Establishment of the Model for Estimating the Organic Carbon Content of Forest Topsoil Based on Remote Sensing Data

Understanding the organic carbon content of forest soil will aid in studying the spatial distribution pattern of regional soil organic carbon (SOC) storage. Monitoring and researching forest SOC content is a crucial task that usually involves outdoor sampling and indoor experiments, which takes up m...

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Published in:	IEEE access Vol. 12; pp. 162062 - 162074
Main Authors:	Zheng, Shulin, Zhang, Jie, Song, Mingyue, Zhou, Pei, Guo, Xiaoyu, Yue, Haijun
Format:	Journal Article
Language:	English
Published:	IEEE 2024
Subjects:	Carbon Correlation Enhanced vegetation index Forestry linear regression algorithms Predictive models Random forests regression tree algorithms Regression tree analysis Remote sensing remote sensing data Soil measurement Topsoil organic carbon content Vegetation mapping
ISSN:	2169-3536, 2169-3536
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Abstract	Understanding the organic carbon content of forest soil will aid in studying the spatial distribution pattern of regional soil organic carbon (SOC) storage. Monitoring and researching forest SOC content is a crucial task that usually involves outdoor sampling and indoor experiments, which takes up much time. To improving its work efficiency, estimation models for topsoil organic carbon content are established. Correlation analysis was employed to evaluate the impact of factors (including elevation, slope, slope orientation, curvature, topographic wetness index, normalized difference vegetation index, enhanced vegetation index, and total nitrogen) on SOC content. Models for forest SOC content were constructed by machine learning algorithms using the above factors to enhance the efficiency of carbon storage estimation. Ultimately, the best model was used to generate a map of the SOC content. Research shows that: The Pearson correlation coefficient (r) between soil total nitrogen and SOC content is highest in both 0-5cm and 5-10cm soil layers (r=0.71, r=0.87). Optimal models for SOC content in the 0-5cm and 5-10cm soil layers are the random forest regression model and the boosted regression tree model, respectively. The coefficient of determination (R2) of the models are above 0.9. In the both soil layers, the performance of models constructed using regression tree algorithms is better than those constructed using linear regression, with the former having a greater R2 than the latter. Specifically, the R2 of the 0-5cm soil layer are 0.998 and 0.789, and the R2 of the 5-10cm soil layer are 0.997 and 0.996.
AbstractList	Understanding the organic carbon content of forest soil will aid in studying the spatial distribution pattern of regional soil organic carbon (SOC) storage. Monitoring and researching forest SOC content is a crucial task that usually involves outdoor sampling and indoor experiments, which takes up much time. To improving its work efficiency, estimation models for topsoil organic carbon content are established. Correlation analysis was employed to evaluate the impact of factors (including elevation, slope, slope orientation, curvature, topographic wetness index, normalized difference vegetation index, enhanced vegetation index, and total nitrogen) on SOC content. Models for forest SOC content were constructed by machine learning algorithms using the above factors to enhance the efficiency of carbon storage estimation. Ultimately, the best model was used to generate a map of the SOC content. Research shows that: The Pearson correlation coefficient (r) between soil total nitrogen and SOC content is highest in both 0-5cm and 5-10cm soil layers (r=0.71, r=0.87). Optimal models for SOC content in the 0-5cm and 5-10cm soil layers are the random forest regression model and the boosted regression tree model, respectively. The coefficient of determination (R2) of the models are above 0.9. In the both soil layers, the performance of models constructed using regression tree algorithms is better than those constructed using linear regression, with the former having a greater R2 than the latter. Specifically, the R2 of the 0-5cm soil layer are 0.998 and 0.789, and the R2 of the 5-10cm soil layer are 0.997 and 0.996.
Author	Yue, Haijun Zhou, Pei Zhang, Jie Song, Mingyue Guo, Xiaoyu Zheng, Shulin
Author_xml	– sequence: 1 givenname: Shulin surname: Zheng fullname: Zheng, Shulin organization: College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, China – sequence: 2 givenname: Jie orcidid: 0000-0002-6420-7356 surname: Zhang fullname: Zhang, Jie email: zhangjie111@imau.edu.cn organization: College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, China – sequence: 3 givenname: Mingyue surname: Song fullname: Song, Mingyue organization: College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, China – sequence: 4 givenname: Pei surname: Zhou fullname: Zhou, Pei organization: College of Forestry, Inner Mongolia Agricultural University, Hohhot, China – sequence: 5 givenname: Xiaoyu surname: Guo fullname: Guo, Xiaoyu organization: Engebei Ecological Demonstration Zone Management Committee of Ordos, Ordos, China – sequence: 6 givenname: Haijun surname: Yue fullname: Yue, Haijun organization: College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, China
