 2024 Jul 10;24(14):4464. doi: 10.3390/s24144464.
6
The importance of accounting method and sampling depth to estimate changes in soil carbon stocks.会计方法和采样深度对估算土壤碳储量变化的重要性。
Carbon Balance Manag. 2024 Jan 26;19(1):2. doi: 10.1186/s13021-024-00249-1.
7
Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales-review and recommendations.不同尺度森林土壤碳储量评估中的误差和不确定性来源——综述与建议
Environ Monit Assess. 2016 Nov;188(11):630. doi: 10.1007/s10661-016-5608-5. Epub 2016 Oct 21.
8
Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization.通过数据协调提高美国本土湿地土壤有机碳储量。
Front Soil Sci. 2021 Oct 12;1:1-16. doi: 10.3389/fsoil.2021.706701.
9
Modelling soil organic carbon at multiple depths in woody encroached grasslands using integrated remotely sensed data.利用综合遥感数据对木质化草原多个深度的土壤有机碳进行建模。
Environ Monit Assess. 2025 Mar 1;197(3):343. doi: 10.1007/s10661-025-13671-w.
10
Developing novel spectral indices for precise estimation of soil pH and organic carbon with hyperspectral data and machine learning.利用高光谱数据和机器学习开发新的光谱指数,以精确估计土壤 pH 值和有机碳。
Environ Monit Assess. 2024 Nov 26;196(12):1255. doi: 10.1007/s10661-024-13406-3.

本文引用的文献

1
Estimation of Soil Organic Carbon Using Vis-NIR Spectral Data and Spectral Feature Bands Selection in Southern Xinjiang, China.利用可见-近红外光谱数据和光谱特征波段选择估算中国南疆地区土壤有机碳。
Sensors (Basel). 2022 Aug 16;22(16):6124. doi: 10.3390/s22166124.
2
A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion.基于主动和集成学习的新型智能方法,利用多光谱和 SAR 数据融合进行农业土壤有机碳预测。
Sci Total Environ. 2022 Jan 15;804:150187. doi: 10.1016/j.scitotenv.2021.150187. Epub 2021 Sep 8.
3
Twenty-five years of observations of soil organic carbon in Swiss croplands showing stability overall but with some divergent trends.
瑞士农田土壤有机碳观测 25 年:整体稳定,但部分趋势出现差异。
Environ Monit Assess. 2019 Apr 13;191(5):277. doi: 10.1007/s10661-019-7435-y.
4
Current and emerging methodologies for estimating carbon sequestration in agricultural soils: A review.当前和新兴的农业土壤碳固存估算方法:综述。
Sci Total Environ. 2019 May 15;665:890-912. doi: 10.1016/j.scitotenv.2019.02.125. Epub 2019 Feb 11.
5
Converting loss-on-ignition to organic carbon content in arable topsoil: pitfalls and proposed procedure.将可耕地表土中的烧失量转换为有机碳含量:陷阱与建议程序
Eur J Soil Sci. 2018 Jul;69(4):604-612. doi: 10.1111/ejss.12558. Epub 2018 May 3.
6
Measurements of Soil Carbon by Neutron-Gamma Analysis in Static and Scanning Modes.静态和扫描模式下通过中子伽马分析测量土壤碳含量
J Vis Exp. 2017 Aug 24(126):56270. doi: 10.3791/56270.
7
Applying Monte-Carlo simulations to optimize an inelastic neutron scattering system for soil carbon analysis.应用蒙特卡洛模拟优化用于土壤碳分析的非弹性中子散射系统。
Appl Radiat Isot. 2017 Oct;128:237-248. doi: 10.1016/j.apradiso.2017.07.003. Epub 2017 Jul 5.
8
Climate-smart soils.气候智能型土壤。
Nature. 2016 Apr 7;532(7597):49-57. doi: 10.1038/nature17174.
9
"Hot background" of the mobile inelastic neutron scattering system for soil carbon analysis.用于土壤碳分析的移动式非弹性中子散射系统的“热本底”
Appl Radiat Isot. 2016 Jan;107:299-311. doi: 10.1016/j.apradiso.2015.11.012. Epub 2015 Nov 6.
10
Thermal decomposition of dolomite under CO2: insights from TGA and in situ XRD analysis.白云石在 CO2 下的热分解:TGA 和原位 XRD 分析的见解。
Phys Chem Chem Phys. 2015 Nov 28;17(44):30162-76. doi: 10.1039/c5cp05596b. Epub 2015 Oct 27.