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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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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