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References	ref13 ref12 ref15 ref14 Du (ref2) 2022; 31 ref30 ref11 ref10 Shen (ref35) 2024; 60 ref1 ref17 ref16 ref19 Zang (ref26) 2020; 42 Muñoz (ref36) 2024; 299 Chen (ref18) 2020; 29 Li (ref31) 2023 Wang (ref32) 2024; 35 Zhao (ref34) 2024; 43 ref23 ref25 Du (ref27) 2022; 35 Yan (ref5) 2023; 20 ref20 Chen (ref6) 2010; 38 ref22 ref21 ref28 Zhao (ref24) 2019; 42 Zhang (ref33) 2014; 34 ref8 ref7 ref9 Zhao (ref29) 2024; 37 ref4 ref3 Yuan (ref37) 2024; 31
References_xml	– ident: ref23 doi: 10.5194/nhess-21-1247-2021 – volume: 20 start-page: 133 issue: 4 year: 2023 ident: ref5 article-title: Soil carbon sequestration capacity of different land use types in the context of sustainable development publication-title: J Yangtze Univ (Natural Sci. Ed.) – ident: ref25 doi: 10.1007/s11676-020-01155-1 – ident: ref20 doi: 10.1016/j.geodrs.2023.e00605 – volume: 31 issue: 5 year: 2024 ident: ref37 article-title: Comparative analysis on models for predicting the spatial distribution of soil organic carbon density with limited samples publication-title: Res. Soil Water Conserv. – ident: ref8 doi: 10.1016/j.geoderma.2024.117025 – volume: 43 start-page: 264 issue: 1 year: 2024 ident: ref34 article-title: Influencing factors of soil organic carbon in mixed spruce-fir-broadleaved forest based on structural equation publication-title: Chin. J. Ecol. – ident: ref1 doi: 10.4028/www.scientific.net/AMR.1010-1012.1194 – ident: ref30 doi: 10.1002/ldr.4895 – ident: ref22 doi: 10.3390/rs12030393 – ident: ref17 doi: 10.1111/j.1365-2656.2008.01390.x – start-page: 80 issue: 4 year: 2023 ident: ref31 article-title: Spatial distribution characteristics of soil organic carbon in Quercus variabilis plantation and its influencing factors publication-title: Forest Grassland Resour Res. – ident: ref15 doi: 10.3390/rs15174241 – volume: 37 start-page: 63 issue: 1 year: 2024 ident: ref29 article-title: Soil carbon changes and its influencing factors in major forest types in the subtropical area of Yunnan province publication-title: Forest Res. – volume: 299 year: 2024 ident: ref36 article-title: A random forest model to predict soil organic carbon storage in mangroves from southern Colombian Pacific coast publication-title: Estuarine, Coastal Shelf Sci. – volume: 35 start-page: 76 issue: 1 year: 2022 ident: ref27 article-title: Active components of forest soil organic carbon and its influencing factors in China publication-title: World Forestry Res. – ident: ref4 doi: 10.1007/s12517-023-11188-5 – ident: ref16 doi: 10.3390/su8111154 – ident: ref19 doi: 10.1016/j.geoderma.2020.114365 – volume: 35 start-page: 1055 issue: 4 year: 2024 ident: ref32 article-title: Estimation model for individual tree in natural Larix Gmelinii forest based on random forest publication-title: Chin. J. Appl. Ecol. – ident: ref12 doi: 10.3390/su14159390 – volume: 31 start-page: 663 issue: 4 year: 2022 ident: ref2 article-title: Distribution characteristics and influencing factors of soil organic carbon in spruce-fir broad-leaved mixed forest on north slope of Changbai mountains publication-title: Ecol. Environ Sci. – ident: ref10 doi: 10.1016/j.geoderma.2024.117036 – volume: 42 start-page: 113 issue: 1 year: 2019 ident: ref24 article-title: Research on topographic wetness index and its implications of surface water environment considering micro-reliefs on plains publication-title: J. Hefei Univ. Technol. (Natural Sci.) – ident: ref28 doi: 10.1016/j.geoderma.2021.115402 – ident: ref7 doi: 10.1016/j.isprsjprs.2024.09.038 – volume: 38 start-page: 59 issue: 4 year: 2010 ident: ref6 article-title: Ecological characteristics of density and reserves of organic carbon in typical forest soil in Guangzhou publication-title: J. Northeast Forestry Univ. – ident: ref9 doi: 10.1016/j.compag.2024.109494 – volume: 42 start-page: 553 issue: 3 year: 2020 ident: ref26 article-title: A study of a stand basal area growth model for Chinese fir plantations using boosted regression trees publication-title: Acta Agriculturae Universitatis Jiangxiensis – volume: 60 start-page: 65 issue: 3 year: 2024 ident: ref35 article-title: Characterization of soil organic carbon and key influencing factors of natural forests in central China based on machine learning algorithms publication-title: Scientia Silvae Sinicae – ident: ref14 doi: 10.9734/ijpss/2023/v35i183460 – ident: ref21 doi: 10.3390/f10111023 – ident: ref13 doi: 10.1016/j.jenvman.2021.112483 – ident: ref3 doi: 10.1002/ldr.2675 – volume: 29 start-page: 322 issue: 2 year: 2020 ident: ref18 article-title: Research on spatio-temporal characteristics and driving factors of urban expansion in Nanchang city based on BRT model publication-title: Resour Environ Yangtze Basin – volume: 34 start-page: 650 issue: 3 year: 2014 ident: ref33 article-title: The basic principle of random forest and its applications in ecology—A case study ofPinus yunnanensis publication-title: Acta Ecolog. Sinica – ident: ref11 doi: 10.3390/f14081518
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SubjectTerms	Carbon Correlation Enhanced vegetation index Forestry linear regression algorithms Predictive models Random forests regression tree algorithms Regression tree analysis Remote sensing remote sensing data Soil measurement Topsoil organic carbon content Vegetation mapping
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Title	Establishment of the Model for Estimating the Organic Carbon Content of Forest Topsoil Based on Remote Sensing Data
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